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Literatura académica sobre el tema "Automated Segmentation Method"

Autor: Grafiati

Publicado: 25 de mayo de 2024

Última modificación: 31 de julio de 2025

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Artículos de revistas sobre el tema "Automated Segmentation Method"

Harkey, Matthew S., Nicholas Michel, Christopher Kuenze, et al. "Validating a Semi-Automated Technique for Segmenting Femoral Articular Cartilage on Ultrasound Images." CARTILAGE 13, no. 2 (2022): 194760352210930. http://dx.doi.org/10.1177/19476035221093069.

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Objective To validate a semi-automated technique to segment ultrasound-assessed femoral cartilage without compromising segmentation accuracy to a traditional manual segmentation technique in participants with an anterior cruciate ligament injury (ACL). Design We recruited 27 participants with a primary unilateral ACL injury at a pre-operative clinic visit. One investigator performed a transverse suprapatellar ultrasound scan with the participant’s ACL injured knee in maximum flexion. Three femoral cartilage ultrasound images were recorded. A single expert reader manually segmented the femoral

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Boellaard, Thierry N., Roy van Erck, Sophia H. van der Graaf, et al. "Comparing AI and Manual Segmentation of Prostate MRI: Towards AI-Driven 3D-Model-Guided Prostatectomy." Diagnostics 15, no. 9 (2025): 1141. https://doi.org/10.3390/diagnostics15091141.

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Background: Robot-assisted radical prostatectomy (RARP) is a common treatment option for prostate cancer. A 3D model for surgical guidance can improve surgical outcomes. Manual expert radiologist segmentation of the prostate and tumor in prostate MRI to create 3D models is labor-intensive and prone to inter-observer variability, highlighting the need for automated segmentation methods. Methods: This study evaluates the performance of the prostate and tumor segmentation using a commercially available AI tool without (fully automated) and with manual adjustment (AI-assisted) compared to manual s

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Wang, Yang, Yihao Chen, Hao Yuan, and Cheng Wu. "An automated learning method of semantic segmentation for train autonomous driving environment understanding." International Journal of Advances in Intelligent Informatics 10, no. 1 (2024): 148. http://dx.doi.org/10.26555/ijain.v10i1.1521.

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One of the major reasons for the explosion of autonomous driving in recent years is the great development of computer vision. As one of the most fundamental and challenging problems in autonomous driving, environment understanding has been widely studied. It directly determines whether the entire in-vehicle system can effectively identify surrounding objects of vehicles and make correct path planning. Semantic segmentation is the most important means of environment understanding among the many image recognition algorithms used in autonomous driving. However, the success of semantic segmentatio

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Kemnitz, Jana, Christian F. Baumgartner, Felix Eckstein, et al. "Clinical evaluation of fully automated thigh muscle and adipose tissue segmentation using a U-Net deep learning architecture in context of osteoarthritic knee pain." Magnetic Resonance Materials in Physics, Biology and Medicine 33, no. 4 (2019): 483–93. http://dx.doi.org/10.1007/s10334-019-00816-5.

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Abstract Objective Segmentation of thigh muscle and adipose tissue is important for the understanding of musculoskeletal diseases such as osteoarthritis. Therefore, the purpose of this work is (a) to evaluate whether a fully automated approach provides accurate segmentation of muscles and adipose tissue cross-sectional areas (CSA) compared with manual segmentation and (b) to evaluate the validity of this method based on a previous clinical study. Materials and methods The segmentation method is based on U-Net architecture trained on 250 manually segmented thighs from the Osteoarthritis Initiat

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Buser, Myrthe A. D., Alida F. W. van der Steeg, Marc H. W. A. Wijnen, et al. "Radiologic versus Segmentation Measurements to Quantify Wilms Tumor Volume on MRI in Pediatric Patients." Cancers 15, no. 7 (2023): 2115. http://dx.doi.org/10.3390/cancers15072115.

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Wilms tumor is a common pediatric solid tumor. To evaluate tumor response to chemotherapy and decide whether nephron-sparing surgery is possible, tumor volume measurements based on magnetic resonance imaging (MRI) are important. Currently, radiological volume measurements are based on measuring tumor dimensions in three directions. Manual segmentation-based volume measurements might be more accurate, but this process is time-consuming and user-dependent. The aim of this study was to investigate whether manual segmentation-based volume measurements are more accurate and to explore whether these

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Matin-Mann, Farnaz, Ziwen Gao, Chunjiang Wei, et al. "Development and In-Silico and Ex-Vivo Validation of a Software for a Semi-Automated Segmentation of the Round Window Niche to Design a Patient Specific Implant to Treat Inner Ear Disorders." Journal of Imaging 9, no. 2 (2023): 51. http://dx.doi.org/10.3390/jimaging9020051.

