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Thayammal, S., and D. Selvathi. "A Review On Segmentation Based I mage Compression Techniques." Journal of Engineering Science and Technology Review 6, no. 3 (June 2013): 134–40. http://dx.doi.org/10.25103/jestr.063.24.
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Wang, Hong Ying, and Er Bao Peng. "Research and Design of Walking Mechanism of Robot and the Parameterization Modeling Based on the UG." Applied Mechanics and Materials 454 (October 2013): 82–85. http://dx.doi.org/10.4028/www.scientific.net/amm.454.82.
Повний текст джерелаАнотація:
The paper introduced image processing technology based on image segmentation about on-line threads images, and describes in detail image processing technology from mage preprocessing, image gmentation, and threaded parameter test. Threaded images of on-line processing parts obtained are introduced as the key technology, Target edge extraction process from the segmented image are also recounted. At last, this article shows a comparison between actual machining parameters of screw thread and the standard parameter , provides the criterion for error compensation.
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Godoy, Dalva Maria Alves, and Hugo Cogo-Moreira. "Evidences of Factorial Structure and Precision of Phonemic Awareness Tasks (TCFe)." Paidéia (Ribeirão Preto) 25, no. 62 (December 2015): 363–72. http://dx.doi.org/10.1590/1982-43272562201510.
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AbstractTo assess phonological awareness - a decisive skill for learning to read and write - it is necessary to provide evidence about an instrument construct to present trustworthy parameters for both empirical research and the development of educational intervention and rehabilitation programs. In Brazil, at this moment, there are no studies regarding the internal structure for tests of phonological awareness. This article shows the factorial validity of a test of phonological awareness composed by three sub-tests: two tasks of subtraction of initial phoneme and one of phonemic segmentation. The multidimensional confirmatory factorial analysis was applied to a sample of 176 Brazilian students ( Mage= 9.3 years) from the first to fifth grade of elementary school. Results indicated a well-adjusted model, with items of intermediate difficulty and high factor loadings; thus, this corroboratedthe internal structure and well-designed theoretical conception.
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Wan, Guo Chun, Meng Meng Li, He Xu, Wen Hao Kang, Jin Wen Rui, and Mei Song Tong. "XFinger-Net: Pixel-Wise Segmentation Method for Partially Defective Fingerprint Based on Attention Gates and U-Net." Sensors 20, no. 16 (August 10, 2020): 4473. http://dx.doi.org/10.3390/s20164473.
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Partially defective fingerprint image (PDFI) with poor performance poses challenges to the automated fingerprint identification system (AFIS). To improve the quality and the performance rate of PDFI, it is essential to use accurate segmentation. Currently, most fingerprint image segmentations use methods with ridge orientation, ridge frequency, coherence, variance, local gradient, etc. This paper proposes a method of XFinger-Net for segmenting PDFIs. Based on U-Net, XFinger-Net inherits its characteristics. The attention gate with fewer parameters is used to replace the cascaded network, which can suppress uncorrelated regions of PDFIs. Moreover, the XFinger-Net implements a pixel-level segmentation and takes non-blocking fingerprint images as an input to preserve the global characteristics of PDFIs. The XFinger-Net can achieve a very good segmentation effect as demonstrated in the self-made fingerprint segmentation test.
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Campbell, N. W., B. T. Thomas, and T. Troscianko. "Automatic Segmentation and Classification of Outdoor Images Using Neural Networks." International Journal of Neural Systems 08, no. 01 (February 1997): 137–44. http://dx.doi.org/10.1142/s0129065797000161.
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The paper describes how neural networks may be used to segment and label objects in images. A self-organising feature map is used for the segmentation phase, and we quantify the quality of the segmentations produced as well as the contribution made by colour and texture features. A multi-layer perceptron is trained to label the regions produced by the segmentation process. It is shown that 91.1% of the image area is correctly classified into one of eleven categories which include cars, houses, fences, roads, vegetation and sky.
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Iyer, Aditi, Maria Thor, Ifeanyirochukwu Onochie, Jennifer Hesse, Kaveh Zakeri, Eve LoCastro, Jue Jiang, et al. "Prospectively-validated deep learning model for segmenting swallowing and chewing structures in CT." Physics in Medicine & Biology 67, no. 2 (January 17, 2022): 024001. http://dx.doi.org/10.1088/1361-6560/ac4000.
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Abstract Objective. Delineating swallowing and chewing structures aids in radiotherapy (RT) treatment planning to limit dysphagia, trismus, and speech dysfunction. We aim to develop an accurate and efficient method to automate this process. Approach. CT scans of 242 head and neck (H&N) cancer patients acquired from 2004 to 2009 at our institution were used to develop auto-segmentation models for the masseters, medial pterygoids, larynx, and pharyngeal constrictor muscle using DeepLabV3+. A cascaded framework was used, wherein models were trained sequentially to spatially constrain each structure group based on prior segmentations. Additionally, an ensemble of models, combining contextual information from axial, coronal, and sagittal views was used to improve segmentation accuracy. Prospective evaluation was conducted by measuring the amount of manual editing required in 91 H&N CT scans acquired February-May 2021. Main results. Medians and inter-quartile ranges of Dice similarity coefficients (DSC) computed on the retrospective testing set (N = 24) were 0.87 (0.85–0.89) for the masseters, 0.80 (0.79–0.81) for the medial pterygoids, 0.81 (0.79–0.84) for the larynx, and 0.69 (0.67–0.71) for the constrictor. Auto-segmentations, when compared to two sets of manual segmentations in 10 randomly selected scans, showed better agreement (DSC) with each observer than inter-observer DSC. Prospective analysis showed most manual modifications needed for clinical use were minor, suggesting auto-contouring could increase clinical efficiency. Trained segmentation models are available for research use upon request via https://github.com/cerr/CERR/wiki/Auto-Segmentation-models. Significance. We developed deep learning-based auto-segmentation models for swallowing and chewing structures in CT and demonstrated its potential for use in treatment planning to limit complications post-RT. To the best of our knowledge, this is the only prospectively-validated deep learning-based model for segmenting chewing and swallowing structures in CT. Segmentation models have been made open-source to facilitate reproducibility and multi-institutional research.
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Weishaupt, L. L., T. Vuong, A. Thibodeau-Antonacci, A. Garant, K. S. Singh, C. Miller, A. Martin, and S. Enger. "A121 QUANTIFYING INTER-OBSERVER VARIABILITY IN THE SEGMENTATION OF RECTAL TUMORS IN ENDOSCOPY IMAGES AND ITS EFFECTS ON DEEP LEARNING." Journal of the Canadian Association of Gastroenterology 5, Supplement_1 (February 21, 2022): 140–42. http://dx.doi.org/10.1093/jcag/gwab049.120.
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Abstract Background Tumor delineation in endoscopy images is a crucial part of clinical diagnoses and treatment planning for rectal cancer patients. However, it is challenging to detect and adequately determine the size of tumors in these images, especially for inexperienced clinicians. This motivates the need for a standardized, automated segmentation method. While deep learning has proven to be a powerful tool for medical image segmentation, it requires a large quantity of high-quality annotated training data. Since the annotation of endoscopy images is prone to high inter-observer variability, creating a robust unbiased deep learning model for this task is challenging. Aims To quantify the inter-observer variability in the manual segmentation of tumors in endoscopy images of rectal cancer patients and investigate an automated approach using deep learning. Methods Three gastrointestinal physicians and radiation oncologists (G1, G2, and G3) segmented 2833 endoscopy images into tumor and non-tumor regions. The whole image classifications and the pixelwise classifications into tumor and non-tumor were compared to quantify the inter-observer variability. Each manual annotator is from a different institution. Three different deep learning architectures (FCN32, U-Net, and SegNet) were trained on the binary contours created by G2. This naive approach investigates the effectiveness of neglecting any information about the uncertainty associated with the task of tumor delineation. Finally, segmentations from G2 and the deep learning models’ predictions were compared against ground truth labels from G1 and G3, and accuracy, sensitivity, specificity, precision, and F1 scores were computed for images where both segmentations contained tumors. Results The deep-learning segmentation took less than 1 second, while manual segmentation took approximately 10 seconds per image. There was significant inter-observer variability for the whole-image classifications made by the manual annotators (Figure 1A). The segmentation scores achieved by the deep learning models (SegNet F1:0.80±0.08) were comparable to the inter-observer variability for the pixel-wise image classification (Figure 1B). Conclusions The large inter-observer variability observed in this study indicates a need for an automated segmentation tool for tumors in endoscopy images of rectal cancer patients. While deep learning models trained on a single observer’s labels can segment tumors with an accuracy similar to the inter-observer variability, these models do not accurately reflect the intrinsic uncertainty associated with tumor delineation. In our ongoing studies, we investigate training a model with all observers’ contours to reflect the uncertainty associated with the tumor segmentations. Funding Agencies CIHRNSERC
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Desser, Dmitriy, Francisca Assunção, Xiaoguang Yan, Victor Alves, Henrique M. Fernandes, and Thomas Hummel. "Automatic Segmentation of the Olfactory Bulb." Brain Sciences 11, no. 9 (August 28, 2021): 1141. http://dx.doi.org/10.3390/brainsci11091141.
