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Long, Hoang, Oh-Heum Kwon, Suk-Hwan Lee, and Ki-Ryong Kwon. "Gabor Feature Representation and Deep Convolution Neural Network for Marine Vessel Classification." Korea Society of Coastal Disaster Prevention 8, no. 3 (July 30, 2021): 121–26. http://dx.doi.org/10.20481/kscdp.2021.8.3.121.
Повний текст джерелаАнотація:
The Vessel Surveillance System (VSS), a crucial tool for fisheries monitoring, controlling, and surveillance, has been required to use for the reservation of the current depressed state of the world's fisheries by fisheries management agencies. An important issue in the vessel surveillance system is the classification of vessels. However, several factors, such as lighting, congestion, and sea state, will affect the vessel's appearance, making it more difficult to classify vessels. There are two main methods for conventional classifications of vessels: the traditional-based- characteristics method and the convolutional neural networks-used method. In this paper, we combine Gabor feature representation (GFR) and deep convolution neural network (DCNN) to classify vessels. Gabor filters in different directions and ratios are used to extract vessel characteristics to create a new image of vessels, which is DCNN's input. The visible and infrared spectrums (VAIS) dataset, the world's first publicly available dataset for paired infrared and visible vessel images, was used to validate the proposed method (GFR-DCNN). The numerical results showed that GFR-DCNN is more accurate than other methods.
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Noh, Cassey Y. "Vasa Vasorum in Deep Vein Thrombus Recanalization." Journal for Vascular Ultrasound 42, no. 1 (March 2018): 33–35. http://dx.doi.org/10.1177/1544316718763396.
Повний текст джерелаАнотація:
Vasa vasorum is a microvessel known as “the vessel of the vessels.” It is found in tunica media in the wall of blood vessels. It is normally not visualized due to its microscopic structure, but when enough extra pressure compresses the wall of a blood vessel, by the presence of an acute thrombus for an example, this microvessel can be visualized. This case study explains how to identify vasa vasorum by analyzing Doppler waveforms. Furthermore, this case study suggests that the appearance of vasa vasorum in the 3-month follow-up may indicate that the overall recanalization of a thrombus may be more complex than how it is suggested in a published case study by Brandao and his colleagues.
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Ma, Yuliang, Xue Li, Xiaopeng Duan, Yun Peng, and Yingchun Zhang. "Retinal Vessel Segmentation by Deep Residual Learning with Wide Activation." Computational Intelligence and Neuroscience 2020 (October 10, 2020): 1–11. http://dx.doi.org/10.1155/2020/8822407.
Повний текст джерелаАнотація:
Purpose. Retinal blood vessel image segmentation is an important step in ophthalmological analysis. However, it is difficult to segment small vessels accurately because of low contrast and complex feature information of blood vessels. The objective of this study is to develop an improved retinal blood vessel segmentation structure (WA-Net) to overcome these challenges. Methods. This paper mainly focuses on the width of deep learning. The channels of the ResNet block were broadened to propagate more low-level features, and the identity mapping pathway was slimmed to maintain parameter complexity. A residual atrous spatial pyramid module was used to capture the retinal vessels at various scales. We applied weight normalization to eliminate the impacts of the mini-batch and improve segmentation accuracy. The experiments were performed on the DRIVE and STARE datasets. To show the generalizability of WA-Net, we performed cross-training between datasets. Results. The global accuracy and specificity within datasets were 95.66% and 96.45% and 98.13% and 98.71%, respectively. The accuracy and area under the curve of the interdataset diverged only by 1%∼2% compared with the performance of the corresponding intradataset. Conclusion. All the results show that WA-Net extracts more detailed blood vessels and shows superior performance on retinal blood vessel segmentation tasks.
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Chen, Ping-Hui, and Pau-Chung Chen. "P.3.05 Maritime fatal accidents and vessel disasters in taiwanese fishing vessels, 2003–2015." Occupational and Environmental Medicine 76, Suppl 1 (April 2019): A98.1—A98. http://dx.doi.org/10.1136/oem-2019-epi.268.
Повний текст джерелаАнотація:
IntroductionFishery is a hazardous industry with high occupational fatalities, mainly due to vessel disasters, especially among smaller vessels, according to European and North-American studies. However, Asian countries with different industry status and larger portion of global marine capture production are short of adequate investigation.MethodsIn Taiwan, Fisheries Agency provided compensation for maritime fatalities and capsizing vessels, and recorded all enrolled crews and fishing vessels in Fishery Administration Management Information System. Using these two databases, incidence rate and odds ratio (OR) were calculated to depict an overall picture of maritime fatal accidents and associated causal factors.ResultsFrom 2003 to 2015, there were 562 cases of fatal accidents, whose mechanisms were man overboard (368, 65.5%), followed by capsizing (53, 9.4%). Overall incidence rate was 3.6 per 10 000 man-labour year. The rates were 2.51, 4.12, and 7.28 per 10 000 man-labour year, and odds ratios were 1.0, 1.64 and 2.90, for coastal (<12 Nautical miles, Nm), inshore (12–200 Nm), and deep sea (>200 Nm) fisheries.There were 632 cases of vessel capsizing, whose mechanisms were fire (162, 25.63%), followed by natural disaster, mechanical problem (85, 13.45%), and collision (71, 11.23%). Overall incidence rate was 152.01 per 10 000 vessels. The rates were 7.15, 21.42, 71.48, and 51.95 per 10 000 vessels, and odds ratios were 1.0, 3.00, 10.05 and 7.29, for small-sized (sampan and fishing raft), small-medium-sized (<20 gross registered tonnages, GRT), medium-large-sized (20–200 GRT) and large-sized (>200 GRT) vessels.ConclusionOur findings showed the mixed effect of vessel size and fishery types on maritime fatal accidents, and deep-sea medium-large-sized vessels, as the smallest vessels in deep sea fisheries, had the highest risk. Compared with other developed countries, more than half fishing vessels of deep sea fisheries in Taiwan are less than 100 GRT, and preventive intervention should be focused on these vessels.
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Matasci, G., J. Plante, K. Kasa, P. Mousavi, A. Stewart, A. Macdonald, A. Webster, and J. Busler. "DEEP LEARNING FOR VESSEL DETECTION AND IDENTIFICATION FROM SPACEBORNE OPTICAL IMAGERY." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2021 (June 17, 2021): 303–10. http://dx.doi.org/10.5194/isprs-annals-v-3-2021-303-2021.
Повний текст джерелаАнотація:
Abstract. We present a deep learning-based vessel detection and (re-)identification approach from spaceborne optical images. We introduce these two components as part of a maritime surveillance from space pipeline and present experimental results on challenging real-world maritime datasets derived from WorldView imagery. First, we developed a vessel detection model based on RetinaNet achieving a performance of 0.795 F1-score on a challenging multi-scale dataset. We then collected a large-scale dataset for vessel identification by applying the detection model on 200+ optical images, detecting the vessels therein and assigning them an identity via an Automatic Identification System association framework. A vessel re-identification model based on Twin neural networks has then been trained on this dataset featuring 2500+ unique vessels with multiple repeated occurrences across different acquisitions. The model allows to naturally establish similarities between vessel images. It returns a relevant ranking of candidate vessels from a database when provided an input image for a specific vessel the user might be interested in, with top-1 and top-10 accuracies of 38.7% and 76.5%, respectively. This study demonstrates the potential offered by the latest advances in deep learning and computer vision when applied to optical remote sensing imagery in a maritime context, opening new opportunities for automated vessel monitoring and tracking capabilities from space.
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Ramakonar, Hari H. "220 A Stereotactic Brain Biopsy Needle Integrating an Optical Coherence Tomography (OCT) Probe with Blood Vessel Detection in Human Patients." Neurosurgery 64, CN_suppl_1 (August 24, 2017): 260. http://dx.doi.org/10.1093/neuros/nyx417.220.
Повний текст джерелаАнотація:
Abstract INTRODUCTION Stereotactic brain biopsies are a common neurosurgical procedure used predominantly to obtain histological diagnosis of brain pathologies. Intracranial haemorrhage is the most frequent complication related to this procedure and is associated with increased morbidity and mortality. We present a pilot study investigating a customised miniature Optical Coherence Tomography (OCT) probe integrated into a commercial stereotactic brain biopsy needle. OCT is a high-resolution optical imaging modality that uses reflections of low-power, near-infrared light to characterise tissue. The probe is combined with fully automated blood vessel detection software based on speckle decorrelation to provide real-time feedback as the needle tip encounters a blood vessel. METHODS We demonstrate the use of such a needle intraoperatively for the first time in humans. A total of 167 superficial blood vessel and control measurements were obtained in 11 patients undergoing craniotomies for various pathologies. Deep blood vessel measurements were also acquired in 3 patients. Superficial blood vessel measurements were obtained by directly placing the probe over cortical vessels exposed during craniotomy and validated against intraoperative photographs. Deep vessels were targeted using preoperative MRI and frameless stereotactic surgical navigation. RESULTS >For the superficial vessel measurements, the probe demonstrated a sensitivity of >88% and specificity >98% for the detection of blood vessels >500microns in diameter. For the deep vessel measurements, the probe was able to detect a blood vessel appropriately on all three occasions. CONCLUSION This pioneering study demonstrates OCT detection of blood vessels in human patients in real-time, integrated with current Neurosurgical practices. This work opens the possibilities of further studies using OCT to detect blood vessels in probe based Neurosurgery to minimise the risk of haemorrhage from such procedures.