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The aim of this study was to develop and validate a semi-automated segmentation approach that identifies the round window niche (RWN) and round window membrane (RWM) for use in the development of patient individualized round window niche implants (RNI) to treat inner ear disorders. Twenty cone beam computed tomography (CBCT) datasets of unilateral temporal bones of patients were included in the study. Defined anatomical landmarks such as the RWM were used to develop a customized 3D Slicer™ plugin for semi-automated segmentation of the RWN. Two otolaryngologists (User 1 and User 2) segmented th

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Sunoqrot, Mohammed R. S., Kirsten M. Selnæs, Elise Sandsmark, et al. "A Quality Control System for Automated Prostate Segmentation on T2-Weighted MRI." Diagnostics 10, no. 9 (2020): 714. http://dx.doi.org/10.3390/diagnostics10090714.

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Computer-aided detection and diagnosis (CAD) systems have the potential to improve robustness and efficiency compared to traditional radiological reading of magnetic resonance imaging (MRI). Fully automated segmentation of the prostate is a crucial step of CAD for prostate cancer, but visual inspection is still required to detect poorly segmented cases. The aim of this work was therefore to establish a fully automated quality control (QC) system for prostate segmentation based on T2-weighted MRI. Four different deep learning-based segmentation methods were used to segment the prostate for 585

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Clark, A. E., B. Biffi, R. Sivera, et al. "Developing and testing an algorithm for automatic segmentation of the fetal face from three-dimensional ultrasound images." Royal Society Open Science 7, no. 11 (2020): 201342. http://dx.doi.org/10.1098/rsos.201342.

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Fetal craniofacial abnormalities are challenging to detect and diagnose on prenatal ultrasound (US). Image segmentation and computer analysis of three-dimensional US volumes of the fetal face may provide an objective measure to quantify fetal facial features and identify abnormalities. We have developed and tested an atlas-based partially automated facial segmentation algorithm; however, the volumes require additional manual segmentation (MS), which is time and labour intensive and may preclude this method from clinical adoption. These manually refined segmentations can then be used as a refer

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Nguyen, Philon, Thanh An Nguyen, and Yong Zeng. "Segmentation of design protocol using EEG." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 33, no. 1 (2018): 11–23. http://dx.doi.org/10.1017/s0890060417000622.

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AbstractDesign protocol data analysis methods form a well-known set of techniques used by design researchers to further understand the conceptual design process. Verbal protocols are a popular technique used to analyze design activities. However, verbal protocols are known to have some limitations. A recurring problem in design protocol analysis is to segment and code protocol data into logical and semantic units. This is usually a manual step and little work has been done on fully automated segmentation techniques. Physiological signals such as electroencephalograms (EEG) can provide assistan

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Tran, Anh T., Dmitriy Desser, Tal Zeevi, et al. "Optimizing Automated Hematoma Expansion Classification from Baseline and Follow-Up Head Computed Tomography." Applied Sciences 15, no. 1 (2024): 111. https://doi.org/10.3390/app15010111.

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Hematoma expansion (HE) is an independent predictor of poor outcomes and a modifiable treatment target in intracerebral hemorrhage (ICH). Evaluating HE in large datasets requires segmentation of hematomas on admission and follow-up CT scans, a process that is time-consuming and labor-intensive in large-scale studies. Automated segmentation of hematomas can expedite this process; however, cumulative errors from segmentation on admission and follow-up scans can hamper accurate HE classification. In this study, we combined a tandem deep-learning classification model with automated segmentation to

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Tesis sobre el tema "Automated Segmentation Method"

Tran, Philippe. "Segmentation and characterization of cerebral white matter hyperintensities : application in individuals with multiple sclerosis and age-related pathologies." Electronic Thesis or Diss., Sorbonne université, 2022. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2022SORUS243.pdf.

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Les hyperintensités de la substance blanche (HSB) sont de plus en plus prises en compte dans le suivi clinique des personnes âgées et/ou des patients atteints de démences, et sont cruciales chez les patients atteints de Sclérose en Plaques (SEP). Des méthodes d'analyse ont été proposées pour aider à quantifier ces lésions à grande échelle, afin de mieux comprendre les mécanismes sous-jacents de ces pathologies. Cependant, à notre connaissance, il n'y a pas de consensus aujourd'hui sur la méthode à utiliser et aucune méthode n'est validée sur ces deux types de sujets. Cette thèse présente plusi

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Shan, Juan. "A Fully Automatic Segmentation Method for Breast Ultrasound Images." DigitalCommons@USU, 2011. https://digitalcommons.usu.edu/etd/905.