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The olfactory bulb (OB) has an essential role in the human olfactory pathway. A change in olfactory function is associated with a change of OB volume. It has been shown to predict the prognosis of olfactory loss and its volume is a biomarker for various neurodegenerative diseases, such as Alzheimer’s disease. Thus far, obtaining an OB volume for research purposes has been performed by manual segmentation alone; a very time-consuming and highly rater-biased process. As such, this process dramatically reduces the ability to produce fair and reliable comparisons between studies, as well as the processing of large datasets. Our study aims to solve this by proposing a novel methodological framework for the unbiased measurement of OB volume. In this paper, we present a fully automated tool that successfully performs such a task, accurately and quickly. In order to develop a stable and versatile algorithm and to train the neural network, we used four datasets consisting of whole-brain T1 and high-resolution T2 MRI scans, as well as the corresponding clinical information of the subject’s smelling ability. One dataset contained data of patients suffering from anosmia or hyposmia (N = 79), and the other three datasets contained data of healthy controls (N = 91). First, the manual segmentation labels of the OBs were created by two experienced raters, independently and blinded. The algorithm consisted of the following four different steps: (1) multimodal data co-registration of whole-brain T1 images and T2 images, (2) template-based localization of OBs, (3) bounding box construction, and lastly, (4) segmentation of the OB using a 3D-U-Net. The results from the automated segmentation algorithm were tested on previously unseen data, achieving a mean dice coefficient (DC) of 0.77 ± 0.05, which is remarkably convergent with the inter-rater DC of 0.79 ± 0.08 estimated for the same cohort. Additionally, the symmetric surface distance (ASSD) was 0.43 ± 0.10. Furthermore, the segmentations produced using our algorithm were manually rated by an independent blinded rater and have reached an equivalent rating score of 5.95 ± 0.87 compared to a rating score of 6.23 ± 0.87 for the first rater’s segmentation and 5.92 ± 0.81 for the second rater’s manual segmentation. Taken together, these results support the success of our tool in producing automatic fast (3–5 min per subject) and reliable segmentations of the OB, with virtually matching accuracy with the current gold standard technique for OB segmentation. In conclusion, we present a newly developed ready-to-use tool that can perform the segmentation of OBs based on multimodal data consisting of T1 whole-brain images and T2 coronal high-resolution images. The accuracy of the segmentations predicted by the algorithm matches the manual segmentations made by two well-experienced raters. This method holds potential for immediate implementation in clinical practice. Furthermore, its ability to perform quick and accurate processing of large datasets may provide a valuable contribution to advancing our knowledge of the olfactory system, in health and disease. Specifically, our framework may integrate the use of olfactory bulb volume (OBV) measurements for the diagnosis and treatment of olfactory loss and improve the prognosis and treatment options of olfactory dysfunctions.
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LESTARI, LUXI IKA, and SAINO SAINO. "Analisis Segmentasi Psikografis dan Sensitivitas Harga Konsumen Rumah Makan di Kabupaten Sidoarjo." BISMA (Bisnis dan Manajemen) 3, no. 1 (June 6, 2018): 15. http://dx.doi.org/10.26740/bisma.v3n1.p15-33.
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Now, eat not just tool to fill up a stomach. Eat have been become lifestyle together with period development and culture hase been made of human. So, restaurant necessary to do exactly segmentatiom to develop marketing strategies more exact and specific for their product target on segmen that more specific of poppulation. Psychographic segmentation is kind of segmentation that intercorrelated with individual consumer’s mind, by exploring such factors as value, lifestyle, and cognitive component (Lowe and Worsley,2002) In this research psycographi segmentation just use value that have 18 indicators and lifestyle that have 6 indicators (Lowe and Worsley,2002). Technique that use to taken sample is non probability sampling. To make taken saple easier, researcher use intidental sampling. Data processing technique use validitas and reliabilitas while statistic analysis use factor analysis, cluster analysis, and ANOVA analysis. Psychographic segmentation that connected with price sensitivity have 4 segment are kekanak-kanakan (1225%), alpha sosializer (23,52%), konservatif (14,21%), optimiser (26,47%), self dominance (9,80%), statis (13,72%). There is found price sensitvity different on each segment where segment stick out, alpha sosializer segment is most sensitive, just the opposite segment that have low price sensitivity are optimiser segment and self domonance segment.
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Colebank, Mitchel J., L. Mihaela Paun, M. Umar Qureshi, Naomi Chesler, Dirk Husmeier, Mette S. Olufsen, and Laura Ellwein Fix. "Influence of image segmentation on one-dimensional fluid dynamics predictions in the mouse pulmonary arteries." Journal of The Royal Society Interface 16, no. 159 (October 2, 2019): 20190284. http://dx.doi.org/10.1098/rsif.2019.0284.
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Computational fluid dynamics (CFD) models are emerging tools for assisting in diagnostic assessment of cardiovascular disease. Recent advances in image segmentation have made subject-specific modelling of the cardiovascular system a feasible task, which is particularly important in the case of pulmonary hypertension, requiring a combination of invasive and non-invasive procedures for diagnosis. Uncertainty in image segmentation propagates to CFD model predictions, making the quantification of segmentation-induced uncertainty crucial for subject-specific models. This study quantifies the variability of one-dimensional CFD predictions by propagating the uncertainty of network geometry and connectivity to blood pressure and flow predictions. We analyse multiple segmentations of a single, excised mouse lung using different pre-segmentation parameters. A custom algorithm extracts vessel length, vessel radii and network connectivity for each segmented pulmonary network. Probability density functions are computed for vessel radius and length and then sampled to propagate uncertainties to haemodynamic predictions in a fixed network. In addition, we compute the uncertainty of model predictions to changes in network size and connectivity. Results show that variation in network connectivity is a larger contributor to haemodynamic uncertainty than vessel radius and length.
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Jin, Felix Q., Anna E. Knight, Adela R. Cardones, Kathryn R. Nightingale, and Mark L. Palmeri. "Semi-automated weak annotation for deep neural network skin thickness measurement." Ultrasonic Imaging 43, no. 4 (May 11, 2021): 167–74. http://dx.doi.org/10.1177/01617346211014138.
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Correctly calculating skin stiffness with ultrasound shear wave elastography techniques requires an accurate measurement of skin thickness. We developed and compared two algorithms, a thresholding method and a deep learning method, to measure skin thickness on ultrasound images. Here, we also present a framework for weakly annotating an unlabeled dataset in a time-effective manner to train the deep neural network. Segmentation labels for training were proposed using the thresholding method and validated with visual inspection by a human expert reader. We reduced decision ambiguity by only inspecting segmentations at the center A-line. This weak annotation approach facilitated validation of over 1000 segmentation labels in 2 hours. A lightweight deep neural network that segments entire 2D images was designed and trained on this weakly-labeled dataset. Averaged over six folds of cross-validation, segmentation accuracy was 57% for the thresholding method and 78% for the neural network. In particular, the network was better at finding the distal skin margin, which is the primary challenge for skin segmentation. Both algorithms have been made publicly available to aid future applications in skin characterization and elastography.