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Chatterjee, Soumick, Kartik Prabhu, Mahantesh Pattadkal, Gerda Bortsova, Chompunuch Sarasaen, Florian Dubost, Hendrik Mattern, Marleen de Bruijne, Oliver Speck, and Andreas Nürnberger. "DS6: Deformation-Aware Semi-Supervised Learning: Application to Small Vessel Segmentation with Noisy Training Data." Journal of Imaging 8, no. 10 (September 22, 2022): 259. http://dx.doi.org/10.3390/jimaging8100259.
Повний текст джерелаАнотація:
Blood vessels of the brain provide the human brain with the required nutrients and oxygen. As a vulnerable part of the cerebral blood supply, pathology of small vessels can cause serious problems such as Cerebral Small Vessel Diseases (CSVD). It has also been shown that CSVD is related to neurodegeneration, such as Alzheimer’s disease. With the advancement of 7 Tesla MRI systems, higher spatial image resolution can be achieved, enabling the depiction of very small vessels in the brain. Non-Deep Learning-based approaches for vessel segmentation, e.g., Frangi’s vessel enhancement with subsequent thresholding, are capable of segmenting medium to large vessels but often fail to segment small vessels. The sensitivity of these methods to small vessels can be increased by extensive parameter tuning or by manual corrections, albeit making them time-consuming, laborious, and not feasible for larger datasets. This paper proposes a deep learning architecture to automatically segment small vessels in 7 Tesla 3D Time-of-Flight (ToF) Magnetic Resonance Angiography (MRA) data. The algorithm was trained and evaluated on a small imperfect semi-automatically segmented dataset of only 11 subjects; using six for training, two for validation, and three for testing. The deep learning model based on U-Net Multi-Scale Supervision was trained using the training subset and was made equivariant to elastic deformations in a self-supervised manner using deformation-aware learning to improve the generalisation performance. The proposed technique was evaluated quantitatively and qualitatively against the test set and achieved a Dice score of 80.44 ± 0.83. Furthermore, the result of the proposed method was compared against a selected manually segmented region (62.07 resultant Dice) and has shown a considerable improvement (18.98%) with deformation-aware learning.
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Ivanchenko, A., and I. Bezkorovayna. "CHANGES IN RETINAL MICROCIRCULATION ACCORDING TO FINDINGS OF OPTICAL COHERENCE TOMOGRAPHY-ANGIOGRAPHY IN PATIENTS AFTER RHEGMATOGENOUS RETINAL DETACHMENT." Актуальні проблеми сучасної медицини: Вісник Української медичної стоматологічної академії 22, no. 3-4 (November 29, 2022): 58–61. http://dx.doi.org/10.31718/2077-1096.22.3.4.58.
Повний текст джерелаАнотація:
The purpose of this study is to conduct a comparative analysis of retinal microcirculation according to OCT -а in patients operated on for rhegmatogenous retinal detachment without macular detachment and with macular detachment. Subjects and Methods: having been checked for inclusion / exclusion criteria eligibility, 116 patients were included in this prospective study: 65 patients after the rhegmatogenous retinal detachment without macular detachment and 51 patients after the rhegmatogenous retinal detachment with macular detachment. Using OCT-а, we analyzed the size of the foveal avascular zone, the density of superficial vessels and the density of deep vessels, the density of choriocapillary vessels in eyes with RRD without detached macula and with detached macula, and then evaluated their correlation with functional and anatomical results through 1.3, 6 and 12 months following the surgical treatment.
Results. In the group without macular detachment, the best-corrected visual acuity improved (on average from 1.02 ± 0.76 to 1.45 ± 0.57) logMAR compared to the initial level, the patients had a lower density of deep vessels in the parafoveal region 58.51-61.35. In the group with macular detachment, the best corrected visual acuity was on average from 0.26 ± 0.28 to 0.33 ± 0.30 logMAR, the patients had a lower average density of superficial vessels in the parafoveal zone 48.81-53.42, a lower average density of deep vessels in the foveal 56.89-60.12 and parafoveal zones 59.98-62.30.
Conclusion. In rhegmatogenous retinal detachment without macular detachment, the final best-corrected visual acuity is related to the parafoveal density of deep vessels (r = − 0.340, p = 0.010). In rhegmatogenous retinal detachment with macular detachment, the best-corrected visual acuity is associated with the foveal density of superficial vessels (r = − 0.451, p = 0.005) and the parafoveal density of deep vessels (r = − 0.418, p = 0.010). When comparing retinal vessels after successful repair of rhegmatogenous retinal detachment without macular detachment and with macular detachment, both study groups had lower vessel density compared to paired eyes (r = − 0.402, p = 0.006).
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Xian, Zhanchao, Xiaoqing Wang, Shaodi Yan, Dahao Yang, Junyu Chen, and Changnong Peng. "Main Coronary Vessel Segmentation Using Deep Learning in Smart Medical." Mathematical Problems in Engineering 2020 (October 21, 2020): 1–9. http://dx.doi.org/10.1155/2020/8858344.
Повний текст джерелаАнотація:
The automatic segmentation of main vessels on X-ray angiography (XRA) images is of great importance in the smart coronary artery disease diagnosis system. However, existing methods have been developed to this task, but these methods have difficulty in recognizing the coronary artery structure in XRA images. Main vessel segmentation is still a challenging task due to the diversity and small-size region of the vessel in the XRA images. In this study, we propose a robust method for main vessel segmentation by using deep learning architectures with fully convolutional networks. Four deep learning models based on the UNet architecture are evaluated on a clinical dataset, which consists of 3200 X-ray angiography images collected from 1118 patients. Using the precision (Pre), recall (Re), and F1 score (F1) as evaluation metrics, the average Pre, Re, and F1 for main vessel segmentation in the entire experimental dataset is 0.901, 0.898, and 0.900, respectively. 89.8% of the images exhibited a high F1 score >0.8. For the main vessel segmentation in XRA images, our deep learning methods demonstrated that vessels could be segmented in real time with a more optimized implementation, to further facilitate the online diagnosis in smart medical.
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Shin, Seung Yeon, Soochahn Lee, Il Dong Yun, and Kyoung Mu Lee. "Topology-Aware Retinal Artery–Vein Classification via Deep Vascular Connectivity Prediction." Applied Sciences 11, no. 1 (December 31, 2020): 320. http://dx.doi.org/10.3390/app11010320.
Повний текст джерелаАнотація:
Retinal artery–vein (AV) classification is a prerequisite for quantitative analysis of retinal vessels, which provides a biomarker for neurologic, cardiac, and systemic diseases, as well as ocular diseases. Although convolutional neural networks have presented remarkable performance on AV classification, it often comes with a topological error, like an abrupt class flipping on the same vessel segment or a weakness for thin vessels due to their indistinct appearances. In this paper, we present a new method for AV classification where the underlying vessel topology is estimated to give consistent prediction along the actual vessel structure. We cast the vessel topology estimation as iterative vascular connectivity prediction, which is implemented as deep-learning-based pairwise classification. In consequence, a whole vessel graph is separated into sub-trees, and each of them is classified as an artery or vein in whole via a voting scheme. The effectiveness and efficiency of the proposed method is validated by conducting experiments on two retinal image datasets acquired using different imaging techniques called DRIVE and IOSTAR.
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Ogawa, Kazuaki, Yutaka Hanamure, Atsushi Sameshima, Kengo Nishimoto, and Kenji Sasaki. "Anomaly of Deep Inferior Epigastric Vessels." Otolaryngology–Head and Neck Surgery 121, no. 4 (October 1999): 499–501. http://dx.doi.org/10.1016/s0194-5998(99)70245-7.
Повний текст джерела
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Diaz, J. Daniel, Jay C. Wang, Patrick Oellers, Inês Lains, Lucia Sobrin, Deeba Husain, Joan W. Miller, Demetrios G. Vavvas, and John B. Miller. "Imaging the Deep Choroidal Vasculature Using Spectral Domain and Swept Source Optical Coherence Tomography Angiography." Journal of VitreoRetinal Diseases 2, no. 3 (April 16, 2018): 146–54. http://dx.doi.org/10.1177/2474126418771805.
Повний текст джерелаАнотація:
Purpose: To evaluate the deeper choroidal vasculature in eyes with various ocular disorders using spectral domain (SD) optical coherence tomography angiography (OCTA) and swept source (SS) OCTA. Methods: Patients underwent OCTA imaging with either SD-OCTA (Zeiss Cirrus Angioplex or Optovue AngioVue) or SS-OCTA (Topcon Triton). Retinal pigment epithelium (RPE) integrity, structural visualization of deep choroidal vessels on en face imaging, and OCTA of deep choroidal blood flow signal were analyzed. Choroidal blood flow was deemed present if deeper choroidal vessels appeared bright after appropriate segmentation. Results: Structural visualization of choroidal vessels was feasible in all eyes by en face imaging. In both SD-OCTA and SS-OCTA, choroidal blood flow signal was present in all eyes with overlying RPE atrophy (100% of eyes with RPE atrophy, 28.6% of all imaged eyes, P < .001). Conclusions: While choroidal vessels can be visualized anatomically in all eyes by en face imaging, choroidal blood flow detection in deep choroidal vessel is largely restricted to areas with overlying RPE atrophy. Intact RPE acts as a barrier for reliable detection of choroidal flow using current OCTA technology, inhibiting evaluation of flow in deeper choroidal vessels in most eyes.