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Breast cancer is the second leading cause of death of women worldwide. Accurate lesion boundary detection is important for breast cancer diagnosis. Since many crucial features for discriminating benign and malignant lesions are based on the contour, shape, and texture of the lesion, an accurate segmentation method is essential for a successful diagnosis. Ultrasound is an effective screening tool and primarily useful for differentiating benign and malignant lesions. However, due to inherent speckle noise and low contrast of breast ultrasound imaging, automatic lesion segmentation is still a cha

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Vestergren, Sara, and Navid Zandpour. "Automatic Image Segmentation for Hair Masking: two Methods." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254258.

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We propose two different methods for image segmentation with the objective of marking contaminated regions in images from biochemical tests. The contaminated regions consists of thin hair or fibers and the purpose of this thesis is to eliminate the tedious task of masking the contaminated regions by hand by implementing automatic hair masking. Initially an algorithm based on Morphological Image Processing is presented, followed by solving the problem of pixelwise classification using a Convolutional Neural Network (CNN). Finally, the performance of each implementation is measured by comparing

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Benhabiles, Halim. "3D-mesh segmentation : automatic evaluation and a new learning-based method." Phd thesis, Université des Sciences et Technologie de Lille - Lille I, 2011. http://tel.archives-ouvertes.fr/tel-00834344.

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Dans cette thèse, nous abordons deux problèmes principaux, à savoir l'évaluation quantitative des algorithmes de segmentation de maillages ainsi que la segmentation de maillages par apprentissage en exploitant le facteur humain. Nous proposons les contributions suivantes : - Un benchmark dédié à l'évaluation des algorithmes de segmentation de maillages 3D. Le benchmark inclut un corpus de segmentations vérités-terrains réalisées par des volontaires ainsi qu'une nouvelle métrique de similarité pertinente qui quantifie la cohérence entre ces segmentations vérités-terrains et cel

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Sun, Felice (Felice Tzu-yun) 1976. "Integrating statistical and knowledge-based methods for automatic phonemic segmentation." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/80127.

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Reavy, Richard Wilson. "Image segmentation for automatic target recognition : an investigation of a method of applying post-segmentation derived information to a secondary segmentation process." Thesis, University of Edinburgh, 1999. http://hdl.handle.net/1842/12840.

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A fundamental automatic target recognition (ATR) system can be composed of an object segmentation stage, followed by feature extraction from those objects produced by segmentation, and finally classification of these object features. The capability of such a system in terms of classification success is therefore limited not only by the quality of the feature extraction and classification methods used, but also by the quality of the initial object segmentation. In this thesis, a novel architecture is described which uses two stages of segmentation. This allows image features derived after a pri

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Arif, Omar. "Robust target localization and segmentation using statistical methods." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/33882.

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This thesis aims to contribute to the area of visual tracking, which is the process of identifying an object of interest through a sequence of successive images. The thesis explores kernel-based statistical methods, which map the data to a higher dimensional space. A pre-image framework is provided to find the mapping from the embedding space to the input space for several manifold learning and dimensional learning algorithms. Two algorithms are developed for visual tracking that are robust to noise and occlusions. In the first algorithm, a kernel PCA-based eigenspace representation is used.

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Li, Xiaolong. "Semi-Automatic Segmentation of Normal Female Pelvic Floor Structures from Magnetic Resonance Images." Cleveland State University / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=csu1265412807.

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Kolesov, Ivan A. "Statistical methods for coupling expert knowledge and automatic image segmentation and registration." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/47739.

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The objective of the proposed research is to develop methods that couple an expert user's guidance with automatic image segmentation and registration algorithms. Often, complex processes such as fire, anatomical changes/variations in human bodies, or unpredictable human behavior produce the target images; in these cases, creating a model that precisely describes the process is not feasible. A common solution is to make simplifying assumptions when performing detection, segmentation, or registration tasks automatically. However, when these assumptions are not satisfied, the results are unsatisf

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McCormick, Neil Howie. "Bayesian methods for automatic segmentation and classification of SLO and SONAR data." Thesis, Heriot-Watt University, 2001. http://hdl.handle.net/10399/452.

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Libros sobre el tema "Automated Segmentation Method"

Behrooz, Ali. Systems and Methods for Automated Segmentation of Individual Skeletal Bones in 3D Anatomical Images: United States Patent 9999400. Independently Published, 2020.

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Capítulos de libros sobre el tema "Automated Segmentation Method"

Feng, Weiyan. "Automated Dental CBCT Segmentation Using Pseudo Labeling Method." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-72396-4_14.

