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Добірка наукової літератури з теми "Deep vessels"

Автор: Grafiati

Опубліковано: 10 березня 2023

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Статті в журналах з теми "Deep vessels"

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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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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Дисертації з теми "Deep vessels"

Černohorská, Lucie. "Klasifikace arteriálního a žilního řečiště v obrazových datech sítnice." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2020. http://www.nusl.cz/ntk/nusl-413018.

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This master's thesis deals with the classification of the retinal blood vessels in retinal image data. The thesis contains a description of anatomy of the human eye with focus on the blood circulation, and imaging and diagnostic methods of the retina are briefly mentioned further. The thesis also summarizes methods of the blood circulation classification with emphasis on the deep learning. The practical section was implemented in Python programming language and describes the pre-processing of the data with determination of AV ratio. Based on a literature search, the U-net architecture was chosen for the classification of the retinal blood vessels. The architecture was modified using the open-source Keras library and tested on images from the experimental video-ophthalmoscope. The modified architecture was initially used for classification of vessels into the corresponding classes and because of unsatisfying results was modified another architecture segmenting retinal vessels, arteries or veins and a proposition of a method of the blood vessels classification.

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Rozhoňová, Andrea. "Metody hlubokého učení pro segmentaci cév a optického disku v oftalmologických sekvencích." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2019. http://www.nusl.cz/ntk/nusl-400968.

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The aim of the following thesis was to study the issue of optical disc and retinal vessels segmentation in ophthalmologic sequences. The theoretical part of the thesis summarizes the principles of different approaches in the field of deep learning, which are used in connection with the given issue. Based on the theoretical part, methods for optical disk segmentation and retinal vessel segmentation based on the convolutional neural networks Linknet, PSPNet, Unet and MaskRCNN are proposed. The practical part of the thesis deals with the description of their implementation and subsequent evaluation.

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Kirby, David Shigeta. "Simulation and validation of deep drawing of pressure vessel end closures." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape17/PQDD_0001/MQ36041.pdf.

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Douglas, Helen E. "Perforating blood vessel selection in deep inferior epigastric artery perforator flaps." Thesis, University of Glasgow, 2014. http://theses.gla.ac.uk/5516/.

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Introduction: The DIEP flap is a popular choice for breast reconstruction, though selection of which perforating blood vessel(s) to supply the flap is still largely based on surgeon preference, with little evidence to support numbers or location of perforators. In addition, many surgeons routinely discard zone IV of the flap, limiting the size of transferrable tissue. The aim of this research was to investigate the effect of number and location of perforators within a DIEP flap, on the total pedicle flow and perfusion of zone IV fat and skin. Methods: This research comprised of two studies; an animal model and a patient study: 1) 20 cranially-based abdominal epigastric perforator flaps were raised in Wistar rats on two perforators. The perforators were sequentially clamped and released in a randomised order and total pedicle flow (measured using microvascular flow-probes) and skin perfusion (measured using laser Doppler Flowmetry) was recorded on the following perforator combinations: • P1 (superior perforator) • P2 (inferior perforator) • P1+2 (both perforators) In addition, half of the animal flaps were randomised to receive a single (15 minute) period of pedicle-clamped ischaemic preconditioning after raising, with all measurements repeated to observe any effect. 2) 13 DIEP flaps were raised in post-mastectomy patients requiring breast reconstruction on two perforators. These were clamped and released as before to assess perfusion of fat and skin in zone IV using SPY Indocyanine-green-fluorescence-angiography scans on the same perforator combinations as in our animal study, listed above. Results: All data were analysed using non-parametric analyses and revealed that in our animal model, total pedicle flow was significantly (p<0.001) greater on a single perforator compared to two but no significant differences were identified in the flap skin perfusion. In our clinical study a single superior perforator supplied zone IV significantly (p=0.039) better than both peroforators, though this was not observed with the single inferior perforator. No significant differences were seen in zone IV skin perfusion. A single period of ischaemic preconditioning significantly (p<0.05) increased the total pedicle flow, but not the skin perfusion in our rat model. Conclusions: Possible reasons for these observed differences could be related to the flow dynamics and resistances specific to perforator flap anatomy and physiology and the possibility of vessel shunting in the subcutis.

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Hofmann, Matthias Colin. "Localized Excitation Fluorescence Imaging (LEFI)." Diss., Virginia Tech, 2012. http://hdl.handle.net/10919/27749.