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Thind, Navreet S., Justus Hering, and Dirk Söffker. "Fast and Precise Generic Model for Position-Based Trajectory Prediction of Inland Waterway Vessels." Automation 3, no. 4 (November 30, 2022): 633–45. http://dx.doi.org/10.3390/automation3040032.
Повний текст джерелаАнотація:
Vessel motion simulation as well as model-based accurate trajectory prediction of vessels require accurate models with respect to related dynamic properties. The ability to predict vessel’s trajectory behaviors will become relevant in the case of future autonomous navigation of vessels to predict the behavior of others. The definition of models or parameters can be realized via first principles or by using experimental modeling methods leading to a time invariant or variant model. Existing hydrodynamical modeling approaches are based on mathematical approaches, which use parameters like mass, hydrodynamic forces, wind velocity, depth under the keel, loading parameters, etc. So, determining a dynamic vessel’s model is a complex task, since the model is vessel-specific. For collision avoidance of autonomous or assisted vessels, the trajectory prediction of encountering other vessels is especially required. It is not possible to use complex hydrodynamical models of encountering vessels online due to missing required information/measurements. Even existing deep learning approaches provide better predictions, but are still insufficient for collision avoidance in the case of strong dynamical changes, since the considered input sequences are long. Due to long input sequences, the model does not adapt to strong dynamical changes. In this work, a simple parameter-based approach is developed to predict the intended behavior using the last seconds of the measured position variables. The idea is to globally identify the model parameters of the vessel, which remains constant for the situation, and additionally two parameters for local adaptation, which adapt at every updated input sequence. Typically parameters like rudder angle, wind velocities, and water current affect the behavior of vessels. The introduced approach works with a sliding window approach for which, after identification of the global system, local values are identified based on the last 80 measurements of the vessels. A trajectory prediction (assuming no additional rudder-based maneuvering) is realized for the prediction horizon of 180 s. To confirm the robustness of the new approach, real AIS/GPS-based measurements from a German research inland vessel for different scenarios and sailing conditions including ‘loaded’ and ‘empty’ sailing cases are used. Furthermore, additional results are shown for position data information of different sample rates.
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De Robertis, Alex, Christopher D. Wilson, Neal J. Williamson, Michael A. Guttormsen, and Sarah Stienessen. "Silent ships sometimes do encounter more fish. 1. Vessel comparisons during winter pollock surveys." ICES Journal of Marine Science 67, no. 5 (February 1, 2010): 985–95. http://dx.doi.org/10.1093/icesjms/fsp299.
Повний текст джерелаАнотація:
Abstract De Robertis, A., Wilson, C. D., Williamson, N. J., Guttormsen, M. A., and Stienessen, S. 2010. Silent ships sometimes do encounter more fish. 1. Vessel comparisons during winter pollock surveys. – ICES Journal of Marine Science, 67: 985–995. Avoidance of approaching vessels by fish is a major source of uncertainty in surveys of fish stocks. In an effort to minimize vessel avoidance, international standards for underwater-noise emission by research vessels have been established. Despite widespread investment in noise-reduced vessels, the effectiveness of noise reduction on vessel avoidance remains poorly understood. Here, we report on vessel comparisons of pollock abundance recorded by the NOAA ships “Oscar Dyson” (OD), a noise-reduced vessel, and “Miller Freeman” (MF), a conventionally designed vessel. The comparisons were made during three acoustic surveys of prespawning aggregations of walleye pollock (Theragra chalcogramma) in Alaska. The experiments demonstrate that a noise-reduced vessel will detect significantly more fish backscatter than a conventional vessel in some situations. OD detected 31% more pollock backscatter than MF in the Shumagin Islands, where pollock were distributed between 100 and 200 m deep, and 13% more pollock backscatter in Shelikof Strait, where pollock were primarily distributed 200–300 m deep. However, there was no difference in the Bogoslof Island area where pollock were found at 400–700 m. In the Shumagin and Shelikof areas, the discrepancy between vessels tended to decrease with fish depth, consistent with a decreasing response to a stimulus propagating from the surface. Analysis of the depth distributions of pollock supports the conclusion that the discrepancies in backscatter stem from differential behavioural responses to the two vessels.
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Єфремова, Н. В., A. Є. Нильва, Н. Н. Котовська, and М. В. Дрига. "Theoretical and experimental investigations of wave field around vessel in shallow water." Herald of the Odessa National Maritime University, no. 62 (August 11, 2020): 72–89. http://dx.doi.org/10.47049/2226-1893-2020-2-72-89.
Повний текст джерелаАнотація:
Un-running vessel at the shallow-water road anchorage is under exposure to waves that come at arbitrary angle from the high sea. 3D waves from deep-sea area become practically 2D when entering shallow water. While mean periods are kept, waves become shorter and their crests become higher and sharpener than for deep-water ones. As a result of diffraction of waves that come from the deep-water sea at the vessel, a transformation zone appears where waves become 3D again. Dimensions of the waves’ transformation zone, character and height of waves in this zone specify safety of auxiliary crafts, e.g. tugboats, bunker vessels, pilot and road crafts, oil garbage collectors and boom crafts. In the complex 3D waves the trajectory of auxiliary vessel’s movement has to be safe, vessel’s motions have to be moderate. Besides waves’ height is one of the parameters that are used for forecast of movement of spilled oil. Last years the biggest part examination of waves’ problems was devoted to estimation of waves’ impact onto stationary or floating shelf facilities. For validity estimation, waves’ characteristics defined due to different theories, are compared with experimental ones. But characteristics of the waves around shelf facilities are hardly able to be compared to same ones of waves around bodies with vessel-type shape. At the experiments with vessels’ models, waves’ impact onto vessel was examined, but not the transformation of the waves themselves. So, comparing of waves area’s characteristics defined by both theoretical experimental ways is an actual problem. Aim of the paper is verification of results of wave area investigation; wave area is located around a vessel that is exposed of arbitrary angle waves at shallow water conditions. Description of experimental investigations of transformed waves in the towing tank is done; transformation zone appears around vessel’s model while running waves diffract on it. Distribution of waves’ amplitudes at the designated points was fixed by the special designed and manufactured unit. Experimental data is compared with computation results both of linear and non-linear theories. It was assumed that experimental results and theoretical data satisfactory meet each other; also that non-linear computations define the maximal values of waves’ amplitudes at all cases.
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Zhou, Xiangyu, Zhengjiang Liu, Fengwu Wang, Yajuan Xie, and Xuexi Zhang. "Using Deep Learning to Forecast Maritime Vessel Flows." Sensors 20, no. 6 (March 22, 2020): 1761. http://dx.doi.org/10.3390/s20061761.
Повний текст джерелаАнотація:
Forecasting vessel flows is important to the development of intelligent transportation systems in the maritime field, as real-time and accurate traffic information has favorable potential in helping a maritime authority to alleviate congestion, mitigate emission of GHG (greenhouse gases) and enhance public safety, as well as assisting individual vessel users to plan better routes and reduce additional costs due to delays. In this paper, we propose three deep learning-based solutions to forecast the inflow and outflow of vessels within a given region, including a convolutional neural network (CNN), a long short-term memory (LSTM) network, and the integration of a bidirectional LSTM network with a CNN (BDLSTM-CNN). To apply those solutions, we first divide the given maritime region into
M
×
N
grids, then we forecast the inflow and outflow for all the grids. Experimental results based on the real AIS (Automatic Identification System) data of marine vessels in Singapore demonstrate that the three deep learning-based solutions significantly outperform the conventional method in terms of mean absolute error and root mean square error, with the performance of the BDLSTM-CNN-based hybrid solution being the best.
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Barros, Guilherme, Michael J. Lang, Nikolaos Mouchtouris, Ashwini D. Sharan, and Chengyuan Wu. "Impact of Trajectory Planning With Susceptibility-Weighted Imaging for Intracranial Electrode Implantation." Operative Neurosurgery 15, no. 1 (October 18, 2017): 60–65. http://dx.doi.org/10.1093/ons/opx215.
Повний текст джерелаАнотація:
Abstract BACKGROUND While T1-weighted gadolinium-enhanced (T1-Gd) magnetic resonance imaging (MRI) is the standard imaging sequence for trajectory planning of stereotactic procedures, including deep brain stimulation, stereoelectroencephalography, and laser interstitial thermal therapy, susceptibility-weighted imaging (SWI) has been reported to demonstrate increased sensitivity for the visualization of microvasculature. OBJECTIVE To determine the impact of SWI visualization on trajectory planning for electrode implantation and evaluate the relationship between the rate of vessel-electrode intersections and intracerebral hemorrhage (ICH). METHODS We conducted a retrospective study of 13 patients who underwent stereoelectroencephalography and laser interstitial thermal therapy placement between 2014 and 2015, using their preoperative T1-Gd and SWI scans, and postoperative MRI scans to determine the rate of vessel-electrode intersections seen on the 2 imaging modalities, the mean diameter and depth of the vessels identified, and the rate of ICH after implantation. RESULTS Among 13 patients, 106 electrodes were implanted. Sixty-three unique vessel-electrode intersections were identified on SWI with a mean of 4.85 intersections per patient. There were 13 intersections seen on T1-Gd with a mean of 1 intersection per patient. The intersected vessels visualized on SWI had a diameter of 1.49 ± 0.46 mm and those on T1-Gd were 2.01 ± 0.52 mm. There was no clear ICH observed in this series. CONCLUSION SWI allows for improved visualization of the smaller, deep vessels, whereas T1-Gd adequately detects superficial, larger vessels. Despite the larger number of vessel-electrode intersections seen on SWI, no clear evidence of ICH was identified. Increased detection of deep vasculature does not appear to significantly benefit trajectory planning for stereotactic intracranial procedures and may limit the number of trajectories perceived to be safe.