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Javaji, Shashidhar Reddy, Advait Gosai, Sovesh Mohapatra, and Gottfried Schlaug. "Automated Ensemble Method for Pediatric Brain Tumor Segmentation." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-76163-8_19.

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Li, Zhihui, Fenggang Huang, and Yongmei Liu. "A Method of Motion Segmentation Based on Region Shrinking." In Intelligent Data Engineering and Automated Learning – IDEAL 2006. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11875581_33.

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Tsagaan, Baigalmaa, Akinobu Shimizu, Hidefumi Kobatake, and Kunihisa Miyakawa. "An Automated Segmentation Method of Kidney Using Statistical Information." In Medical Image Computing and Computer-Assisted Intervention — MICCAI 2002. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45786-0_69.

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Chan, Robin, Svenja Uhlemeyer, Matthias Rottmann, and Hanno Gottschalk. "Detecting and Learning the Unknown in Semantic Segmentation." In Deep Neural Networks and Data for Automated Driving. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-01233-4_10.

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AbstractSemantic segmentation is a crucial component for perception in automated driving. Deep neural networks (DNNs) are commonly used for this task, and they are usually trained on a closed set of object classes appearing in a closed operational domain. However, this is in contrast to the open world assumption in automated driving that DNNs are deployed to. Therefore, DNNs necessarily face data that they have never encountered previously, also known as anomalies, which are extremely safety-critical to properly cope with. In this chapter, we first give an overview about anomalies from an info

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Hashemi, Atiye Sadat, Andreas Bär, Saeed Mozaffari, and Tim Fingscheidt. "Improving Transferability of Generated Universal Adversarial Perturbations for Image Classification and Segmentation." In Deep Neural Networks and Data for Automated Driving. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-01233-4_6.

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AbstractAlthough deep neural networks (DNNs) are high-performance methods for various complex tasks, e.g., environment perception in automated vehicles (AVs), they are vulnerable to adversarial perturbations. Recent works have proven the existence of universal adversarial perturbations (UAPs), which, when added to most images, destroy the output of the respective perception function. Existing attack methods often show a low success rate when attacking target models which are different from the one that the attack was optimized on. To address such weak transferability, we propose a novel learni

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Klingner, Marvin, and Tim Fingscheidt. "Improved DNN Robustness by Multi-task Training with an Auxiliary Self-Supervised Task." In Deep Neural Networks and Data for Automated Driving. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-01233-4_5.

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AbstractWhile deep neural networks for environment perception tasks in autonomous driving systems often achieve impressive performance on clean and well-prepared images, their robustness under real conditions, i.e., on images being perturbed with noise patterns or adversarial attacks, is often subject to a significantly decreased performance. In this chapter, we address this problem for the task of semantic segmentation by proposing multi-task training with the additional task of depth estimation with the goal to improve the DNN robustness. This method has a very wide potential applicability a

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Popescu, Iulia A., Alessandra Borlotti, Erica Dall’Armellina, and Vicente Grau. "Automated LGE Myocardial Scar Segmentation Using MaskSLIC Supervoxels - Replicating the Clinical Method." In Communications in Computer and Information Science. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60964-5_20.

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Schneider, Zofia, and Elżbieta Pociask. "Automated External Contour-Segmentation Method for Vertebrae in Lateral Cervical Spine Radiographs." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88976-0_16.

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Zhan, Yiqiang, and Dinggang Shen. "Automated Segmentation of 3D US Prostate Images Using Statistical Texture-Based Matching Method." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39899-8_84.

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Actas de conferencias sobre el tema "Automated Segmentation Method"

Dolgov, Alexander, Danil Dolgov, and Imil Khairullin. "Instant Road Marking Line Segmentation Method for Highly Automated Vehicles." In 2025 International Russian Smart Industry Conference (SmartIndustryCon). IEEE, 2025. https://doi.org/10.1109/smartindustrycon65166.2025.10986095.

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Chen, Yahan, Wenzheng Liu, and Xiaowei Luo. "Semantic Segmentation Method for Automated Indoor 3D Reconstruction based on Architectural-Knowledge-Aware Features." In 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE, 2025. https://doi.org/10.1109/wacv61041.2025.00269.

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Nash, Will, Tom Drummond, and Nick Birbilis. "Deep Learning AI for Corrosion Detection." In CORROSION 2019. NACE International, 2019. https://doi.org/10.5006/c2019-13267.