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A major limitation in tissue engineering is the lack of nondestructive methods to assess the development of tissue scaffolds undergoing preconditioning in bioreactors. Due to significant optical scattering in most scaffolding materials, current microscope-based imaging methods cannot â seeâ through thick and optically opaque tissue constructs. To address this deficiency, we developed a scanning fiber imaging method capable of nondestructive imaging of fluorescently labeled cells through a thick and optically opaque vascular scaffold, contained in a bioreactor. This imaging modality is based on local excitation of fluorescent cells, acquisition of fluorescence through the scaffold, and fluorescence mapping based on the position of the excitation light. To evaluate the capability and accuracy of the imaging system, human endothelial cells, stably expressing green fluorescent protein (GFP), were imaged through a fibrous scaffold. Without sacrificing the scaffolds, we nondestructively visualized the distribution of GFP-labeled endothelial cells on the luminal surface through a ~500 Âµm thick tubular scaffold at cell-level resolutions and distinct localization. These results were similar to control images obtained using an optical microscope with direct line-of-sight access. Through a detailed quantitative analysis, we demonstrated that this method achieved a resolution of the order of 20-30 Âµm, with 10% or less deviation from standard optical microscopy. Furthermore, we demonstrated that the penetration depth of the imaging method exceeded that of confocal laser scanning microscopy by more than a factor of 2. Our imaging method also possesses a working distance (up to 8 cm) much longer than that of a standard confocal microscopy system, which can significantly facilitate bioreactor integration. This method will enable nondestructive monitoring of endothelial cells seeded on the lumen of a tissue-engineered vascular graft during preconditioning in vitro, as well as for other tissue-engineered constructs in the future.
Ph. D.

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Hematian, Jamal. "Finite element modeling of wrinkling during deep drawing of pressure vessel end closures (PVECs)." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/MQ55911.pdf.

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Guerrero, Julian. "System for vessel characterization : development and evaluation with application to deep vein thrombosis diagnosis." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/1558.

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A system for vessel characterization aimed at detecting deep vein thrombosis (DVT) in the lower limbs has been developed and evaluated using ultrasound image processing, location and force sensors measurements, blood flow information and a protocol based on the current clinical standard, compression ultrasound. The goal is to provide an objective and repeatable system to measure DVT in a rapid and standardized manner, as this has been suggested in the literature as an approach to improve overall detection of the disease. The system uses a spatial Kalman filter-based algorithm with an elliptical model in the measurement equation to detect vessel contours in transverse ultrasound images and estimate ellipse parameters, and temporal constant velocity Kalman filters for tracking vessel location in real-time. The vessel characterization also comprises building a 3-D vessel model and performing compression and blood flow assessments to calculate measures that indicate the possibility of DVT in a vessel. A user interface designed for assessing a vessel for DVT was also developed. The system and components were implemented and tested in simulations, laboratory settings, and clinical settings. Contour detection results are good, with mean and rms errors ranging from 1.47-3.64 and 3.69-9.67 pixels, respectively, in simulated and patient images, and parameter estimation errors of 5%. Experiments showed errors of 3-5 pixels for the tracking approaches. The measures for DVT were evaluated, independently and integrated in the system. The complete system was evaluated, with sensitivity of 67-100% and specificity of 50-89.5%. System learnability and memorability were evaluated in a separate user study, with good results. Contributions include a segmentation approach using a full parameter ellipse model in an extended Kalman filter, incorporating multiple measurements, an alternate sampling method for faster parameter convergence and application-specific initialization, and a tracking approach that includes a sub-sampled sum of absolutes similarity calculation and a method to detect vessel bifurcations using flow data. Further contributions include an integrated system for DVT detection that can combine ultrasound B-mode, colour flow and elastography images for vessel characterization, a system interface design focusing on usability that was evaluated with medical professionals, and system evaluations through multiple patient studies.

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Bondada, Harshith. "Retinal Vessel Segmentation on Ultra Wide-field Fluorescein Angiography Images." University of Cincinnati / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1573811275083678.

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Borra, Davide. "Sviluppo ed applicazione di reti neurali convoluzionali con dati di neuroimaging." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018.