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Jia, Hao, Bin Chen, and Dong Li. "Accurate Simulation of Light Propagation in Complex Skin Tissues Using an Improved Tetrahedron-Based Monte Carlo Method." Applied Sciences 11, no. 7 (March 26, 2021): 2998. http://dx.doi.org/10.3390/app11072998.
Повний текст джерелаАнотація:
Understanding light transportation in skin tissues can help improve clinical efficacy in the laser treatment of dermatosis, such as port-wine stains (PWS). Patient-specific cross-bridge PWS vessels are structurally complicated and considerably influence laser energy deposition due to shading effects. The shading effect of PWS vessels is investigated using a tetrahedron-based Monte Carlo (MC) method with extended boundary condition (TMCE). In TMCE, body-fitted tetrahedra are generated in different tissues, and the precision of photon–surface interaction can be considerably improved via mesh refinement. Such improvement is difficult to achieve with the widely used voxel-based MC method. To fit the real physical boundary, the extended boundary condition is adapted by extending photon propagation to the semi-infinite tissue layers while restricting the statistics of photon propagation in the computational domain. Results indicate that the shading parameters, such as the cross angle, vessel distance, and geometric shadow (GS), of cross-bridge blood vessel pairs determine the peak characteristics of photon deposition in deep vessels by affecting the relative deposition of collimated and diffused light. Collimated light is shaded, attenuated, and partially transformed into diffused light due to the increase in vessel distance and GS of vessel pairs, resulting in difficulty in treating deep and shallow vessels with one laser pulse. The TMCE method can be used for the individualized and precise forecasting of laser energy deposition based on the morphology and embedding characteristics of vascular lesions.
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Hu, Xiaolong, Liejun Wang, Shuli Cheng, and Yongming Li. "HDC-Net: A hierarchical dilation convolutional network for retinal vessel segmentation." PLOS ONE 16, no. 9 (September 7, 2021): e0257013. http://dx.doi.org/10.1371/journal.pone.0257013.
Повний текст джерелаАнотація:
The cardinal symptoms of some ophthalmic diseases observed through exceptional retinal blood vessels, such as retinal vein occlusion, diabetic retinopathy, etc. The advanced deep learning models used to obtain morphological and structural information of blood vessels automatically are conducive to the early treatment and initiative prevention of ophthalmic diseases. In our work, we propose a hierarchical dilation convolutional network (HDC-Net) to extract retinal vessels in a pixel-to-pixel manner. It utilizes the hierarchical dilation convolution (HDC) module to capture the fragile retinal blood vessels usually neglected by other methods. An improved residual dual efficient channel attention (RDECA) module can infer more delicate channel information to reinforce the discriminative capability of the model. The structured Dropblock can help our HDC-Net model to solve the network overfitting effectively. From a holistic perspective, the segmentation results obtained by HDC-Net are superior to other deep learning methods on three acknowledged datasets (DRIVE, CHASE-DB1, STARE), the sensitivity, specificity, accuracy, f1-score and AUC score are {0.8252, 0.9829, 0.9692, 0.8239, 0.9871}, {0.8227, 0.9853, 0.9745, 0.8113, 0.9884}, and {0.8369, 0.9866, 0.9751, 0.8385, 0.9913}, respectively. It surpasses most other advanced retinal vessel segmentation models. Qualitative and quantitative analysis demonstrates that HDC-Net can fulfill the task of retinal vessel segmentation efficiently and accurately.
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Goto, M., A. E. Flynn, J. W. Doucette, C. M. Jansen, M. M. Stork, D. L. Coggins, D. D. Muehrcke, W. K. Husseini, and J. I. Hoffman. "Cardiac contraction affects deep myocardial vessels predominantly." American Journal of Physiology-Heart and Circulatory Physiology 261, no. 5 (November 1, 1991): H1417—H1429. http://dx.doi.org/10.1152/ajpheart.1991.261.5.h1417.
Повний текст джерелаАнотація:
To evaluate the roles of intramyocardial forces and systolic ventricular pressure in myocardial flow in the different layers separately, we measured myocardial flow in rabbit hearts during stable systolic contracture with left ventricular pressures of 60 (n = 5) and 0 mmHg (n = 5) and during stable diastolic arrest (n = 5). We also measured the number and size of the intramyocardial vessels after perfusion fixation (systolic arrest, n = 5; diastolic arrest, n = 5). In 25 rabbits, hearts were excised and perfused from the aortic root. Systolic arrest was achieved by perfusion of a low-Ca2+ Tyrode solution containing 2.0 mM Ba2+. Diastolic arrest was achieved by intraventricular injection of 700-1,000 mg pentobarbital sodium and was maintained by perfusion with St. Thomas cardioplegic solution. At perfusion pressure of 100 mmHg, subendocardial flow was lower than subepicardial flow during systolic arrest regardless of left ventricular pressure, whereas during diastolic arrest, subendocardial flow was higher than subepicardial flow. Subendocardial-to-subepicardial flow ratios for a physiological range of perfusion pressures were lower during systolic arrest with low rather than with high left ventricular pressure. Small arteriolar and capillary densities showed no difference between subendocardium and subepicardium. During systolic arrest, diameters of subendocardial terminal arterioles (4.6 +/- 1.3 microns) and capillaries (4.0 +/- 1.3 microns) were smaller than those in the subepicardium (8.8 +/- 1.7 and 7.1 +/- 1.6 microns, respectively; P less than 0.0001), whereas during diastolic arrest, diameters of subendocardial terminal arterioles (10.1 +/- 2.0 microns) and capillaries (7.6 +/- 1.8 microns) were slightly larger than those in the subepicardium (9.5 +/- 1.5 and 6.7 +/- 1.0 microns, respectively; P less than 0.01). We conclude that cardiac contraction predominantly affects subendocardial vessels and impedes subendocardial flow more than subepicardial flow regardless of left ventricular pressure.
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Arsalan, Owais, Mahmood, Cho, and Park. "Aiding the Diagnosis of Diabetic and Hypertensive Retinopathy Using Artificial Intelligence-Based Semantic Segmentation." Journal of Clinical Medicine 8, no. 9 (September 11, 2019): 1446. http://dx.doi.org/10.3390/jcm8091446.
Повний текст джерелаАнотація:
Automatic segmentation of retinal images is an important task in computer-assisted medical image analysis for the diagnosis of diseases such as hypertension, diabetic and hypertensive retinopathy, and arteriosclerosis. Among the diseases, diabetic retinopathy, which is the leading cause of vision detachment, can be diagnosed early through the detection of retinal vessels. The manual detection of these retinal vessels is a time-consuming process that can be automated with the help of artificial intelligence with deep learning. The detection of vessels is difficult due to intensity variation and noise from non-ideal imaging. Although there are deep learning approaches for vessel segmentation, these methods require many trainable parameters, which increase the network complexity. To address these issues, this paper presents a dual-residual-stream-based vessel segmentation network (Vess-Net), which is not as deep as conventional semantic segmentation networks, but provides good segmentation with few trainable parameters and layers. The method takes advantage of artificial intelligence for semantic segmentation to aid the diagnosis of retinopathy. To evaluate the proposed Vess-Net method, experiments were conducted with three publicly available datasets for vessel segmentation: digital retinal images for vessel extraction (DRIVE), the Child Heart Health Study in England (CHASE-DB1), and structured analysis of retina (STARE). Experimental results show that Vess-Net achieved superior performance for all datasets with sensitivity (Se), specificity (Sp), area under the curve (AUC), and accuracy (Acc) of 80.22%, 98.1%, 98.2%, and 96.55% for DRVIE; 82.06%, 98.41%, 98.0%, and 97.26% for CHASE-DB1; and 85.26%, 97.91%, 98.83%, and 96.97% for STARE dataset.
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Yang, Dan, Mengcheng Ren, and Bin Xu. "Retinal Blood Vessel Segmentation with Improved Convolutional Neural Networks." Journal of Medical Imaging and Health Informatics 9, no. 6 (August 1, 2019): 1112–18. http://dx.doi.org/10.1166/jmihi.2019.2733.
Повний текст джерелаАнотація:
Retinal blood vessel feature is one of crucial biomarkers for ophthalmologic and cardiovascular diseases, efficiency image segmentation technologies will help doctors diagnose these related diseases. We propose an improved deep CNN model to segment retinal blood vessels. Our method includes three steps: Data augmentation, Image preprocessing methods and Model training. The data augmentation uses the rotation and image mirroring to make the training image better generalization. The CLAHE algorithm is used for image preprocessing, which can reduce the image noise and enhance tiny retinal blood vessels features. Finally, we used a deep CNN model combined with U-Net and Dense-Net structure to train retinal blood vessel image. The result of proposed model was tested on public available dataset DRIVE, achieving an average accuracy 0.951, specificity 0.973, sensitivity 0.797 and the average AUC is 0.885. The results show its potential for clinical application.