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Kouti, Angelica, Doerthe Ziegelitz, Tobias Hallén, Thomas Skoglund, Dan Farahmand, and Karl Lindberg. "Three-Dimensional Volumetric Segmentation of Pituitary Tumors: Assessment of Inter-rater Agreement and Comparison with Conventional Geometric Equations." Journal of Neurological Surgery Part B: Skull Base 79, no. 05 (January 19, 2018): 475–81. http://dx.doi.org/10.1055/s-0037-1618577.
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Background The assessment of pituitary tumor (PT) volume is important in the treatment and follow-up of patients with PT. Previously, PT volume estimation has been performed by conventional geometric equations (CGE) such as abc/2 (simplified ellipsoid volume equation) and 4πr3/3 (sphere), both presuming a symmetric tumor shape, which occurs uncommonly in patients with PT. In contrast, three-dimensional (3D) voxel-based software segmentation takes the irregular and asymmetric shapes that PTs often possess into account and might be a more accurate method for PT volume segmentation.The purpose of this study is twofold. (1) To compare 3D segmentation with CGE for PT volume estimation. (2) To assess inter-rater reliability in 3D segmentation of PTs. Methods Nineteen high-resolution (1mm slice thickness) T1-weighted MRI examinations of patients with PT were independently analyzed and manually segmented, using the software ITK-SNAP, by two certified neuroradiologists. Concurrently, the volumes of the PTs were estimated with abc/2 and 4πr3/3 by a clinician, and the results were compared with the corresponding segmented volumes. Results There was a significant decrease in PT volume attained from the segmentations compared with the calculations made with abc/2 (p < 0.001, mean volume 18% higher than segmentation) and 4πr3/3 (p < 0.001, mean volume 28% higher than segmentation). The intraclass correlation coefficient (ICC) for the two sets of segmented PTs was 0.99. Conclusion CGE (abc/2 and 4πr3/3) significantly overestimates PT volume compared with 3D volumetric segmentation. The inter-rater agreement on manual 3D volumetric software segmentation is excellent.
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Avola, Danilo, and Daniele Pannone. "MAGI: Multistream Aerial Segmentation of Ground Images with Small-Scale Drones." Drones 5, no. 4 (October 4, 2021): 111. http://dx.doi.org/10.3390/drones5040111.
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In recent years, small-scale drones have been used in heterogeneous tasks, such as border control, precision agriculture, and search and rescue. This is mainly due to their small size that allows for easy deployment, their low cost, and their increasing computing capability. The latter aspect allows for researchers and industries to develop complex machine- and deep-learning algorithms for several challenging tasks, such as object classification, object detection, and segmentation. Focusing on segmentation, this paper proposes a novel deep-learning model for semantic segmentation. The model follows a fully convolutional multistream approach to perform segmentation on different image scales. Several streams perform convolutions by exploiting kernels of different sizes, making segmentation tasks robust to flight altitude changes. Extensive experiments were performed on the UAV Mosaicking and Change Detection (UMCD) dataset, highlighting the effectiveness of the proposed method.
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Li, Chenshuang, Leanne Lin, Zhong Zheng, and Chun-Hsi Chung. "A User-Friendly Protocol for Mandibular Segmentation of CBCT Images for Superimposition and Internal Structure Analysis." Journal of Clinical Medicine 10, no. 1 (January 1, 2021): 127. http://dx.doi.org/10.3390/jcm10010127.
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Background: Since cone-beam computed tomography (CBCT) technology has been widely adopted in orthodontics, multiple attempts have been made to devise techniques for mandibular segmentation and 3D superimposition. Unfortunately, as the software utilized in these methods are not specifically designed for orthodontics, complex procedures are often necessary to analyze each case. Thus, this study aimed to establish an orthodontist-friendly protocol for segmenting the mandible from CBCT images that maintains access to the internal anatomic structures. Methods: The “sculpting tool” in the Dolphin 3D Imaging software was used for segmentation. The segmented mandible images were saved as STL files for volume matching in the 3D Slicer to validate the repeatability of the current protocol and were exported as DICOM files for internal structure analysis and voxel-based superimposition. Results: The mandibles of all tested CBCT datasets were successfully segmented. The volume matching analysis showed high consistency between two independent segmentations for each mandible. The intraclass correlation coefficient (ICC) analysis on 20 additional CBCT mandibular segmentations further demonstrated the high consistency of the current protocol. Moreover, all of the anatomical structures for superimposition identified by the American Board of Orthodontics were found in the voxel-based superimposition, demonstrating the ability to conduct precise internal structure analyses with the segmented images. Conclusion: An efficient and precise protocol to segment the mandible while retaining access to the internal structures was developed on the basis of CBCT images.
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Gustav Johannsen, Carl. "Understanding users: from man-made typologies to computer-generated clusters." New Library World 115, no. 9/10 (October 7, 2014): 412–25. http://dx.doi.org/10.1108/nlw-05-2014-0052.
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Purpose – The aim of this article is to identify the main approaches and discuss their perspectives, including their strengths and weaknesses in, especially, public library contexts. The purpose is also to present and discuss the results of a recent – 2014 – Danish library user segmentation project using computer-generated clusters. Compared to traditional marketing texts, this article also tries to identify users segments or images created by the library profession itself. Segmentation of users can help libraries in the process of understanding user similarities and differences. Segmentation can also form the basis for selecting segments as target users and for developing tailored services for specific target segments. Thus, several approaches and techniques have been tested in library contexts. Design/methodology/approach – Basically, the article is built upon a literature review concerning different approaches to user segmentation in, especially, public library context from approximately 1980 till now (May 2014). Findings – The article reveals that – at least – five different principal approaches to user segmentation have been applied by the library sector during the past 30-35 years. Characteristics, strengths and weaknesses of the different approaches are identified, discussed and evaluated. Practical implications – When making decisions on future library user surveys, it is certainly an advantage, concerning the ability to make qualified decision, to know what opportunities that are at hand for identifying important segments. Originality/value – Some of the approaches have been treated individually in the library literature; however, it is probably the first time that the professions own user images and metaphors are dealt with in a user segmentation context.
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Müller-Franzes, Gustav, Sven Nebelung, Justus Schock, Christoph Haarburger, Firas Khader, Federico Pedersoli, Maximilian Schulze-Hagen, Christiane Kuhl, and Daniel Truhn. "Reliability as a Precondition for Trust—Segmentation Reliability Analysis of Radiomic Features Improves Survival Prediction." Diagnostics 12, no. 2 (January 19, 2022): 247. http://dx.doi.org/10.3390/diagnostics12020247.
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Machine learning results based on radiomic analysis are often not transferrable. A potential reason for this is the variability of radiomic features due to varying human made segmentations. Therefore, the aim of this study was to provide comprehensive inter-reader reliability analysis of radiomic features in five clinical image datasets and to assess the association of inter-reader reliability and survival prediction. In this study, we analyzed 4598 tumor segmentations in both computed tomography and magnetic resonance imaging data. We used a neural network to generate 100 additional segmentation outlines for each tumor and performed a reliability analysis of radiomic features. To prove clinical utility, we predicted patient survival based on all features and on the most reliable features. Survival prediction models for both computed tomography and magnetic resonance imaging datasets demonstrated less statistical spread and superior survival prediction when based on the most reliable features. Mean concordance indices were Cmean = 0.58 [most reliable] vs. Cmean = 0.56 [all] (p < 0.001, CT) and Cmean = 0.58 vs. Cmean = 0.57 (p = 0.23, MRI). Thus, preceding reliability analyses and selection of the most reliable radiomic features improves the underlying model’s ability to predict patient survival across clinical imaging modalities and tumor entities.
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Tang, Z., and J. Pauli. "Automatic identification of functional kinematic bone features from MRT segmentation for gait analysis." Materialwissenschaft und Werkstofftechnik 40, no. 10 (October 2009): 725–31. http://dx.doi.org/10.1002/mawe.200900502.