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La malattia di Alzheimer (AD) è un disordine neurodegenerativo che rappresenta la forma più comune di demenza negli adulti sopra i 65 anni, mentre la compromissione cognitiva lieve (MCI) è una condizione che in alcuni casi può rappresentare una fase prodromica della malattia di Alzheimer, mentre in altri, è comune in pazienti con la malattia dei piccoli vasi cerebrali (SVD). In questo elaborato sono state sviluppate due reti neurali convoluzionali 2-D, NeuroNet-1 e NeuroNet-2 (o NeuroNet), ed applicate alla classificazione a 2 vie di: a) MCI con SVD (40 pazienti in totale) con dati di diffusione come gli indici di diffusività media (MD), anisotropia frazionaria (FA) e moda del tensore di diffusione (MO); b) AD (200 pazienti in totale) con dati MRI T1-pesati. NeuroNet-2 è basata su NeuroNet-1, attraverso considerazioni frutto di una fase preliminare di studio. I risultati sui dati di diffusione suggeriscono che, l'utilizzo di un approccio multi-modalità e di un numero di fette analizzate in relazione al numero di soggetti, comportino risultati migliori. Infatti, l'accuratezza sui dati di test ottenuta nello studio multi-modalità su 6 fette è di 0.97±0.08 (media±deviazione standard). Inoltre, l'utilizzo di tecniche di interpretazione dell'apprendimento, come Gradient-weighted Class Activation Mapping (Grad-CAM) e test di occlusione, ha permesso di valutare le regioni cerebrali più importanti nella predizione e dimostrare che, oltre ai lobi frontali, alcune regioni di sostanza bianca come il corpo calloso, il tapetum, le radiazioni talamiche posteriori ed il braccio anteriore della capsula interna, sono molto importanti nella predizione delle funzioni esecutive. Infine, nello studio che riporta risultati migliori, l'ordinamento secondo importanza delle mappe di indici di diffusione considerate risulta MD>MO>FA. Invece, sui dati MRI T1-pesati è stata ottenuta un'accuratezza sui dati di test di 0.97±0.01.

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Breda, Pedro Filipe Cavaleiro. "Deep Learning for the Segmentation of Vessels in Retinal Fundus images and its Interpretation." Master's thesis, 2018. https://hdl.handle.net/10216/116105.

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The main goal of this dissertation is to study and analyze different approaches based on deep learning techniques for the segmentation of retinal blood vessels. In order to do so, different design and architectures of CNN's will be studied and analysed, as their results and performance are evaluated and compared with the available algorithms. One other important objective of this work is to study and evaluate the different techniques that have been used for vessel segmentation, such as machine learning, and how these can be combined with the deep learning approaches. By Analyzing the features that the learned models are using to perform classification and combining them with different machine learning techniques (such as Random Forest and SVM Classifiers), another goal is to proposed a solution or set of solutions to perform the retinal vessel segmentation.

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Книги з теми "Deep vessels"

R, Bass B., Oak Ridge National Laboratory, and U.S. Nuclear Regulatory Commission. Office of Nuclear Regulatory Research. Division of Engineering., eds. A comparison of analysis methodologies for predicting cleavage arrest of a deep crack in a reactor pressure vessel subjected to pressurized-thermal-shock loading conditions. Washington, DC: Division of Engineering, Office of Nuclear Regulatory Research, U.S. Nuclear Regulatory Commission, 1992.

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Müzesi, Bodrum Sualtı Arkeoloji, ed. Sparkles from the deep: Glass vessels of the Bodrum Museum of Underwater Archaeology. Çayırova, Gebze, İstanbul: Bericap, 2000.

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Pelletier, James Laurence. Deep-draft vessel owners, U.S.A. Augusta, Me: Marine Techniques, 1997.

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Pelletier, James Laurence. Deep-draft vessel owners, U.S.A. Augusta, Me: Marine Techniques, 1996.

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Pelletier, James Laurence. Deep-draft vessel owners, foreign. Augusta, Me: Marine Techniques, 1996.

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(Firm), Odyssey Marine Exploration, ed. Oceans Odyssey 3: The deep-sea Tortugas shipwreck, Straits of Florida : a merchant vessel from Spain's 1622 Tierra Firme fleet. Oxford, UK: Oxbow Books, 2013.