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Kumar, Priyatham. "Deep Vein Thrombosis and Pulmonary Embolism in Sickle Cell Disease." Biomedical Research and Clinical Reviews 1, no. 5 (December 4, 2020): 01–04. http://dx.doi.org/10.31579/2692-9406/024.
Повний текст джерелаАнотація:
Sickle Cell Disease (SCD) is considered a group of genetic red blood cell (RBC) disorders. Healthy red blood cells (RBC) are round in shape and migrates throughout the body to carry oxygen in the small blood vessels. In SCD, the RBC turns into hard and sticky, and the shape is similar to a C-Shaped tool called "SICKLE." Because of the early death of the sickle cells, a constant shortage of red blood cells arises. Because of the typical shape of the sickle cells, their movement in the blood vessel is not as smooth as normal RBC and get stuck and clog the blood flow leading to anemia. The changes in shape make the cells more easily destroyed, causing anemia. Defective hemoglobin is the primary cause of SCD.
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Shim, Yong Soo, Dong-Won Yang, Catherine M. Roe, Mary A. Coats, Tammie L. Benzinger, Chengjie Xiong, James E. Galvin, Nigel J. Cairns, and John C. Morris. "Pathological Correlates of White Matter Hyperintensities on Magnetic Resonance Imaging." Dementia and Geriatric Cognitive Disorders 39, no. 1-2 (November 8, 2014): 92–104. http://dx.doi.org/10.1159/000366411.
Повний текст джерелаАнотація:
Background/Aims: We investigated the histopathological correlates of white matter hyperintensities (WMHs) in participants with Alzheimer's disease (AD) or cerebrovascular disease, and in aged controls. Methods: We reviewed 57 participants who had neuropathology and in whom neuroimaging was done. In addition to AD pathology, cortical microinfarcts, lacunes, and cerebral hemorrhages were assessed. Small-vessel disease included arteriolosclerosis and cerebral amyloid angiopathy. Postmortem brain tissue corresponding to regions of WMHs was investigated in 14 participants. The variables included: demyelination of the deep and periventricular white matter (WM), atrophy of the ventricular ependyma, and thickness of blood vessels. Partial Spearman's rank test and linear regression analysis, adjusted for age at the clinical evaluation and the duration to death, were performed. Results: The severity of arteriosclerosis was correlated with the volume of periventricular hyperintensity (PVH) estimated by magnetic resonance imaging. Deep white matter hyperintensity (DWMH) volume was correlated with the presence of cortical microinfarcts and cerebral hemorrhages. The severity of the breakdown of the ventricular lining was correlated with PVHs, and DWMHs correlated with the severity of deep WM demyelination. The diameter of small blood vessels was not associated with WMHs. Conclusion: WMHs are consistent with small-vessel disease and increase the tissue water content. We found no association between WMHs and the thickness of small blood vessels. © 2014 S. Karger AG, Basel
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Liu, Congjun, Penghui Gu, and Zhiyong Xiao. "Multiscale U-Net with Spatial Positional Attention for Retinal Vessel Segmentation." Journal of Healthcare Engineering 2022 (January 10, 2022): 1–10. http://dx.doi.org/10.1155/2022/5188362.
Повний текст джерелаАнотація:
Retinal vessel segmentation is essential for the detection and diagnosis of eye diseases. However, it is difficult to accurately identify the vessel boundary due to the large variations of scale in the retinal vessels and the low contrast between the vessel and the background. Deep learning has a good effect on retinal vessel segmentation since it can capture representative and distinguishing features for retinal vessels. An improved U-Net algorithm for retinal vessel segmentation is proposed in this paper. To better identify vessel boundaries, the traditional convolutional operation CNN is replaced by a global convolutional network and boundary refinement in the coding part. To better divide the blood vessel and background, the improved position attention module and channel attention module are introduced in the jumping connection part. Multiscale input and multiscale dense feature pyramid cascade modules are used to better obtain feature information. In the decoding part, convolutional long and short memory networks and deep dilated convolution are used to extract features. In public datasets, DRIVE and CHASE_DB1, the accuracy reached 96.99% and 97.51%. The average performance of the proposed algorithm is better than that of existing algorithms.
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Raza, Mohsin, Khuram Naveed, Awais Akram, Nema Salem, Amir Afaq, Hussain Ahmad Madni, Mohammad A. U. Khan, and Mui-zzud din. "DAVS-NET: Dense Aggregation Vessel Segmentation Network for retinal vasculature detection in fundus images." PLOS ONE 16, no. 12 (December 31, 2021): e0261698. http://dx.doi.org/10.1371/journal.pone.0261698.
Повний текст джерелаАнотація:
In this era, deep learning-based medical image analysis has become a reliable source in assisting medical practitioners for various retinal disease diagnosis like hypertension, diabetic retinopathy (DR), arteriosclerosis glaucoma, and macular edema etc. Among these retinal diseases, DR can lead to vision detachment in diabetic patients which cause swelling of these retinal blood vessels or even can create new vessels. This creation or the new vessels and swelling can be analyzed as biomarker for screening and analysis of DR. Deep learning-based semantic segmentation of these vessels can be an effective tool to detect changes in retinal vasculature for diagnostic purposes. This segmentation task becomes challenging because of the low-quality retinal images with different image acquisition conditions, and intensity variations. Existing retinal blood vessels segmentation methods require a large number of trainable parameters for training of their networks. This paper introduces a novel Dense Aggregation Vessel Segmentation Network (DAVS-Net), which can achieve high segmentation performance with only a few trainable parameters. For faster convergence, this network uses an encoder-decoder framework in which edge information is transferred from the first layers of the encoder to the last layer of the decoder. Performance of the proposed network is evaluated on publicly available retinal blood vessels datasets of DRIVE, CHASE_DB1, and STARE. Proposed method achieved state-of-the-art segmentation accuracy using a few number of trainable parameters.
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Kontopoulos, Ioannis, Antonios Makris, and Konstantinos Tserpes. "A Deep Learning Streaming Methodology for Trajectory Classification." ISPRS International Journal of Geo-Information 10, no. 4 (April 8, 2021): 250. http://dx.doi.org/10.3390/ijgi10040250.
Повний текст джерелаАнотація:
Due to the vast amount of available tracking sensors in recent years, high-frequency and high-volume streams of data are generated every day. The maritime domain is no different as all larger vessels are obliged to be equipped with a vessel tracking system that transmits their location periodically. Consequently, automated methodologies able to extract meaningful information from high-frequency, large volumes of vessel tracking data need to be developed. The automatic identification of vessel mobility patterns from such data in real time is of utmost importance since it can reveal abnormal or illegal vessel activities in due time. Therefore, in this work, we present a novel approach that transforms streaming vessel trajectory patterns into images and employs deep learning algorithms to accurately classify vessel activities in near real time tackling the Big Data challenges of volume and velocity. Two real-world data sets collected from terrestrial, vessel-tracking receivers were used to evaluate the proposed methodology in terms of both classification and streaming execution performance. Experimental results demonstrated that the vessel activity classification performance can reach an accuracy of over 96% while achieving sub-second latencies in streaming execution performance.
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Parsons, Miles, and Mark Meekan. "Acoustic Characteristics of Small Research Vessels." Journal of Marine Science and Engineering 8, no. 12 (November 27, 2020): 970. http://dx.doi.org/10.3390/jmse8120970.
Повний текст джерелаАнотація:
Vessel noise is an acute and chronic stressor of a wide variety of marine fauna. Understanding, modelling and mitigating the impacts of this pollutant requires quantification of acoustic signatures for various vessel classes for input into propagation models and at present there is a paucity of such data for small vessels (<25 m). Our study provides this information for three small vessels (<6 m length and 30, 90 and 180 hp engines). The closest point of approach was recorded at various ranges across a flat, ≈10 m deep sandy lagoon, for multiple passes at multiple speeds (≈5, 10, 20, 30 km h−1) by each vessel at Lizard Island, Great Barrier Reef, Australia. Radiated noise levels (RNLs) and environment-affected source levels (ASLs) determined by linear regression were estimated for each vessel and speed. From the slowest to fastest speeds, median RNLs ranged between 153.4 and 166.1 dB re 1 µPa m, whereas ASLs ranged from 146.7 to 160.0 dB re 1 µPa m. One-third octave band-level RNLs are provided for each vessel–speed scenario, together with their interpolated received levels with range. Our study provides data on source spectra of small vessels to assist in understanding and modelling of acoustic exposure experienced by marine fauna.
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Mehrzadi, Mojtaba, Yacine Terriche, Chun-Lien Su, Peilin Xie, Najmeh Bazmohammadi, Matheus N. Costa, Chi-Hsiang Liao, Juan C. Vasquez, and Josep M. Guerrero. "A Deep Learning Method for Short-Term Dynamic Positioning Load Forecasting in Maritime Microgrids." Applied Sciences 10, no. 14 (July 16, 2020): 4889. http://dx.doi.org/10.3390/app10144889.