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Jeong, Hongbae, Georgios Ntolkeras, Michel Alhilani, Seyed Reza Atefi, Lilla Zöllei, Kyoko Fujimoto, Ali Pourvaziri, Michael H. Lev, P. Ellen Grant, and Giorgio Bonmassar. "Development, validation, and pilot MRI safety study of a high-resolution, open source, whole body pediatric numerical simulation model." PLOS ONE 16, no. 1 (January 13, 2021): e0241682. http://dx.doi.org/10.1371/journal.pone.0241682.
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Numerical body models of children are used for designing medical devices, including but not limited to optical imaging, ultrasound, CT, EEG/MEG, and MRI. These models are used in many clinical and neuroscience research applications, such as radiation safety dosimetric studies and source localization. Although several such adult models have been reported, there are few reports of full-body pediatric models, and those described have several limitations. Some, for example, are either morphed from older children or do not have detailed segmentations. Here, we introduce a 29-month-old male whole-body native numerical model, “MARTIN”, that includes 28 head and 86 body tissue compartments, segmented directly from the high spatial resolution MRI and CT images. An advanced auto-segmentation tool was used for the deep-brain structures, whereas 3D Slicer was used to segment the non-brain structures and to refine the segmentation for all of the tissue compartments. Our MARTIN model was developed and validated using three separate approaches, through an iterative process, as follows. First, the calculated volumes, weights, and dimensions of selected structures were adjusted and confirmed to be within 6% of the literature values for the 2-3-year-old age-range. Second, all structural segmentations were adjusted and confirmed by two experienced, sub-specialty certified neuro-radiologists, also through an interactive process. Third, an additional validation was performed with a Bloch simulator to create synthetic MR image from our MARTIN model and compare the image contrast of the resulting synthetic image with that of the original MRI data; this resulted in a “structural resemblance” index of 0.97. Finally, we used our model to perform pilot MRI safety simulations of an Active Implantable Medical Device (AIMD) using a commercially available software platform (Sim4Life), incorporating the latest International Standards Organization guidelines. This model will be made available on the Athinoula A. Martinos Center for Biomedical Imaging website.
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Xu, Lin, Qin Zhang, Dan Dong Wang, and Jian Zhang. "Research of Chinese Segmentation Based on MMSeg and Double Array TRIE." Advanced Materials Research 225-226 (April 2011): 945–48. http://dx.doi.org/10.4028/www.scientific.net/amr.225-226.945.
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Chinese segmentation system is a difficulty in computer Chinese information handling. A deep discussion on methods of MMSeg and double array TRIE Chinese segmentation matching on the basis of existing technology in Chinese segmentation is made. On this basis of it , some im2provements are made in the dictionary construction and segmentation arithmetic , designing a Chinese segmentation system based on MMSeg and double array TRIE. Experiment shows that the improving arithmetic accelerated the speed of Chinese segmentation.
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Sandino, Alvaro, Ruchika Verma, Yijiang Chen, David Becerra, Eduardo Romero, and Pallavi Tiwari. "TAMI-22. SEGMENTATION OF DISTINCT TUMOR HALLMARKS OF GLIOBLASTOMA ON DIGITAL HISTOPATHOLOGY USING A HIERARCHICAL DEEP LEARNING APPROACH." Neuro-Oncology 23, Supplement_6 (November 2, 2021): vi202—vi203. http://dx.doi.org/10.1093/neuonc/noab196.806.
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Abstract PURPOSE Glioblastoma is a highly heterogeneous brain tumor. Primary treatment for glioblastoma involves maximally-safe surgical resection. After surgery, resected tissue slides are visually analyzed by neuro-pathologists to identify distinct histological hallmarks characterizing glioblastoma including high cellularity, necrosis, and vascular proliferation. In this work, we present a hierarchical deep learning-based strategy to automatically segment distinct Glioblastoma niches including necrosis, cellular tumor, and hyperplastic blood vessels, on digitized histopathology slides. METHODS We employed the IvyGap cohort for which Hematoxylin and eosin (H&E) slides (digitized at 20X magnification) from n=41 glioblastoma patients were available. Additionally, expert-driven segmentations of cellular tumor, necrosis, and hyperplastic blood vessels (along with other histological attributes) were made available. We randomly employed n=120 slides from 29 patients for training, n=38 slides from 6 cases for validation, and n=30 slides from 6 patients to feed our deep learning model based on Residual Network architecture (ResNet-50). ~2,000 patches of 224x224 pixels were sampled for every slide. Our hierarchical model included first segmenting necrosis from non-necrotic (i.e. cellular tumor) regions, and then from the regions segmented as non-necrotic, identifying hyperplastic blood-vessels from the rest of the cellular tumor. RESULTS Our model achieved a training accuracy of 94%, and a testing accuracy of 88% with an area under the curve (AUC) of 92% in distinguishing necrosis from non-necrotic (i.e. cellular tumor) regions. Similarly, we obtained a training accuracy of 78%, and a testing accuracy of 87% (with an AUC of 94%) in identifying hyperplastic blood vessels from the rest of the cellular tumor. CONCLUSION We developed a reliable hierarchical segmentation model for automatic segmentation of necrotic, cellular tumor, and hyperplastic blood vessels on digitized H&E-stained Glioblastoma tissue images. Future work will involve extension of our model for segmentation of pseudopalisading patterns and microvascular proliferation.
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Wei, Wang, and Yang Xin. "Feature extraction for man-made objects segmentation in aerial images." Machine Vision and Applications 19, no. 1 (May 5, 2007): 57–64. http://dx.doi.org/10.1007/s00138-007-0080-4.
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Csippa, Benjamin, Zsuzsanna Mihály, Zsófia Czinege, Márton Bence Németh, Gábor Halász, György Paál, and Péter Sótonyi. "Comparison of Manual versus Semi-Automatic Segmentations of the Stenotic Carotid Artery Bifurcation." Applied Sciences 11, no. 17 (September 3, 2021): 8192. http://dx.doi.org/10.3390/app11178192.
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Background: The image reconstruction of stenotic carotid bifurcation can be managed by medical practitioners and non-medical investigators with semi-automatic or manual segmentation. The outcome of blood flow simulations may vary because of a single mean voxel difference along the examined section, possibly more in the stenotic lesions, which can lead to conflicting results regarding other research findings. The aim of our project is computational geometry reconstruction for blood flow simulations to make it suitable for comparison with plaque image analysis performed by commercially available software. In this paper, a comparison is made between the manual and semi-automatic segmentations performed by non-medical and medical investigators, respectively. Methods: 30 patients were classified into three homogeneous groups. Our group classification was based on the following parameters: plaque calcification score, thickness, extent, remodeling and plaque localization. The images in the first group were segmented individually by medical practitioners and experienced non-medical investigators, the second group was segmented collectively, and the last group was segmented individually again. Cross-sections along the centerline were extracted, then geometrical and statistical analyses were performed. Exploratory flow simulations were carried out on two patients to showcase the effect of geometrical differences on the hemodynamic flow field. Results: The largest centerline-averaged voxel difference between the medical and non-medical investigators occurred in the first group with a positive difference of 1.16 voxels. In the second and third groups, the average voxel difference decreased to 0.65 and 0.75, respectively. The example case from the first group showed that the difference in maximum wall shear stress in the middle of the stenosis is 30% with an average voxel difference of 1.73. Meanwhile, it can decrease to 4% when the average voxel difference is 0.64 for the example case from the third group. Conclusions: A collective review of the medical images should preceded the manual segmentations before applying them in computational simulations in order to ensure a proper comparison with plaque image analysis. Especially complex pathology such as calcifications should be segmented under medical supervision or after specific training. Non-significant differences in the segmentation can lead to significant differences in the computed flow field.
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Gagliolo, S., and D. Sguerso. "MAGO APPROACH FOR SEMANTIC SEGMENTATION: THE CASE STUDY OF UAVID BENCHMARK DATASET." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2021 (June 28, 2021): 353–59. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2021-353-2021.