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Canada. Agreement amending treaty with Canada concerning Pacific Coast albacore tuna vessels and port privileges: Message from the President of the United States transmitting agreement amending treaty between the government of the United States of America and the government of Canada on Pacific Coast albacore tuna vessels and port privileges done at Washington, D.C., May 26, 1981 (The "Treaty"), effected by an exchange of diplomatic notes at Washington on July 17, 2002, and August 13, 2002 (The "Agreement"). Washington: U.S. G.P.O., 2003.

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Heaton, P. M. The "Redbrook": A deep-sea tramp : an account of the management and operation of a South Wales owned vessel in the 1960s. Abergavenny: P. M. Heaton, 1995.

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2002), Antarktis-Expedition mit FS "Polarstern" (19th. The expeditions ANTARKTIS-XIX/3-4 of the research vessel Polarstern in 2002: ANDEEP I and II : Antartic benthic deep-sea biodiversity--colonization history and recent community patterns. Bremerhaven: Alfred-Wegener-Institut für Polar- und Meeresforschung, 2003.

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Частини книг з теми "Deep vessels"

Gurunian, Raffi, Rebecca Knackstedt, Karlina Kegecik, and Richard L. Drake. "Deep Inferior Epigastric Vessels." In Recipient Vessels in Reconstructive Microsurgery, 89–95. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75389-4_15.

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Meyer, Maria Ines, Adrian Galdran, Pedro Costa, Ana Maria Mendonça, and Aurélio Campilho. "Deep Convolutional Artery/Vein Classification of Retinal Vessels." In Lecture Notes in Computer Science, 622–30. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93000-8_71.

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Aghdam, Hamed H., Martin Bouchard, Robert Laganiere, Emil M. Petriu, and Philip Wort. "A Deep Neural Network for Counting Vessels in Sonar Signals." In Advances in Artificial Intelligence, 257–69. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47358-7_25.

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Etemad, Mohammad, Nader Zare, Mahtab Sarvmaili, Amílcar Soares, Bruno Brandoli Machado, and Stan Matwin. "Using Deep Reinforcement Learning Methods for Autonomous Vessels in 2D Environments." In Advances in Artificial Intelligence, 220–31. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47358-7_21.

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Adeyinka, Adegun Adekanmi, Marion Olubunmi Adebiyi, Noah Oluwatobi Akande, Roseline Oluwaseun Ogundokun, Anthonia Aderonke Kayode, and Tinuke Omolewa Oladele. "A Deep Convolutional Encoder-Decoder Architecture for Retinal Blood Vessels Segmentation." In Computational Science and Its Applications – ICCSA 2019, 180–89. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-24308-1_15.

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Chitra, M. T., B. Gayatri Menon, and Elizabeth Sherly. "Real-Time Communication Alert System for Missing Vessels in Deep Sea." In Applied Soft Computing and Communication Networks, 207–22. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3852-0_13.

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Wargnier-Dauchelle, Valentine, Camille Simon-Chane, and Aymeric Histace. "Retinal Blood Vessels Segmentation: Improving State-of-the-Art Deep Methods." In Computer Analysis of Images and Patterns, 5–16. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29930-9_1.

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Sule, Olubunmi, Serestina Viriri, and Mandlenkosi Gwetu. "Contrast Enhancement in Deep Convolutional Neural Networks for Segmentation of Retinal Blood Vessels." In Recent Challenges in Intelligent Information and Database Systems, 278–90. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1685-3_23.

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Virzì, A., P. Gori, C. O. Muller, E. Mille, Q. Peyrot, L. Berteloot, N. Boddaert, S. Sarnacki, and I. Bloch. "Segmentation of Pelvic Vessels in Pediatric MRI Using a Patch-Based Deep Learning Approach." In Data Driven Treatment Response Assessment and Preterm, Perinatal, and Paediatric Image Analysis, 97–106. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00807-9_10.

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Kleinfeld, David, Beth Friedman, Patrick D. Lyden, and Andy Y. Shih. "Targeted Occlusion to Surface and Deep Vessels in Neocortex via Linear and Nonlinear Optical Absorption." In Springer Protocols Handbooks, 169–85. Totowa, NJ: Humana Press, 2009. http://dx.doi.org/10.1007/978-1-60327-185-1_14.

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Тези доповідей конференцій з теми "Deep vessels"

Spencer, R., and R. F. Spencer. "Assessment of Station Keeping Capability of Dynamically Positioned Vessels." In Development In Deep Waters. RINA, 1986. http://dx.doi.org/10.3940/rina.ddw.1986.16.