Повний текст джерелаАнотація:
The dynamic positioning (DP) system is a progressive technology, which is used in marine vessels and maritime structures. To keep the ship position from displacement in operation mode, its thrusters are used automatically to control and stabilize the position and heading of vessels. Hence, the DP load forecasting is already an essential part of DP vessels, which the DP power demand from the power management system (PMS) for thrusting depends on weather conditions. Furthermore, the PMS is used to control power generation, and prevent power failure, limitation. To perform station keeping of vessels by DPS in environmental changes such as wind, waves, capacity, and reliability of the power generators. Hence, a lack of power may lead to lower DP performance, loss of power, and position, which is called shutdown. Therefore, precise DP power demand prediction for maintaining the vessel position can provide the PMS with sufficient information for better performance in a complex decision-making process for the DP vessel. In this paper, the concept of deep learning techniques is introduced into DPS for DP load forecasting. A Levenberg–Marquardt algorithm based on a nonlinear recurrent neural network is employed in this paper for predicting thrusters’ power consumption in sea state variations due to challenges in power generation with the relative degree of accuracy by combining weather parameter dependencies as environmental disturbances. The proposed method evaluates with three traditional forecasting methods through a set of practical real-time DP load and weather parametric data. Numerical analysis has shown that with the proposed method, the future DP load behavior can be predicted more accurately than that obtained from the traditional methods, which greatly assists in operation and planning of power system to maintain system stability, security, reliability, and economics.
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Kalinin, R. E., I. A. Suchkov, E. A. Klimentova, I. N. Shanayev, and R. M. Khashumov. "The Algorithm for the Study of Deep Femoral Vessels Using Ultrasound Duplex Angioscanning." Russian Sklifosovsky Journal "Emergency Medical Care" 11, no. 4 (February 4, 2023): 676–82. http://dx.doi.org/10.23934/2223-9022-2022-11-4-676-682.
Повний текст джерелаАнотація:
The deep femoral vessels are the main branches/donor vessels of the femoral vessels. Their branches penetrate the entire array of muscles of the posteromedial group and descend almost to the popliteal region, so they are able to both largely compensate for blood flow disorders in obliterating atherosclerosis of the arteries of the lower extremities, and in the presence of an anastomosis with the popliteal vein, influence the course of acute and chronic vein diseases. The modern standard for studying the vascular system of the lower extremities is duplex scan, but it allows deep femoral vessels to be examined only in the ostium segment up to 5–6 cm.AIM OF STUDY To determine the patency and state of blood flow in the deep femoral vessels throughout the entire length using ultrasound duplex angioscanning.MATERIAL AND METHODS The analysis of the results of 30 computed tomograms and 100 ultrasound scans of patients (aged 20 to 85 years) who underwent routine examination of the vascular system of the lower extremities in a polyclinic setting was carried out. The study was performed according to the original method (Patent for invention No. 2751819).RESULTS In the upper third of the thigh, the deep femoral vessels are located most superficially, 2.3±0.15 cm from the skin surface and 0.5±0.08 cm from the posterior wall of the femoral artery. In the middle third of the thigh, the depth of the deep femoral vessels is 3.5±0.9 cm from the skin surface and 4.3±0.24 cm from the posterior wall of the femoral artery. The deep femoral vessels are located between the vastus medialis and adductor longus muscles closer to the femur. In the lower third of the thigh, deep femoral vessels are located at a distance of 4.3±0.4 cm from the skin surface and 1.8±0.5 cm from the posterior wall of the femoral artery. Therefore, for ultrasound examination, a linear probe is first used, which is placed along the projection line of the femoral vessels in the upper third of the thigh, and then the orifice of the deep femoral vessels is visualized. Next, a convex probe is used, and in the middle and lower third of the thigh, it is drawn along a line located 2 cm medially to the projection line of the femoral vessels, while the probe itself deviates posteriorly by ~ 15°.CONCLUSION The research algorithm helps increase the length of the areas of the deep femoral artery and vein available for research and help the physician choose the optimal method of treating the patient.
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Kalinin, R. E., I. A. Suchkov, E. A. Klimentova, and I. N. Shanaev. "RARE VERSION OF TOPOGRAPHY OF DEEP FEMORAL ARTERY." NAUKA MOLODYKH (Eruditio Juvenium) 8, no. 4 (December 30, 2020): 591–98. http://dx.doi.org/10.23888/hmj202084591-598.
Повний текст джерелаАнотація:
The deep femoral artery is a large branch of the common femoral artery that is of much interest for vascular and endovascular surgeons due to the role it plays in collateral circulation between the vessels of the small pelvis and arteries of the popliteal-tibial segment. In most cases, the deep femoral artery branches off from the posterolateral or posterior surface of the common femoral artery. At the same time, anomalies of development of the deep femoral vessels may become the cause for iatrogenic damages in implementation of the open or endovascular interventions. In the article, a clinical case of a patient referred for a planned ultrasound examination of vessels of the lower limbs before angiographic examination of the vessels of the heart and of the lower limbs, is described that revealed atypical topography of branching of the two trunks of the deep femoral artery from the common femoral artery. The upper trunk of the deep femoral artery branched off from the anteromedial surface of the common femoral artery and in the initial part was positioned above the common femoral vein. The lower trunk of the deep femoral artery sepa-rated from the anterolateral surface of the common femoral artery. Preoperative identification of the variant anatomy of the vessels of the femoral triangle permitted to perform angiographic examination of the coronary vessels through the femoral artery on the contralateral limb without complications.
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NAIR, ARUN T., and K. MUTHUVEL. "AUTOMATED SCREENING OF DIABETIC RETINOPATHY WITH OPTIMIZED DEEP CONVOLUTIONAL NEURAL NETWORK: ENHANCED MOTH FLAME MODEL." Journal of Mechanics in Medicine and Biology 21, no. 01 (February 2021): 2150005. http://dx.doi.org/10.1142/s0219519421500056.
Повний текст джерелаАнотація:
Nowadays, analysis on retinal image exists as one of the challenging area for study. Numerous retinal diseases could be recognized by analyzing the variations taking place in retina. However, the main disadvantage among those studies is that, they do not have higher recognition accuracy. The proposed framework includes four phases namely, (i) Blood Vessel Segmentation (ii) Feature Extraction (iii) Optimal Feature Selection and (iv) Classification. Initially, the input fundus image is subjected to blood vessel segmentation from which two binary thresholded images (one from High Pass Filter (HPF) and other from top-hat reconstruction) are acquired. These two images are differentiated and the areas that are common to both are said to be the major vessels and the left over regions are fused to form vessel sub-image. These vessel sub-images are classified with Gaussian Mixture Model (GMM) classifier and the resultant is summed up with the major vessels to form the segmented blood vessels. The segmented images are subjected to feature extraction process, where the features like proposed Local Binary Pattern (LBP), Gray-Level Co-Occurrence Matrix (GLCM) and Gray Level Run Length Matrix (GLRM) are extracted. As the curse of dimensionality seems to be the greatest issue, it is important to select the appropriate features from the extracted one for classification. In this paper, a new improved optimization algorithm Moth Flame with New Distance Formulation (MF-NDF) is introduced for selecting the optimal features. Finally, the selected optimal features are subjected to Deep Convolutional Neural Network (DCNN) model for classification. Further, in order to make the precise diagnosis, the weights of DCNN are optimally tuned by the same optimization algorithm. The performance of the proposed algorithm will be compared against the conventional algorithms in terms of positive and negative measures.
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Lindorf, Helga. "Eco-Anatomical Wood Features of Species from a Very Dry Tropical Forest." IAWA Journal 15, no. 4 (1994): 361–76. http://dx.doi.org/10.1163/22941932-90001370.
Повний текст джерелаАнотація:
In 19 species of a very dry forest in Venezuela vessel diameter, vessel frequency, vessel grouping, vessel element length, and intervessel pit size, were studied and compared with data from other habitats. A predominance of characters that presumably contribute to hydraulic safety was observed: numerous grouped vessels of small diameter, short vessel elements, and minute intervessel pits. In some species, a xeromorphic wood anatomy coexists together with adaptations such as deciduousness, xeromorphic foliage, deep or superficially-extended roots, and succulence. In other species studied, the presence of xerophytic adaptations such as assimilating stems, succulence, and deep roots, seem to mitigate the xeromorphic wood appearance and, to some extent, lend it a mesomorphic character.
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Islam, Manjurul, Muhammad Sohaib, Jaeyoung Kim, and Jong-Myon Kim. "Crack Classification of a Pressure Vessel Using Feature Selection and Deep Learning Methods." Sensors 18, no. 12 (December 11, 2018): 4379. http://dx.doi.org/10.3390/s18124379.
Повний текст джерелаАнотація:
Pressure vessels (PV) are designed to hold liquids, gases, or vapors at high pressures in various industries, but a ruptured pressure vessel can be incredibly dangerous if cracks are not detected in the early stage. This paper proposes a robust crack identification technique for pressure vessels using genetic algorithm (GA)-based feature selection and a deep neural network (DNN) in an acoustic emission (AE) examination. First, hybrid features are extracted from multiple AE sensors that represent diverse symptoms of pressure vessel faults. These features stem from various signal processing domains, such as the time domain, frequency domain, and time-frequency domain. Heterogenous features from various channels ensure a robust feature extraction process but are high-dimensional, so may contain irrelevant and redundant features. This can cause a degraded classification performance. Therefore, we use GA with a new objective function to select the most discriminant features that are highly effective for the DNN classifier when identifying crack types. The potency of the proposed method (GA + DNN) is demonstrated using AE data obtained from a self-designed pressure vessel. The experimental results indicate that the proposed method is highly effective at selecting discriminant features. These features are used as the input of the DNN classifier, achieving a 94.67% classification accuracy.