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Abstract. The present work is focused on a semantic segmentation strategy implemented in the workflow of the tool MAGO (standing for “Adaptive Mesh for Orthophoto Generation”), considering the contribution of the 3D geometry and the colour information, both deriving from the point cloud of the scene. Moreover, the 2D source imagery, previously used to obtain the photogrammetric point cloud, is employed even to enhance the procedure with the recognition of moving objects, comparing the evolution of epochs.The analysed context is an urban scene, deriving from the UAVid dataset proposed for the ISPRS benchmark. In particular, the so-called “seq18”, a set of high-resolution oblique images taken by UAV (Unmanned Aerial Vehicle), has been used to test the semantic segmentation. The workflow includes the production of two Digital Surface Models (DSMs), containing the geometric and radiometric information, respectively, and their processing by means of the Harris corner detector, allowing the understanding of the image variability. Then, starting from the source geometry and colour information and combining them with their variability mapping, a preliminary classification is performed. Further criteria allow the segmentation of the humans and cars present in the scene. In particular, static objects are identified according to the content of the neighbour pixels in a certain kernel, while the evolution in time of moving elements is recognized by means of the comparison of the projected images belonging to the different epochs. The presented preliminary achievements show some criticalities that require further attention and improvement. In particular, the strategy could be enriched getting more information from the source 2D images, which at the moment are directly used only for the comparison of consecutive epochs.
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F.W. Onifade, Olufade, Paul Akinde, and Folasade Olubusola Isinkaye. "Circular Gabor wavelet algorithm for fingerprint liveness detection." Journal of Advanced Computer Science & Technology 9, no. 1 (January 11, 2020): 1. http://dx.doi.org/10.14419/jacst.v9i1.29908.
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Biometrics usage is growing daily and fingerprint-based recognition system is among the most effective and popular methods of personality identification. The conventional fingerprint sensor functions on total internal reflectance (TIR), which is a method that captures the external features of the finger that is presented to it. Hence, this opens it up to spoof attacks. Liveness detection is an anti-spoofing approach that has the potentials to identify physiological features in fingerprints. It has been demonstrated that spoof fingerprint made of gelatin, gummy and play-doh can easily deceive sensor. Therefore, the security of such sensor is not guaranteed. Here, we established a secure and robust fake-spoof fingerprint identification algorithm using Circular Gabor Wavelet for texture segmentation of the captured images. The samples were exposed to feature extraction processing using circular Gabor wavelet algorithm developed for texture segmentations. The result was evaluated using FAR which measures if a user presented is accepted under a false claimed identity. The FAR result was 0.03125 with an accuracy of 99.968% which showed distinct difference between live and spoof fingerprint.
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Visser, Hennie. "The Prevalence of Market Segmentation Research in the Tourism Industry in Africa." April 2021, Volume 10(2) (April 30, 2021): 500–510. http://dx.doi.org/10.46222/ajhtl.19770720.114.
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Despite the benefits that market segmentation could provide to marketers, it is uncertain to what extend market segmentation research informs decisions about marketing strategy in the tourism industry in Africa. The purpose of this paper is to appraise the incidence of market segmentation research in this context. Market segmentation is used to demarcate a broad market into smaller segments to enable the formulation of marketing strategies based on segment needs. A review of journal articles with market segmentation in the tourism industry in the African context as focus area is provided. While research in this context is available, recommendations are made about possible market segmentation research focus areas in the tourism industry in an African context.
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Wu, Xiao Ying, Li Juan Ma, Zhao Feng Li, and Shi Tao Yan. "Color Image Region Growth Segmentation Integration of Normalized Cut." Advanced Materials Research 143-144 (October 2010): 139–42. http://dx.doi.org/10.4028/www.scientific.net/amr.143-144.139.
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This paper solves that image segmentation result is not consistent with human visual perception or too broken. First of all, based on the continuity of image features, appropriate human vision, calculated the similarity of color image pixel as Eq.2 in HSV space to grow region, then made the regional merge, using normalized-cut segmentation method as Eq.4 and Eq.5 to eliminate over-segmentation phenomenon. In this paper, experimental results shows that the segmentation can be achieved very good results as Fig.1, and parts of the method can be applied in other segmentation to solve over segmentation. This method on color images as the research object is different from other methods on gray images, the selection of seeds and achieves these automatic that differ from general algorithms, presents a new implementation to solve over-segmentation.
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MPORAS, IOSIF, TODOR GANCHEV, and NIKOS FAKOTAKIS. "PHONETIC SEGMENTATION OF EMOTIONAL SPEECH WITH HMM-BASED METHODS." International Journal of Pattern Recognition and Artificial Intelligence 24, no. 07 (November 2010): 1159–79. http://dx.doi.org/10.1142/s0218001410008329.
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In the present work we address the problem of phonetic segmentation of emotional speech. Investigating various traditional and recent HMM-based methods for speech segmentation, which we elaborated for the specifics of emotional speech segmentation, we demonstrate that the HMM-based method with hybrid embedded-isolated training offers advantageous segmentation accuracy, when compared to other HMM-based models used so far. The increased precision of the segmentation is a consequence of the iterative training process employed in the hybrid-training method, which refines the model parameters and the estimated phonetic boundaries taking advantage of the estimations made at previous iterations. Furthermore, we demonstrate the benefits of using purposely-built models for each target category of emotional speech, when compared to the case of one common model built solely from neutral speech. This advantage, in terms of segmentation accuracy, justifies the effort for creating and employing the purposely-built segmentation models per emotion category, since it significantly improves the overall segmentation accuracy.
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Wählby, Carolina, Joakim Lindblad, Mikael Vondrus, Ewert Bengtsson, and Lennart Björkesten. "Algorithms for Cytoplasm Segmentation of Fluorescence Labelled Cells." Analytical Cellular Pathology 24, no. 2-3 (2002): 101–11. http://dx.doi.org/10.1155/2002/821782.
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Automatic cell segmentation has various applications in cytometry, and while the nucleus is often very distinct and easy to identify, the cytoplasm provides a lot more challenge. A new combination of image analysis algorithms for segmentation of cells imaged by fluorescence microscopy is presented. The algorithm consists of an image pre‐processing step, a general segmentation and merging step followed by a segmentation quality measurement. The quality measurement consists of a statistical analysis of a number of shape descriptive features. Objects that have features that differ to that of correctly segmented single cells can be further processed by a splitting step. By statistical analysis we therefore get a feedback system for separation of clustered cells. After the segmentation is completed, the quality of the final segmentation is evaluated. By training the algorithm on a representative set of training images, the algorithm is made fully automatic for subsequent images created under similar conditions. Automatic cytoplasm segmentation was tested on CHO‐cells stained with calcein. The fully automatic method showed between 89% and 97% correct segmentation as compared to manual segmentation.
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Wang, Jing Qiu, and Xiao Lei Wang. "The Segmentation of Ferrography Images: A Brief Survey." Materials Science Forum 770 (October 2013): 427–32. http://dx.doi.org/10.4028/www.scientific.net/msf.770.427.
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This paper provides a general overview on the developments and progress in the segmentation of ferrography images. The problems experienced with applying traditional image processing methods in the segmentation of wear particles, revealed that it is still a big challenge for intelligent ferrography. This has highlighted the need for combining the segmentation and clustering methods for performing ferrography image analysis. In this paper, some of the developments reported in the literature relating to progress made with wear particle image segmentation are reported and examined as a basis for establishing improved methods of ferrography image analysis.
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Kim, Justin J., Hyejin Nam, Neelambar R. Kaipatur, Paul W. Major, Carlos Flores-Mir, Manuel O. Lagravere, and Daniel L. Romanyk. "Reliability and accuracy of segmentation of mandibular condyles from different three-dimensional imaging modalities: a systematic review." Dentomaxillofacial Radiology 49, no. 5 (July 2020): 20190150. http://dx.doi.org/10.1259/dmfr.20190150.