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Wu, Xiong-Jian, and W. G. Price. "The Behaviour of Shallow Draft Offshore Structures and Service Vessels in Deeper Water." In Development In Deep Waters. RINA, 1986. http://dx.doi.org/10.3940/rina.ddw.1986.17.

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McKie, Nigel R., Daniel T. Peters, and Keegan A. Tooley. "Deep Well Drilling Applications." In ASME 2013 Pressure Vessels and Piping Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/pvp2013-97053.

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The majority of oilfield Wellhead and Tree equipment has been designed with guidance from codes API 6A and 17D. However, their design methods are not the most appropriate for the new High Pressure High Temperature (HPHT) applications; equipment rated above 15 ksi (103 MPa) Working Pressure and/or above 350 °F (177 °C). This paper discusses the limitations of established design methods and presents more suitable methods for HPHT applications. FEA is well established as a stress analysis method for use in conventional Pressure Vessel design; however it is not so well established for load bearing interfaces. This leaves a gap in our Design Methods, since load bearing interfaces are intrinsic to Wellhead Equipment Pressure Vessel design. Intrinsic because many of our Pressure Vessels are “capped” by hangers and connectors instead of flanges; if a hanger Load Shoulder fails then the Pressure Vessel above it has failed. Unique to the oilfield are infrequent but extremely high loads. These loads are much higher than the Working Condition and in most cases they stem from field testing and emergency situations. If the established ASME methods are used for these cases certain projects may not be viable.

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Solheim, Astrid V., Per Olaf Brett, Jose Jorge Garcia Agis, Stein Ove Erikstad, and Bjørn Egil Asbjørnslett. "Technology Transfer in Novel Ship Design: A Deep Seabed Mining Study." In SNAME 14th International Marine Design Conference. SNAME, 2022. http://dx.doi.org/10.5957/imdc-2022-240.

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Designing new ships for new purposes, in this case deep seabed mining, without using proper selected reference vessels, if available, is challenging. In this paper, we show that the vessel features of a future deep seabed mining vessel have many similarities to offshore vessels in the deep-sea offshore oil and gas industry and can be used as such. We evaluate and discuss the technical, operational, and commercial performance of three possible vessel design solutions developed based on such a case ship.

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Hoen, Christopher. "Riser Response Based Optimal Positioning of Deep-Water Vessels." In ASME 2005 24th International Conference on Offshore Mechanics and Arctic Engineering. ASMEDC, 2005. http://dx.doi.org/10.1115/omae2005-67013.

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The present paper discusses the mathematical modeling of risers and riser-like structures applied in a positioning context for deep-water floating vessels. The main purpose of the paper is to show that an estimate for the optimal vessel position, sufficient for most practical applications, is obtained from measurements of the riser inclinations or related parameters at lower end, and optionally upper end, through a solution based on the variably tensioned beam differential equation. Due to the ease of implementation this solution is well suited for direct application in on-line riser monitoring systems. The method is an attractive alternative to on-line FE-analyses, application of pre-computed regression curves based on idealized loading or black-box neural networks, which has been proposed by others to be applied as basis for interpretation of the measured riser responses. The basic idea behind the method is based on the observation that the riser inclinations or stress-joint moments at upper and lower end have mainly two causes. Firstly an effect caused by the position of the riser top end relative to the wellhead due to permanent vessel offset and slow drift vessel motions, and secondly the effects of transverse current down the riser. The general theory behind the method will be outlined. It will then be shown how the method adapts to drilling-risers with flex-joints, risers with stress-joints and also to the special case of well intervention with coiled tubing in open sea without applying a work-over or marine riser. The performance of the method is illustrated using simulated vessel and riser dynamic response data. The simulations are performed for selected vessel types both for deep-water and shallower waters applying state-of-the-art software for simulation of the riser and vessel dynamic response in random sea states.

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Wu, Cong, YanLong Liu, and YiXuan Zou. "Preliminary Study on Deep-learning for Retinal Vessels Segmentation." In 2020 15th International Conference on Computer Science & Education (ICCSE). IEEE, 2020. http://dx.doi.org/10.1109/iccse49874.2020.9201832.