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Yang, Lei, Huaixin Wang, Qingshan Zeng, Yanhong Liu, and Guibin Bian. "A hybrid deep segmentation network for fundus vessels via deep-learning framework." Neurocomputing 448 (August 2021): 168–78. http://dx.doi.org/10.1016/j.neucom.2021.03.085.
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Liskowski, Pawel, and Krzysztof Krawiec. "Segmenting Retinal Blood Vessels With_newline Deep Neural Networks." IEEE Transactions on Medical Imaging 35, no. 11 (November 2016): 2369–80. http://dx.doi.org/10.1109/tmi.2016.2546227.
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Koshima, Isao, Kiichi Inagawa, Katsuyuki Urushibara, and Takahiko Moriguchi. "Paraumbilical Perforator Flap without Deep Inferior Epigastric Vessels." Plastic and Reconstructive Surgery 102, no. 4 (September 1998): 1052–57. http://dx.doi.org/10.1097/00006534-199809020-00020.
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Koshima, Isao, Kiichi Inagawa, Katsuyuki Urushibara, and Takahiko Moriguchi. "Paraumbilical Perforator Flap without Deep Inferior Epigastric Vessels." Plastic & Reconstructive Surgery 102, no. 4 (September 1998): 1052–57. http://dx.doi.org/10.1097/00006534-199809040-00020.
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Pradillon, Florence, Bruce Shillito, Jean-Claude Chervin, Gérard Hamel, and Françoise Gaill. "Pressure vessels forin vivostudies of deep-sea fauna." High Pressure Research 24, no. 2 (June 2004): 237–46. http://dx.doi.org/10.1080/08957950410001699818.
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Prajna, Yellamelli, and Malaya Kumar Nath. "Efficient blood vessel segmentation from color fundus image using deep neural network." Journal of Intelligent & Fuzzy Systems 42, no. 4 (March 4, 2022): 3477–89. http://dx.doi.org/10.3233/jifs-211479.
Повний текст джерелаАнотація:
Blood vessel segmentation is an essential element of automatic retinal disease screening systems. In particular, retinal blood vessel analysis from fundus image is vital in the identification and diagnosis of cardiovascular and ophthalmological diseases (Ex: Diabetic Retinopathy, Macular degeneration, Retinal Pigmentosa, Macular Edema, and various stages of Glaucoma, etc). Wherefore, the diagnosis of these diseases by automatic vessel segmentation has become essential, especially in disclosure of premature prognosis of vision condition. In general, blood vessel extraction is divided into vessel tracking and pixel classification. In vessel tracking a vasculature model is expanded from a seed point. In pixel classification, the classifier classifies the pixels as either a vessel or background pixel, which is demonstrated in the proposed architecture. In this paper, deep learning based 19 layer U-Net architecture is proposed for the accurate and efficient segmentation of blood vessels. Prior to segmentation, a pre-processing block of AlexNet architecture is introduced for the classification of high-quality images from the experimented databases. This pre-classification stage helps in efficiently picking high-quality images determined by clarity, field definition, and sharpness. AlexNet classification is pivotal in enhancing the overall performance of the system by segmenting fine and tiny blood vessels. The proposed U-Net architecture has an encoder-decoder framework with 9 and 5 convolutional layers in each respectively. In order to boost the efficiency of the network as well as to reduce training and testing time, a proper choice of kernel dimension and number of filters are necessary. Our architecture was investigated on popular databases such as DRIVE, ARIA_d and MESSIDOR and various performance measures (accuracy, sensitivity, specificity, sensibility, Dice coefficient, and Jaccard coefficient) have been computed along with the Receiver Operating Characteristics. It is observed that the accuracy for DRIVE, ARIA_d and MESSIDOR are 90.60%, 87.60% and 83.42%, respectively. Area under curve in Receiver Operating Characteristics plot is found to be 98.54%, 93.28% and 88.18%, for DRIVE, ARIA_d and MESSIDOR databases, respectively. Results with the proposed architecture show remarkable improvement in the performance metrics for blood vessel segmentation.
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Seong, Ik Hyun, and Kyong-Je Woo. "Comparison of the second and third intercostal spaces regarding the use of internal mammary vessels as recipient vessels in DIEP flap breast reconstruction: An anatomical and clinical study." Archives of Plastic Surgery 47, no. 4 (July 15, 2020): 333–39. http://dx.doi.org/10.5999/aps.2019.01312.
Повний текст джерелаАнотація:
Background The purpose of this study was to compare the anatomical features of the internal mammary vessels (IMVs) at the second and third intercostal spaces (ICSs) with regard to their use as recipient vessels in deep inferior epigastric artery perforator (DIEP) flap breast reconstruction.Methods A total of 38 consecutive DIEP breast reconstructions in 36 patients were performed using IMVs as recipient vessels between March 2017 and August 2018. The intraoperative findings and postoperative complications were analyzed. Anatomical analyses were performed using intraoperative measurements and computed tomography (CT) angiographic images.Results CT angiographic analysis revealed the mean diameter of the deep inferior epigastric artery to be 2.42±0.27 mm, while that of the deep inferior epigastric vein was 2.91±0.30 mm. A larger mean vessel diameter was observed at the second than at the third ICS for both the internal mammary artery (2.26±0.32 mm vs. 1.99±0.33 mm, respectively; P=0.001) and the internal mammary vein (IMv) (2.52±0.46 mm vs. 2.05±0.42 mm, respectively; P<0.001). Similarly, the second ICS was wider than the third (18.08±3.72 mm vs. 12.32±2.96 mm, respectively; P<0.001) and the distance from the medial sternal border to the medial IMv was greater (9.49±2.28 mm vs. 7.18±2.13 mm, respectively; P<0.001). Bifurcations of the IMv were found in 18.4% of cases at the second ICS and in 63.2% of cases at the third ICS.Conclusions The IMVs at the second ICS had more favorable anatomic features for use as recipient vessels in DIEP flap breast reconstruction than those at the third ICS.
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JONES, M. E., K. LADHANI, V. MUDERA, A. O. GROBBELAAR, D. A. MCGROUTHER, and R. SANDERS. "Flexor Tendon Blood Vessels." Journal of Hand Surgery 25, no. 6 (December 2000): 552–59. http://dx.doi.org/10.1054/jhsb.2000.0458.
Повний текст джерелаАнотація:
The aim of this study was to assess rabbit long flexor tendon vascularity in a qualitative and quantitative manner using immunohistochemistry. The endothelial cell surface marker CD31 was targeted with a specific monoclonal mouse-anti-human antibody with good species cross-reactivity. Subsequent signal amplification and chromogen labelling allowed vessel visualization. Computer image analysis was performed. Values for vessel number and total vessel area per section, as well as the sections’ cross-sectional tendon areas, were obtained. There was a consistent deep tendon avascular zone between the A2 and A4 pulley in the rabbit forepaw. This was not the case in the hindpaw, with dorsally orientated longitudinal vessels coursing the length of the intrasynovial tendon. The area of least vascularity in the hindpaw was around the metacarpophalangeal joint. We therefore recommend the use of hindpaw tendons when using the rabbit as a flexor tendon experimental model. This is because its vascular pattern is similar to that of the human flexor digitorum profundus.
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Zhang, Kun, Hongbin Zhang, Huiyu Zhou, Danny Crookes, Ling Li, Yeqin Shao, and Dong Liu. "Zebrafish Embryo Vessel Segmentation Using a Novel Dual ResUNet Model." Computational Intelligence and Neuroscience 2019 (February 3, 2019): 1–14. http://dx.doi.org/10.1155/2019/8214975.
Повний текст джерелаАнотація:
Zebrafish embryo fluorescent vessel analysis, which aims to automatically investigate the pathogenesis of diseases, has attracted much attention in medical imaging. Zebrafish vessel segmentation is a fairly challenging task, which requires distinguishing foreground and background vessels from the 3D projection images. Recently, there has been a trend to introduce domain knowledge to deep learning algorithms for handling complex environment segmentation problems with accurate achievements. In this paper, a novel dual deep learning framework called Dual ResUNet is developed to conduct zebrafish embryo fluorescent vessel segmentation. To avoid the loss of spatial and identity information, the U-Net model is extended to a dual model with a new residual unit. To achieve stable and robust segmentation performance, our proposed approach merges domain knowledge with a novel contour term and shape constraint. We compare our method qualitatively and quantitatively with several standard segmentation models. Our experimental results show that the proposed method achieves better results than the state-of-art segmentation methods. By investigating the quality of the vessel segmentation, we come to the conclusion that our Dual ResUNet model can learn the characteristic features in those cases where fluorescent protein is deficient or blood vessels are overlapped and achieves robust performance in complicated environments.