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Objective: To critically synthesize the literature surrounding segmentation of the mandibular condyle using three-dimensional imaging modalities. Specifically, analyzing the reliability and accuracy of methods used for three-dimensional condyle segmentation. Methods: Three electronic databases were searched for studies reporting the reliability and accuracy of various methods used to segment mandibular condyles from three-dimensional imaging modalities. Two authors independently reviewed articles for eligibility and data extraction. Results: Nine studies fulfilled the inclusion criteria. Eight studies assessed the condylar segmentation from CBCT images and limited studies were available on non-CBCT three-dimensional imaging modalities. Threshold-based volume segmentation, manual segmentation, and semi-automatic segmentation techniques were presented. Threshold-based volume segmentation reported higher accuracy when completed by an experienced technician compared to clinicians. Adequate reliability and accuracy were observed in manual segmentation. Although adequate reliability was reported in semi-automatic segmentation, data on its accuracy were lacking. Conclusion: A definitive conclusion with regards to which current technique is most reliable and accurate to efficiently segment the mandibular condyle cannot be made with the currently available evidence. This is especially true in terms of non-CBCT imaging modalities with very limited literature available.
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Liu, Xiangbin, Liping Song, Shuai Liu, and Yudong Zhang. "A Review of Deep-Learning-Based Medical Image Segmentation Methods." Sustainability 13, no. 3 (January 25, 2021): 1224. http://dx.doi.org/10.3390/su13031224.
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As an emerging biomedical image processing technology, medical image segmentation has made great contributions to sustainable medical care. Now it has become an important research direction in the field of computer vision. With the rapid development of deep learning, medical image processing based on deep convolutional neural networks has become a research hotspot. This paper focuses on the research of medical image segmentation based on deep learning. First, the basic ideas and characteristics of medical image segmentation based on deep learning are introduced. By explaining its research status and summarizing the three main methods of medical image segmentation and their own limitations, the future development direction is expanded. Based on the discussion of different pathological tissues and organs, the specificity between them and their classic segmentation algorithms are summarized. Despite the great achievements of medical image segmentation in recent years, medical image segmentation based on deep learning has still encountered difficulties in research. For example, the segmentation accuracy is not high, the number of medical images in the data set is small and the resolution is low. The inaccurate segmentation results are unable to meet the actual clinical requirements. Aiming at the above problems, a comprehensive review of current medical image segmentation methods based on deep learning is provided to help researchers solve existing problems.
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Grosgeorge, Damien, Caroline Petitjean, Bernard Dubray, and Su Ruan. "Esophagus Segmentation from 3D CT Data Using Skeleton Prior-Based Graph Cut." Computational and Mathematical Methods in Medicine 2013 (2013): 1–6. http://dx.doi.org/10.1155/2013/547897.
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The segmentation of organs at risk in CT volumes is a prerequisite for radiotherapy treatment planning. In this paper, we focus on esophagus segmentation, a challenging application since the wall of the esophagus, made of muscle tissue, has very low contrast in CT images. We propose in this paper an original method to segment in thoracic CT scans the 3D esophagus using a skeleton-shape model to guide the segmentation. Our method is composed of two steps: a 3D segmentation by graph cut with skeleton prior, followed by a 2D propagation. Our method yields encouraging results over 6 patients.
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He, Chu, Shenglin Li, Dehui Xiong, Peizhang Fang, and Mingsheng Liao. "Remote Sensing Image Semantic Segmentation Based on Edge Information Guidance." Remote Sensing 12, no. 9 (May 8, 2020): 1501. http://dx.doi.org/10.3390/rs12091501.
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Semantic segmentation is an important field for automatic processing of remote sensing image data. Existing algorithms based on Convolution Neural Network (CNN) have made rapid progress, especially the Fully Convolution Network (FCN). However, problems still exist when directly inputting remote sensing images to FCN because the segmentation result of FCN is not fine enough, and it lacks guidance for prior knowledge. To obtain more accurate segmentation results, this paper introduces edge information as prior knowledge into FCN to revise the segmentation results. Specifically, the Edge-FCN network is proposed in this paper, which uses the edge information detected by Holistically Nested Edge Detection (HED) network to correct the FCN segmentation results. The experiment results on ESAR dataset and GID dataset demonstrate the validity of Edge-FCN.
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Wang, Jiaxin, Chris H. Q. Ding, Sibao Chen, Chenggang He, and Bin Luo. "Semi-Supervised Remote Sensing Image Semantic Segmentation via Consistency Regularization and Average Update of Pseudo-Label." Remote Sensing 12, no. 21 (November 3, 2020): 3603. http://dx.doi.org/10.3390/rs12213603.
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Image segmentation has made great progress in recent years, but the annotation required for image segmentation is usually expensive, especially for remote sensing images. To solve this problem, we explore semi-supervised learning methods and appropriately utilize a large amount of unlabeled data to improve the performance of remote sensing image segmentation. This paper proposes a method for remote sensing image segmentation based on semi-supervised learning. We first design a Consistency Regularization (CR) training method for semi-supervised training, then employ the new learned model for Average Update of Pseudo-label (AUP), and finally combine pseudo labels and strong labels to train semantic segmentation network. We demonstrate the effectiveness of the proposed method on three remote sensing datasets, achieving better performance without more labeled data. Extensive experiments show that our semi-supervised method can learn the latent information from the unlabeled data to improve the segmentation performance.
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Diaz Ruiz, Carlos A., and Hans Kjellberg. "Feral segmentation: How cultural intermediaries perform market segmentation in the wild." Marketing Theory 20, no. 4 (April 30, 2020): 429–57. http://dx.doi.org/10.1177/1470593120920330.
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This article extends the S-T-P framework of market segmentation (i.e. segmentation, targeting, and positioning), showing that firms have more sources of segments than previously acknowledged, including the option of integrating feral segments that emerge publicly in the marketplace. While the S-T-P framework currently focuses on ad hoc segmentation tailored for a focal firm and syndicated segmentation made for commercialization to multiple firms, this article introduces feral segmentation in which cultural intermediaries (CIs) coin consumers categories through their familiarity with popular culture. Empirically, the article investigates how CIs constructed the lumbersexual segment, a neologism combining the narcissism of the metrosexual with the roughness of the lumberjack. The findings include a four-step feral segmentation process: (1) Establishing deviance—singling out anomalies that lower the explanatory power of existing segments. (2) Prototyping—sketching profiles that enhance familiarity and allow identification. (3) Anchoring—attaching the segment into public discussions. (4) Vaccination—coining preemptive validations against criticism.
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Baker, D. C., S. S. Hwang, and J. K. Aggarwal. "Detection and segmentation of man-made objects in outdoor scenes: concrete bridges." Journal of the Optical Society of America A 6, no. 6 (June 1, 1989): 938. http://dx.doi.org/10.1364/josaa.6.000938.
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Yan, Yingjing, and Defu Zhang. "Multi-scale U-like network with attention mechanism for automatic pancreas segmentation." PLOS ONE 16, no. 5 (May 27, 2021): e0252287. http://dx.doi.org/10.1371/journal.pone.0252287.
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In recent years, the rapid development of deep neural networks has made great progress in automatic organ segmentation from abdominal CT scans. However, automatic segmentation for small organs (e.g., the pancreas) is still a challenging task. As an inconspicuous and small organ in the abdomen, the pancreas has a high degree of anatomical variability and is indistinguishable from the surrounding organs and tissues, which usually leads to a very vague boundary. Therefore, the accuracy of pancreatic segmentation is sometimes below satisfaction. In this paper, we propose a 2.5D U-net with an attention mechanism. The proposed network includes 2D convolutional layers and 3D convolutional layers, which means that it requires less computational resources than 3D segmentation models while it can capture more spatial information along the third dimension than 2D segmentation models. Then We use a cascaded framework to increase the accuracy of segmentation results. We evaluate our network on the NIH pancreas dataset and measure the segmentation accuracy by the Dice similarity coefficient (DSC). Experimental results demonstrate a better performance compared with state-of-the-art methods.