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Liu, Shuai, Xuan Huang, Zhipeng Feng, Xiaozhou Jiang, Bihao Wang, and Wanjun Wu. "Research on Numerical Calculation Model of Impact Load on Reef in Deep Sea." In ASME 2021 Pressure Vessels & Piping Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/pvp2021-62110.

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Abstract In the deep sea, one of the main threats to the structural safety of ships is the impact load caused by reef. This paper established a numerical calculation model which is suitable for simulating the impact load on reef in the deep sea. The model considers the coupling effects of large deformation, fluid-structure interaction, and material plastic deformation. The mesh size, material constitutive model and fluid structure coupling method are studied. The explicit dynamic analysis method is verified by the AISI experiment which there is little difference between the numerical simulation results and the experimental results. Based on the method, the model considering the effect of fluid-structure interaction (FSI) is established and the effectiveness of the FSI is verified by the theoretical results of the national military standard. Then, fluid structure interaction algorithm is studied. Finally, the model which is suitable for simulating the collision between ship and reef in deep sea is established. The effect of reef size and shape on the impact load was studied. It can be seen that the shape and size of the reef mainly affect the contact area and contact stiffness and have a great impact on the impact load. It can provide load input for the structural design of ship and ship equipments. In addition, the model has strong applicability and can be used to calculate the impact load in deep sea. However, the impact load caused by reef also depends on the material property of the ship and reefs condition of the navigation area.

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Iwamatsu, Fuminori, Katsumasa Miyazaki, Hajime Miyata, and Hideki Yuya. "Application of Stress Intensity Factors for Deep Surface Cracks to Crack Growth Evaluation." In ASME 2013 Pressure Vessels and Piping Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/pvp2013-97465.

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Flaw evaluation for nuclear power plants is conducted on the basis of a fitness-for-service code. For instance, the ASME Boiler and Pressure Vessel Code Section XI (ASME Section XI) and the JSME Rules on Fitness-for-Service for Nuclear Power Plants (JSME Code) prescribe a flaw evaluation procedure. In flaw evaluation, an aspect ratio of a detected surface crack is defined by a/l, where a is the crack depth and l is the crack length, and the aspect ratio a/l does not exceed 0.5. Therefore, a deep surface crack, which has an aspect ratio a/l greater than 0.5, is characterized as a semicircle with l = 2a. Meanwhile, deep surface cracks caused by stress corrosion cracking (SCC) have been detected in the Ni based alloy weld metal. Since the limit of the flaw characterization rule which is an aspect ratio a/l ≤ 0.5 seems to conduct to a conservative evaluation result for a deep surface crack, more rational and applicable flaw evaluation is required in order to eliminate surplus conservatism. In this study, a flaw evaluation procedure based on ASME Section XI or JSME Code is extended to deal with a deep surface crack. To evaluate crack growth behavior for a deep surface crack, coefficients to calculate stress intensity factors were evaluated by finite element analysis (FEA) and shown in tabular form on the basis of equations prescribed in ASME Section XI and JSME Code. To verify the applicability of proposed coefficients to crack growth evaluation, SCC crack growth behavior for a deep initial crack was evaluated by coefficients applied to the ASME Section XI procedure and a detailed FEA method. Applicability of coefficients to crack growth evaluation was verified through comparisons of crack growth behaviors for deep surface crack under various stress fields.

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Zheng, Gang, Sayeed Hossain, Feng Shen, and Chris Truman. "Analysis and Optimization of the Deep-Hole Drilling Technique in Measuring Complex Residual Stress." In ASME 2017 Pressure Vessels and Piping Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/pvp2017-65165.

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The aim of the present study was to utilize a complex residual stress generated within a welded circular disc to further investigate the standard deep-hole drilling (DHD) technique and the newly developed over-coring deep-hole drilling (oDHD) technique in accurately measuring residual stresses well over yield stress. Finite Element Analysis (FEA) was used to optimize and extend the deep-hole drilling technique and improve its accuracy. The standard DHD procedure involves 4 steps. (1) A reference hole is gun-drilled through the component. (2) The internal diameter of the reference hole is measured at different angular positions through the depth of the component. (3) A cylindrical section with the reference hole as its longitudinal axis is trepanned free from the component. (4) Finally, the relaxed internal diameter is re-measured at the same angular positions and the same depths. The drilling, trepanning procedures and the parameters of the deep-hole drilling technique were all studied in detail to optimize the technique. Comparison is made between the FEA predicted residual stress in the weld, the measurements and the reconstructed residual stresses of the measurements. The close correlations confirmed the suitability of new modifications made in the deep-hole drilling technique to account for plasticity when measuring near yield residual stresses present in a component.