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Shinohara, Hiroki, Satoshi Kodera, Kota Ninomiya, Mitsuhiko Nakamoto, Susumu Katsushika, Akihito Saito, Shun Minatsuki, et al. "Automatic detection of vessel structure by deep learning using intravascular ultrasound images of the coronary arteries." PLOS ONE 16, no. 8 (August 5, 2021): e0255577. http://dx.doi.org/10.1371/journal.pone.0255577.
Повний текст джерелаАнотація:
Intravascular ultrasound (IVUS) is a diagnostic modality used during percutaneous coronary intervention. However, specialist skills are required to interpret IVUS images. To address this issue, we developed a new artificial intelligence (AI) program that categorizes vessel components, including calcification and stents, seen in IVUS images of complex lesions. When developing our AI using U-Net, IVUS images were taken from patients with angina pectoris and were manually segmented into the following categories: lumen area, medial plus plaque area, calcification, and stent. To evaluate our AI’s performance, we calculated the classification accuracy of vessel components in IVUS images of vessels with clinically significantly narrowed lumina (< 4 mm2) and those with severe calcification. Additionally, we assessed the correlation between lumen areas in manually-labeled ground truth images and those in AI-predicted images, the mean intersection over union (IoU) of a test set, and the recall score for detecting stent struts in each IVUS image in which a stent was present in the test set. Among 3738 labeled images, 323 were randomly selected for use as a test set. The remaining 3415 images were used for training. The classification accuracies for vessels with significantly narrowed lumina and those with severe calcification were 0.97 and 0.98, respectively. Additionally, there was a significant correlation in the lumen area between the ground truth images and the predicted images (ρ = 0.97, R2 = 0.97, p < 0.001). However, the mean IoU of the test set was 0.66 and the recall score for detecting stent struts was 0.64. Our AI program accurately classified vessels requiring treatment and vessel components, except for stents in IVUS images of complex lesions. AI may be a powerful tool for assisting in the interpretation of IVUS imaging and could promote the popularization of IVUS-guided percutaneous coronary intervention in a clinical setting.
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Nascimento, Ariel Victor do, Marcus Pinto da Costa da Rocha, Valcir João da Cunha Farias, and Miércio Cardoso de Alcântara Neto. "Development of a Convolutional Neural Network for Classification of Type of Vessels." International Journal of Advanced Engineering Research and Science 10, no. 1 (2023): 156–60. http://dx.doi.org/10.22161/ijaers.101.21.
Повний текст джерелаАнотація:
in this paper, was used a method which use concepts of intelligence artificial, machine learning, deep learning for Classification of Type of Vessels. With a technique from deep learning called Convolutional Neural Network (CNN) was applied to recognize images to identify the type of ship and to use the same method to identify if the vessel. The CNN projected with determined 5 layers, the first layer containing 32 neurons, the second layer with 64 neurons, the third layer with 128 neurons, the fourth layer with 512 neurons. Activation functions for these specified layers contain the ReLU function. The fifth and last layer is the output layer is the output layer, so the number of neurons is equal to the number of vessel type. In our study six classes were used, which are the vessel types, in this layer the activation function was the Softmax. The CNN generate satisfactory results, where could get results of prediction with all corrects answers to identify the ship.
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Ra, Ho, Nam Yeo Kang, Jiyun Song, Junhyuck Lee, Inkee Kim, and Jiwon Baek. "Discordance in Retinal and Choroidal Vascular Densities in Patients with Type 2 Diabetes Mellitus on Optical Coherence Tomography Angiography." Journal of Ophthalmology 2021 (February 8, 2021): 1–7. http://dx.doi.org/10.1155/2021/8871602.
Повний текст джерелаАнотація:
Purpose. In the present study, the retinal and choroidal vascular densities (VDs) in type 2 diabetes mellitus (DM) patients were analyzed using optical coherence tomography angiography (OCTA). Methods. The study included 282 eyes of 152 patients with type 2 DM (114 without retinopathy, 79 nonproliferative diabetic retinopathy (NPDR), 48 severe NPDR, and 41 proliferative diabetic retinopathy (PDR) eyes). The superficial and deep retinal vessel, choriocapillaris, and choroidal VDs were measured using a binarization method on OCTA images. VDs were compared based on retinopathy severity. Correlations among densities were analyzed. Results. Retinal and choriocapillaris VDs were lower in PDR than in NPDR (all P < 0.05 ). Correlation analysis showed significant positive correlations among densities of superficial and deep retinal vessels and choriocapillaris (all P < 0.001 ). Choroidal VD showed a negative correlation with superficial and deep retinal vessels and choriocapillaris (all P < 0.001 ). Retinal and choriocapillaris VDs showed a negative correlation with diabetic retinopathy (DR) grade (all P < 0.001 ); however, the choroidal VD showed a weak positive correlation ( P = 0.030 ). Conclusion. Choroidal VD increased as retinal and choriocapillaris VDs decreased, indicating that the outer layer of the choroid is less affected by DR severity and VD of larger choroidal vessels may even be increased as a compensatory mechanism for decreased retinal and choriocapillaris VDs in type 2 DM patients.
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Kholdi, Mohsen, Abbas Loghman, Hossein Ashrafi, and Mohammad Arefi. "Analysis of thick-walled spherical shells subjected to external pressure: Elastoplastic and residual stress analysis." Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications 234, no. 1 (October 21, 2019): 186–97. http://dx.doi.org/10.1177/1464420719882958.
Повний текст джерелаАнотація:
When cylindrical and spherical vessels are subjected to the internal pressure, tensile tangential stresses are created throughout the thickness, the maximum of which are located at the inner surface of the vessels. To improve the performance of these vessels, autofrettage process has been devised to produce beneficial compressive residual stresses at the inner part of such vessels. The question arises whether the process such as autofrettage can be useful for vessels such as submarines or other thick walled tanks, which are used in deep sea waters and, therefore, subjected to high external hydrostatic pressure causing both radial and tangential stresses to be compressive across the thickness. On the other hand, is the residual stresses created by unloading from an external pressure beyond elastic limit beneficial and enhance their performances? In this study, elastoplastic and residual stresses in a thick-walled spherical vessel under external hydrostatic pressure has been investigated. The material behavior is considered to be elastic-perfectly plastic. Von Misses yield criterion is used to obtain initial yield point and for the ideal elastoplastic regime analytical relations are presented. It has been found that by applying external hydrostatic pressure yielding process will start from inside of the sphere. Finally after unloading, residual tensile stresses are created at the inner part of the vessel which is useful for the vessel. The residual stresses and the condition of reverse yielding is studied in this paper.
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Saxena, Eshika. "Deep Learning for Personalized Preoperative Planning of Microsurgical Free Tissue Transfers." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (June 28, 2022): 13140–41. http://dx.doi.org/10.1609/aaai.v36i11.21706.
Повний текст джерелаАнотація:
Breast reconstruction surgery requires extensive planning, usually with a CT scan that helps surgeons identify which vessels are suitable for harvest. Currently, there is no quantitative method for preoperative planning. In this work, we successfully develop a Deep Learning algorithm to segment the vessels within the region of interest for breast reconstruction. Ultimately, this information will be used to determine the optimal reconstructive method (choice of vessels, extent of the free flap/harvested tissue) to reduce intra- and postoperative complication rates.
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Kim, Youngchul, Youngchul Suh, JoonPio Hong, and Hyunsuk Suh. "Multidetector Computed Tomography (CT) Analysis of 168 Cases in Diabetic Patients with Total Superficial Femoral Artery Occlusion: Is It Safe to Use an Anterolateral Thigh Flap without CT Angiography in Diabetic Patients?" Journal of Reconstructive Microsurgery 34, no. 01 (September 13, 2017): 065–70. http://dx.doi.org/10.1055/s-0037-1606340.
Повний текст джерелаАнотація:
Background The superficial femoral artery (SFA) is the most common site of lower extremity atherosclerosis, and collateral vessels from the deep femoral artery (DFA) play an important compensatory role between the iliofemoral segment and the popliteal artery. We examined SFA occlusion and collateral vessel developments in patients with diabetes mellitus using computed tomography (CT) angiography. We also compared the collateral systems from the DFA and the descending branch of the lateral circumflex femoral artery (dbLCFA) in the case of SFA occlusion. Methods We retrospectively reviewed 1,316 sets of CT angiographic data collected from 673 patients with diabetes between 2008 and 2010. The degree of stenosis in each segment of the proximal and distal SFA and the number and size of collateral vessels originating from the DFA and dbLCFA were measured using established scoring systems. In cases where the SFA was occluded, the numbers of collateral vessels originating from the DFA and the dbLCFA vessel were compared. Results The mean occlusion rate of the SFA was 15.6%. We noted that collateral vessels from DFA and dbLCFA were the main circulatory route in cases of occlusions of the SFA. More collateral vessels developed from the DFA than from the dbLCFA. Overall, 0.6% of the patients had only collateral systems from the dbLCFA. Conclusion When planning to use anterolateral thigh free flaps in diabetic patients with suspected SFA total occlusion, thorough investigations of the peripheral vessels are essential.
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Leyngold, Mark, and Carlos Rivera-Serrano. "Microvascular Penile Replantation Utilizing the Deep Inferior Epigastric Vessels." Journal of Reconstructive Microsurgery 30, no. 08 (July 4, 2014): 581–84. http://dx.doi.org/10.1055/s-0034-1383427.
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