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Yang, Jingjing, Hongbin Xu, Juke Liang, Jongyeob Jeong, and Taojin Xu. "Monitoring the training dose and acute fatigue response during elbow flexor resistance training using a custom-made resistance band." PeerJ 8 (February 25, 2020): e8689. http://dx.doi.org/10.7717/peerj.8689.
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Background Home-based resistance training offers an alternative to traditional, hospital-based or rehabilitation center-based resistance training and has attracted much attention recently. However, without the supervision of a therapist or the assistance of an exercise monitoring system, one of the biggest challenges of home-based resistance training is that the therapist may not know if the patient has performed the exercise as prescribed. A lack of objective measurements limits the ability of researchers to evaluate the outcome of exercise interventions and choose suitable training doses. Objective To create an automated and objective method for segmenting resistance force data into contraction phase-specific segments and calculate the repetition number and time-under-tension (TUT) during elbow flexor resistance training. A pilot study was conducted to evaluate the performance of the segmentation algorithm and to show the capability of the system in monitoring the compliance of patients to a prescribed training program in a practical resistance training setting. Methods Six subjects (three male and three female) volunteered to participate in a fatigue and recovery experiment (5 min intermittent submaximal contraction (ISC); 1 min rest; 2 min ISC). A custom-made resistance band was used to help subjects perform biceps curl resistance exercises and the resistance was recorded through a load cell. The maximum and minimum values of the force-derivative were obtained as distinguishing features and a segmentation algorithm was proposed to divide the biceps curl cycle into concentric, eccentric and isometric contraction, and rest phases. Two assessors, who were unfamiliar with the study, were recruited to manually pick the visually observed cut-off point between two contraction phases and the TUT was calculated and compared to evaluate performance of the segmentation algorithm. Results The segmentation algorithm was programmatically implemented and the repetition number and contraction-phase specific TUT were calculated. During isometric, the average TUT (3.75 ± 0.62 s) was longer than the prescribed 3 s, indicating that most subjects did not perform the exercise as prescribed. There was a good TUT agreement and contraction segment agreement between the proposed algorithm and the assessors. Conclusion The good agreement in TUT between the proposed algorithm and the assessors indicates that the proposed algorithm can correctly segment the contraction into contraction phase-specific parts, thereby providing clinicians and researchers with an automated and objective method for quantifying home-based elbow flexor resistance training. The instrument is easy to use and cheap, and the segmentation algorithm is programmatically implemented, indicating good application prospect of the method in a practical setting.
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Zhu, Qikui, Bo Du, Baris Turkbey, Peter Choyke, and Pingkun Yan. "Exploiting Interslice Correlation for MRI Prostate Image Segmentation, from Recursive Neural Networks Aspect." Complexity 2018 (2018): 1–10. http://dx.doi.org/10.1155/2018/4185279.
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Segmentation of the prostate from Magnetic Resonance Imaging (MRI) plays an important role in prostate cancer diagnosis. However, the lack of clear boundary and significant variation of prostate shapes and appearances make the automatic segmentation very challenging. In the past several years, approaches based on deep learning technology have made significant progress on prostate segmentation. However, those approaches mainly paid attention to features and contexts within each single slice of a 3D volume. As a result, this kind of approaches faces many difficulties when segmenting the base and apex of the prostate due to the limited slice boundary information. To tackle this problem, in this paper, we propose a deep neural network with bidirectional convolutional recurrent layers for MRI prostate image segmentation. In addition to utilizing the intraslice contexts and features, the proposed model also treats prostate slices as a data sequence and utilizes the interslice contexts to assist segmentation. The experimental results show that the proposed approach achieved significant segmentation improvement compared to other reported methods.
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Deng, Wu, Kai Luo, Qinke Shi, Yi Yang, and Ning Ning. "Automatic Segmentation and Diagnosis Based on Multi-Scale Two-Stage Region Growing and Skeleton Extraction for Vessel Stenosis in Coronary Angiography." Journal of Medical Imaging and Health Informatics 10, no. 2 (February 1, 2020): 446–51. http://dx.doi.org/10.1166/jmihi.2020.2878.
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Although great progress has been made in vessel segmentation, the existing methods still can not accurately segment small vessels. A novel vessel segmentation and automatic diagnosis in coronary angiography image was proposed. During vessel segmentation, a new vessel function based on Hessian matrix was put forward. Then the vessel contour was extracted by the dual-stage region growing with automatic selection of seed point. Next, the automatic diagnosis was realized by vessel skeleton extraction, skeleton point search and diameter measurement. The experimental results demonstrate that our proposed vessel segmentation can extract the main branch contour accurately and have a good effect on the enhancement and segmentation of small vessels. The automatic diagnosis of vessel stenosis is fast. With a relatively accurate diagnosis result, it can provide a good reference and quantitative basis for the final judgment of the doctor.
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Wang, Ping, Rong Chen, Wei Luo, and Rui Hua Xia. "Research and Implementation of Conglutinated Macrophage Image Segmentation Based on Improved Watershed Algorithm." Advanced Materials Research 225-226 (April 2011): 483–87. http://dx.doi.org/10.4028/www.scientific.net/amr.225-226.483.
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In the process of macrophage image manufacture, cell adhesion often appears because of equipment or man-made reason and then influences subsequent automatic detection and analysis. The traditional watershed algorithm is easy to cause over-segmentation for the volatility of gray extremum. To overcome these phenomena, an improved watershed algorithm which was based on macrophages image segmentation was introduced in this paper. The simulation results show that the improved algorithm can effectively segment adhesive macrophages and restrain the over-segmentation phenomena with an acceptable computation speed.
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Rashwan, Shaheera, Mohamed Talaat Faheem, Amany Sarhan, and Bayumy A. B. Youssef. "A Wavelet Relational FuzzyC-Means Algorithm for 2D Gel Image Segmentation." Computational and Mathematical Methods in Medicine 2013 (2013): 1–13. http://dx.doi.org/10.1155/2013/430516.
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One of the most famous algorithms that appeared in the area of image segmentation is the FuzzyC-Means (FCM) algorithm. This algorithm has been used in many applications such as data analysis, pattern recognition, and image segmentation. It has the advantages of producing high quality segmentation compared to the other available algorithms. Many modifications have been made to the algorithm to improve its segmentation quality. The proposed segmentation algorithm in this paper is based on the FuzzyC-Means algorithm adding the relational fuzzy notion and the wavelet transform to it so as to enhance its performance especially in the area of 2D gel images. Both proposed modifications aim to minimize the oversegmentation error incurred by previous algorithms. The experimental results of comparing both the FuzzyC-Means (FCM) and the Wavelet FuzzyC-Means (WFCM) to the proposed algorithm on real 2D gel images acquired from human leukemias, HL-60 cell lines, and fetal alcohol syndrome (FAS) demonstrate the improvement achieved by the proposed algorithm in overcoming the segmentation error. In addition, we investigate the effect of denoising on the three algorithms. This investigation proves that denoising the 2D gel image before segmentation can improve (in most of the cases) the quality of the segmentation.
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Wei, Xin, Huan Wan, Fanghua Ye, and Weidong Min. "HPS-Net: Multi-Task Network for Medical Image Segmentation with Predictable Performance." Symmetry 13, no. 11 (November 6, 2021): 2107. http://dx.doi.org/10.3390/sym13112107.
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In recent years, medical image segmentation (MIS) has made a huge breakthrough due to the success of deep learning. However, the existing MIS algorithms still suffer from two types of uncertainties: (1) the uncertainty of the plausible segmentation hypotheses and (2) the uncertainty of segmentation performance. These two types of uncertainties affect the effectiveness of the MIS algorithm and then affect the reliability of medical diagnosis. Many studies have been done on the former but ignore the latter. Therefore, we proposed the hierarchical predictable segmentation network (HPS-Net), which consists of a new network structure, a new loss function, and a cooperative training mode. According to our knowledge, HPS-Net is the first network in the MIS area that can generate both the diverse segmentation hypotheses to avoid the uncertainty of the plausible segmentation hypotheses and the measure predictions about these hypotheses to avoid the uncertainty of segmentation performance. Extensive experiments were conducted on the LIDC-IDRI dataset and the ISIC2018 dataset. The results show that HPS-Net has the highest Dice score compared with the benchmark methods, which means it has the best segmentation performance. The results also confirmed that the proposed HPS-Net can effectively predict TNR and TPR.