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AFFANE, Abir, Marie-Ange LEBRE, Utkarsh MITTAL, and Antoine VACAVANT. "Literature Review of Deep Learning Models for Liver Vessels Reconstruction." In 2020 Tenth International Conference on Image Processing Theory, Tools and Applications (IPTA). IEEE, 2020. http://dx.doi.org/10.1109/ipta50016.2020.9286639.

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Звіти організацій з теми "Deep vessels"

Malej, Matt, and Fengyan Shi. Suppressing the pressure-source instability in modeling deep-draft vessels with low under-keel clearance in FUNWAVE-TVD. Engineer Research and Development Center (U.S.), May 2021. http://dx.doi.org/10.21079/11681/40639.

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This Coastal and Hydraulics Engineering Technical Note (CHETN) documents the development through verification and validation of three instability-suppressing mechanisms in FUNWAVE-TVD, a Boussinesq-type numerical wave model, when modeling deep-draft vessels with a low under-keel clearance (UKC). Many large commercial ports and channels (e.g., Houston Ship Channel, Galveston, US Army Corps of Engineers [USACE]) are traveled and affected by tens of thousands of commercial vessel passages per year. In a series of recent projects undertaken for the Galveston District (USACE), it was discovered that when deep-draft vessels are modeled using pressure-source mechanisms, they can suffer from model instabilities when low UKC is employed (e.g., vessel draft of 12 m¹ in a channel of 15 m or less of depth), rendering a simulation unstable and obsolete. As an increasingly large number of deep-draft vessels are put into service, this problem is becoming more severe. This presents an operational challenge when modeling large container-type vessels in busy shipping channels, as these often will come as close as 1 m to the bottom of the channel, or even touch the bottom. This behavior would subsequently exhibit a numerical discontinuity in a given model and could severely limit the sample size of modeled vessels. This CHETN outlines a robust approach to suppressing such instability without compromising the integrity of the far-field vessel wave/wake solution. The three methods developed in this study aim to suppress high-frequency spikes generated nearfield of a vessel. They are a shock-capturing method, a friction method, and a viscosity method, respectively. The tests show that the combined shock-capturing and friction method is the most effective method to suppress the local high-frequency noises, while not affecting the far-field solution. A strong test, in which the target draft is larger than the channel depth, shows that there are no high-frequency noises generated in the case of ship squat as long as the shock-capturing method is used.

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Kruse, C., Dong Hun Kang, Kenneth Mitchell, Patricia DiJoseph, and Marin Kress. Freight fluidity for the Port of Baltimore : vessel approach and maritime mobility metrics. Engineer Research and Development Center (U.S.), January 2022. http://dx.doi.org/10.21079/11681/43000.

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The United States Army Corps of Engineers is tasked with maintaining waterborne transportation system elements. Understanding channel utilization by vessels informs decisions regarding operations, maintenance, and investments in those elements. Historically, investment decisions have been informed by safety, environmental considerations, and projected economic benefits of alleviating channel restrictions or shipping delays (usually derived from models). However, quantifying causes and impacts of shipping delays based on actual historical vessel location data and then identifying which causes could be ameliorated through investment has been out of reach until recently. In this study, Automatic Identification System vessel position reports were used to develop quantitative measures of transit and dwell-time reliabilities for commercial vessels calling at the Port of Baltimore, Maryland. This port has two deep-water approaches: Chesapeake Bay and the Chesapeake and Delaware Canal. Descriptive metrics were determined for each approach, including port cycle time, harbor stay hours, travel time inbound, and travel time outbound. Then, additional performance measures were calculated: baseline travel time, travel time index, and planning time index. The key finding of this study is that the majority of variability in port cycle time is due to the variability in harbor stay hours, not from channel conditions or channel restrictions.

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Messer, Walker L., Todd A. Nettles, Alicia Sellers, and Ryan M. Stoner. Improving container shipment analysis. U.S. Army Engineer Research and Development Center, May 2022. http://dx.doi.org/10.21079/11681/44380.