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Abstract Visual inspection is a vital component of asset management that stands to benefit from automation. Using artificial intelligence to assist inspections can increase safety, reduce access costs, provide objective classification, and integrate with digital asset management systems. The work presented herein investigates the impact of dataset size on Deep Learning for automatic detection of corrosion on steel assets. Dataset creation is typically one of the first steps when applying Machine Learning methods to a new task; and the real-world performance of models hinges on the quality and

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Renner, Johan, Roland Gårdhagen, and Matts Karlsson. "Subject Specific In-Vivo CFD Estimated Aortic WSS: Comparison Between Manual and Automated Segmentation Methods." In ASME 2008 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2008. http://dx.doi.org/10.1115/sbc2008-192735.

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When making computational fluid dynamics (CFD) based estimations of wall shear stress (WSS) in the human aorta, medical image converting processes to 3D geometries are important as the result is strongly dependent on the quality of the geometry [1]. The image interpretation process or segmentation can be more or less automated; however in clinical work today the gold standard is to manually interpret the medical image information. This combined magnetic resonance imaging (MRI) and CFD method aims to estimate WSS in human arteries in-vivo as WSS is strongly linked to atherosclerosis [2]. More o

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Khouaja, Sourour, Hajer Jlassi, and Kamel Hamrouni. "An automated method for breast mass segmentation." In 2014 6th International Conference of Soft Computing and Pattern Recognition (SoCPaR). IEEE, 2014. http://dx.doi.org/10.1109/socpar.2014.7008002.

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Narote, Sandipan P., Abhilasha S. Narote, Laxman M. Waghmare, and Arun N. Gaikwad. "An Automated Segmentation Method For Iris Recognition." In TENCON 2006 - 2006 IEEE Region 10 Conference. IEEE, 2006. http://dx.doi.org/10.1109/tencon.2006.344211.

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Huo, Zhimin, and Maryellen L. Giger. "Evaluation of an automated segmentation method based on performances of an automated classification method." In Medical Imaging 2000, edited by Elizabeth A. Krupinski. SPIE, 2000. http://dx.doi.org/10.1117/12.383111.

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Kalka, Nathan, Nick Bartlow, and Bojan Cukic. "An automated method for predicting iris segmentation failures." In 2009 IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems (BTAS). IEEE, 2009. http://dx.doi.org/10.1109/btas.2009.5339062.

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Chakraborty, Shouvik, Kalyani Mali, Sankhadeep Chatterjee, et al. "An integrated method for automated biomedical image segmentation." In 2017 4th International Conference on Opto-Electronics and Applied Optics (Optronix). IEEE, 2017. http://dx.doi.org/10.1109/optronix.2017.8349978.

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Huang, Jida, and Tsz-Ho Kwok. "Comparing Segmentation Approaches for Learning-Aware Wireframe Generation on Human Model." In ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/detc2020-22616.

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Abstract Wireframe has been proved very useful for learning human body from semantic parameters. However, the definition of the wireframe is highly dependent on the anthropological experiences of experts in previous works. Hence it is usually not easy to obtain a well-defined wireframe for a new set of human models in the available database. To overcome such difficulty, an automated wireframe generation method would be very helpful in relieving the need for manual anthropometric definition. In order to find such an automated wireframe designing method, a natural way is using automatic segmenta

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Informes sobre el tema "Automated Segmentation Method"

Klobucar, Blaz. Urban Tree Detection in Historical Aerial Imagery of Sweden : a test in automated detection with open source Deep Learning models. Faculty of Landscape Architecture, Horticulture and Crop Production Science, Swedish University of Agricultural Sciences, 2024. http://dx.doi.org/10.54612/a.7kn4q7vikr.

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Urban trees are a key component of the urban environment. In Sweden, ambitious goals have been expressed by authorities regarding the retention and increase of urban tree cover, aiming to mitigate climate change and provide a healthy, livable urban environment in a highly contested space. Tracking urban tree cover through remote sensing serves as an indicator of how past urban planning has succeeded in retaining trees as part of the urban fabric, and historical imagery spanning back decades for such analysis is widely available. This short study examines the viability of automated detection us

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Burks, Thomas F., Victor Alchanatis, and Warren Dixon. Enhancement of Sensing Technologies for Selective Tree Fruit Identification and Targeting in Robotic Harvesting Systems. United States Department of Agriculture, 2009. http://dx.doi.org/10.32747/2009.7591739.bard.

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The proposed project aims to enhance tree fruit identification and targeting for robotic harvesting through the selection of appropriate sensor technology, sensor fusion, and visual servo-control approaches. These technologies will be applicable for apple, orange and grapefruit harvest, although specific sensor wavelengths may vary. The primary challenges are fruit occlusion, light variability, peel color variation with maturity, range to target, and computational requirements of image processing algorithms. There are four major development tasks in original three-year proposed study. First, s

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