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Rohl, Mary, and William E. Tunmer. "Phonemic segmentation skill and spelling acquisition." Applied Psycholinguistics 9, no. 4 (December 1988): 335–50. http://dx.doi.org/10.1017/s0142716400008043.
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ABSTRACTA spelling-age match design was used to test the hypothesis that deficits in phonologically related skills may be causally related to difficulties in acquiring basic spelling knowledge. Poor grade 5 spellers, average grade 3 spellers, and good grade 2 spellers matched on a standardized spelling test, and a group of good grade 5 spellers matched by chronological age with the poor grade 5 spellers were administered a phonemic segmentation test containing nondigraph pseudowords and an experimental spelling test containing words of the following four types: exception, ambiguous, regular, and pseudowords. Consistent with the hypothesis, it was found that when compared with the poor spellers, the average and good spellers performed better on the phonemic segmentation task, made fewer errors in spelling pseudowords, and made spelling errors that were more phonetically accurate.
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Xu, Jiangyong, Mingquan Zhou, Zhongke Wu, Wuyang Shui, and Sajid Ali. "Robust surface segmentation and edge feature lines extraction from fractured fragments of relics." Journal of Computational Design and Engineering 2, no. 2 (January 15, 2015): 79–87. http://dx.doi.org/10.1016/j.jcde.2014.12.002.
Повний текст джерелаАнотація:
Abstract Surface segmentation and edge feature lines extraction from fractured fragments of relics are essential steps for computer assisted restoration of fragmented relics. As these fragments were heavily eroded, it is a challenging work to segment surface and extract edge feature lines. This paper presents a novel method to segment surface and extract edge feature lines from triangular meshes of irregular fractured fragments. Firstly, a rough surface segmentation is accomplished by using a clustering algorithm based on the vertex normal vector. Secondly, in order to differentiate between original and fracture faces, a novel integral invariant is introduced to compute the surface roughness. Thirdly, an accurate surface segmentation is implemented by merging faces based on face normal vector and roughness. Finally, edge feature lines are extracted based on the surface segmentation. Some experiments are made and analyzed, and the results show that our method can achieve surface segmentation and edge extraction effectively.
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Hu, Jingfei, Hua Wang, Jie Wang, Yunqi Wang, Fang He, and Jicong Zhang. "SA-Net: A scale-attention network for medical image segmentation." PLOS ONE 16, no. 4 (April 14, 2021): e0247388. http://dx.doi.org/10.1371/journal.pone.0247388.
Повний текст джерелаАнотація:
Semantic segmentation of medical images provides an important cornerstone for subsequent tasks of image analysis and understanding. With rapid advancements in deep learning methods, conventional U-Net segmentation networks have been applied in many fields. Based on exploratory experiments, features at multiple scales have been found to be of great importance for the segmentation of medical images. In this paper, we propose a scale-attention deep learning network (SA-Net), which extracts features of different scales in a residual module and uses an attention module to enforce the scale-attention capability. SA-Net can better learn the multi-scale features and achieve more accurate segmentation for different medical image. In addition, this work validates the proposed method across multiple datasets. The experiment results show SA-Net achieves excellent performances in the applications of vessel detection in retinal images, lung segmentation, artery/vein(A/V) classification in retinal images and blastocyst segmentation. To facilitate SA-Net utilization by the scientific community, the code implementation will be made publicly available.
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Jiang, Peilong, and Xiao Ke. "Lightweight spatial pyramid pooling network for real-time semantic segmentation." Journal of Physics: Conference Series 2234, no. 1 (April 1, 2022): 012012. http://dx.doi.org/10.1088/1742-6596/2234/1/012012.
Повний текст джерелаАнотація:
Abstract In recent years, the state-of-the-art semantic segmentation models have made extremely successful in various challenging scenes. However, the high computation costs of these models make it difficult to deploy to mobile devices. To better serve in computation constraint scenes, the semantic segmentation model should not only have high segmentation performance, but also fast inference speed. In this paper, we proposed an efficient multi-scale context module named LSPPM, which can gather abundant context information at a low computation cost. Base on this, we present a real-time semantic segmentation model called LSPPNet which is specially designed for real-time application. We have done an exhaustive experiment to evaluate LSPPNet in the challenge urban street scenes datasets Cityscapes. Extensive experiment shows that LSPPNet gets a better trade-off between segmentation performance and inference speed. We test LSPPNet on an NVIDIA 2080 super graphics card and it can achieve 75.8% MIoU in Cityscapes test set in real-time speed.
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Wu, Jing, Ana-Maria Philip, Dominika Podkowinski, Bianca S. Gerendas, Georg Langs, Christian Simader, Sebastian M. Waldstein, and Ursula M. Schmidt-Erfurth. "Multivendor Spectral-Domain Optical Coherence Tomography Dataset, Observer Annotation Performance Evaluation, and Standardized Evaluation Framework for Intraretinal Cystoid Fluid Segmentation." Journal of Ophthalmology 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/3898750.
Повний текст джерелаАнотація:
Development of image analysis and machine learning methods for segmentation of clinically significant pathology in retinal spectral-domain optical coherence tomography (SD-OCT), used in disease detection and prediction, is limited due to the availability of expertly annotated reference data. Retinal segmentation methods use datasets that either are not publicly available, come from only one device, or use different evaluation methodologies making them difficult to compare. Thus we present and evaluate a multiple expert annotated reference dataset for the problem of intraretinal cystoid fluid (IRF) segmentation, a key indicator in exudative macular disease. In addition, a standardized framework for segmentation accuracy evaluation, applicable to other pathological structures, is presented. Integral to this work is the dataset used which must be fit for purpose for IRF segmentation algorithm training and testing. We describe here a multivendor dataset comprised of 30 scans. Each OCT scan for system training has been annotated by multiple graders using a proprietary system. Evaluation of the intergrader annotations shows a good correlation, thus making the reproducibly annotated scans suitable for the training and validation of image processing and machine learning based segmentation methods. The dataset will be made publicly available in the form of a segmentation Grand Challenge.
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Li, Weihao, and Michael Ying Yang. "EFFICIENT SEMANTIC SEGMENTATION OF MAN-MADE SCENES USING FULLY-CONNECTED CONDITIONAL RANDOM FIELD." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 10, 2016): 633–40. http://dx.doi.org/10.5194/isprsarchives-xli-b3-633-2016.
Повний текст джерелаАнотація:
In this paper we explore semantic segmentation of man-made scenes using fully connected conditional random field (CRF). Images of man-made scenes display strong contextual dependencies in the spatial structures. Fully connected CRFs can model long-range connections within the image of man-made scenes and make use of contextual information of scene structures. The pairwise edge potentials of fully connected CRF models are defined by a linear combination of Gaussian kernels. Using filter-based mean field algorithm, the inference is very efficient. Our experimental results demonstrate that fully connected CRF performs better than previous state-of-the-art approaches on both eTRIMS dataset and LabelMeFacade dataset.
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Li, Weihao, and Michael Ying Yang. "EFFICIENT SEMANTIC SEGMENTATION OF MAN-MADE SCENES USING FULLY-CONNECTED CONDITIONAL RANDOM FIELD." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 10, 2016): 633–40. http://dx.doi.org/10.5194/isprs-archives-xli-b3-633-2016.
Повний текст джерелаАнотація:
In this paper we explore semantic segmentation of man-made scenes using fully connected conditional random field (CRF). Images of man-made scenes display strong contextual dependencies in the spatial structures. Fully connected CRFs can model long-range connections within the image of man-made scenes and make use of contextual information of scene structures. The pairwise edge potentials of fully connected CRF models are defined by a linear combination of Gaussian kernels. Using filter-based mean field algorithm, the inference is very efficient. Our experimental results demonstrate that fully connected CRF performs better than previous state-of-the-art approaches on both eTRIMS dataset and LabelMeFacade dataset.