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US Army Corps of Engineers (USACE) deep-draft navigation economic analyses use assumptions about the sensitivity of vessel operations to channel modification to estimate national economic development benefits. The complexity and proprietary nature of carrier deployment decisions and loading practices adds uncertainty to USACE navigation studies. This report attempts to provide an overview of containership deployment and loading practices as it relates to USACE navigation studies to improve the quality of deep-draft economics. The report relies on trade data, vessel order books, and carrier interviews to study the impact of channel modification on vessel loading and deployment. The report makes recommendations for developing deployment and loading inputs for future economic evaluations.

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Kwasnitschka, Tom. Open-Water Test of the LIGHTHOUSE Situational Awareness System, Cruise No. AL555, 28.4.21 – 11.5.21, Kiel (Germany) – Kiel (Germany) LIGHTHOUSE-DM, Alkor-Berichte AL555. GEOMAR Helmholtz Centre for Ocean Research Kiel, 2021. http://dx.doi.org/10.3289/cr_al555.

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The objective of this cruise was to establish the first order functionality of the LIGHTHOUSE system in terms of mechanics, ROV operations, electrical components and data link. This is a highly integrated suite of optical and acoustic sensors to create a real time 360° scan of an underwater environment, in order to enhance the situational awareness of pilots and mission specialists. The tests were to be conducted in the clear, deep waters of the Norwegian Sognefjord. Due to severe technical malfunctions that became only apparent during mobilization in the port of Kiel, the vessel stayed moored to the east shore and west shore quays in Kiel Harbor, except for an excursion off Boknis Eck in the Eckernförde Bight. Despite these restrictions in locality, the majority of the work programme was carried out with great success.

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McAlpin, Jennifer N., and Cassandra G. Ross. Houston Ship Channel Expansion Channel Improvement Project (ECIP) Numerical Modeling Report : Increased Channel Width Analysis. Engineer Research and Development Center (U.S.), February 2021. http://dx.doi.org/10.21079/11681/39739.

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The Houston Ship Channel is one of the busiest deep -draft navigation channels in the United States and must be able to accommodate larger vessel dimensions over time. The U.S. Army Engineer District, Galveston (SWG) requested the U.S. Army Engineer Research and Development Center, Coastal and Hydraulics Laboratory perform hydrodynamic and sediment modeling of proposed modifications along the Houston Ship Channel. The modeling results are necessary to provide data for salinity and sediment transport analysis a s well as ship simulation studies. SWG provided a project alternative that includes channel widening, deepening, and bend easing. After initial analysis, two additional channel widths in the bay portion of the Houston Ship Channel were requested for testing. The results of these additional channel widths are presented in this report. The model shows that the salinity does not vary significantly due to the channel modifications being considered for this project. Changes in salinity are 2 parts per thousand or less. The tidal prism increases by less than 2% when the project is included, and the tidal amplitudes increase by no more than 0.01 meter. The residual velocity vectors do vary in and around areas where project modifications are made.

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McAlpin, Jennifer, and Cassandra Ross. Houston Ship Channel Expansion Channel Improvement Project (ECIP) numerical modeling report : BABUS cell and Bird Island analysis. Engineer Research and Development Center (U.S.), August 2021. http://dx.doi.org/10.21079/11681/41581.

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The Houston Ship Channel (HSC) is one of the busiest deep-draft navigation channels in the United States and must be able to accommodate increasing vessel sizes. The US Army Engineer District, Galveston (SWG), requested the Engineer Research and Development Center, Coastal and Hydraulics Laboratory, perform hydrodynamic and sediment modeling of proposed modifications in Galveston and Trinity Bays and along the HSC. The modeling results are necessary to provide data for hydrodynamic, salinity, and sediment transport analysis. SWG provided three project alternatives that include closing Rollover Pass, Bay Aquatic Beneficial Use System cells, Bird Islands, and HSC modifications. These alternatives and a Base (existing condition) will be simulated for present (2029) and future (2079) conditions. The results of these alternatives/conditions as compared to the Base are presented in this report. The model shows that the mean salinity varies by 2–3 ppt due to the HSC channel modifications and by approximately 5 ppt in the area of East Bay due to the closure of Rollover Pass. The tidal prism increases by 2.5% to 5% in the alternatives. The tidal amplitudes change by less than 0.01 m. The residual velocity vectors vary in and around areas where project modifications are made.

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