Literatura científica selecionada sobre o tema "Automated Segmentation Method"
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Artigos de revistas sobre o assunto "Automated Segmentation Method"
Harkey, Matthew S., Nicholas Michel, Christopher Kuenze, Ryan Fajardo, Matt Salzler, Jeffrey B. Driban e Ilker Hacihaliloglu. "Validating a Semi-Automated Technique for Segmenting Femoral Articular Cartilage on Ultrasound Images". CARTILAGE 13, n.º 2 (abril de 2022): 194760352210930. http://dx.doi.org/10.1177/19476035221093069.
Texto completo da fonteWang, Yang, Yihao Chen, Hao Yuan e Cheng Wu. "An automated learning method of semantic segmentation for train autonomous driving environment understanding". International Journal of Advances in Intelligent Informatics 10, n.º 1 (29 de fevereiro de 2024): 148. http://dx.doi.org/10.26555/ijain.v10i1.1521.
Texto completo da fonteKemnitz, Jana, Christian F. Baumgartner, Felix Eckstein, Akshay Chaudhari, Anja Ruhdorfer, Wolfgang Wirth, Sebastian K. Eder e Ender Konukoglu. "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, n.º 4 (23 de dezembro de 2019): 483–93. http://dx.doi.org/10.1007/s10334-019-00816-5.
Texto completo da fonteBuser, Myrthe A. D., Alida F. W. van der Steeg, Marc H. W. A. Wijnen, Matthijs Fitski, Harm van Tinteren, Marry M. van den Heuvel-Eibrink, Annemieke S. Littooij e Bas H. M. van der Velden. "Radiologic versus Segmentation Measurements to Quantify Wilms Tumor Volume on MRI in Pediatric Patients". Cancers 15, n.º 7 (1 de abril de 2023): 2115. http://dx.doi.org/10.3390/cancers15072115.
Texto completo da fonteMatin-Mann, Farnaz, Ziwen Gao, Chunjiang Wei, Felix Repp, Eralp-Niyazi Artukarslan, Samuel John, Dorian Alcacer Labrador, Thomas Lenarz e Verena Scheper. "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, n.º 2 (20 de fevereiro de 2023): 51. http://dx.doi.org/10.3390/jimaging9020051.
Texto completo da fonteSunoqrot, Mohammed R. S., Kirsten M. Selnæs, Elise Sandsmark, Gabriel A. Nketiah, Olmo Zavala-Romero, Radka Stoyanova, Tone F. Bathen e Mattijs Elschot. "A Quality Control System for Automated Prostate Segmentation on T2-Weighted MRI". Diagnostics 10, n.º 9 (18 de setembro de 2020): 714. http://dx.doi.org/10.3390/diagnostics10090714.
Texto completo da fonteClark, A. E., B. Biffi, R. Sivera, A. Dall'Asta, L. Fessey, T. L. Wong, G. Paramasivam, D. Dunaway, S. Schievano e C. C. Lees. "Developing and testing an algorithm for automatic segmentation of the fetal face from three-dimensional ultrasound images". Royal Society Open Science 7, n.º 11 (novembro de 2020): 201342. http://dx.doi.org/10.1098/rsos.201342.
Texto completo da fonteNguyen, Philon, Thanh An Nguyen e Yong Zeng. "Segmentation of design protocol using EEG". Artificial Intelligence for Engineering Design, Analysis and Manufacturing 33, n.º 1 (3 de abril de 2018): 11–23. http://dx.doi.org/10.1017/s0890060417000622.
Texto completo da fonteNishiyama, Daisuke, Hiroshi Iwasaki, Takaya Taniguchi, Daisuke Fukui, Manabu Yamanaka, Teiji Harada e Hiroshi Yamada. "Deep generative models for automated muscle segmentation in computed tomography scanning". PLOS ONE 16, n.º 9 (10 de setembro de 2021): e0257371. http://dx.doi.org/10.1371/journal.pone.0257371.
Texto completo da fonteG, Mohandass, Hari Krishnan G e Hemalatha R J. "An approach to automated retinal layer segmentation in SDOCT images". International Journal of Engineering & Technology 7, n.º 2.25 (3 de maio de 2018): 56. http://dx.doi.org/10.14419/ijet.v7i2.25.12371.
Texto completo da fonteTeses / dissertações sobre o assunto "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.
Texto completo da fonteWhite matter hyperintensities (WMH) are more and more taken into account in the clinical monitoring of elderly subjects and/or dementia patients, and are crucial in patients with Multiple Sclerosis (MS). Automated methods have been proposed to better quantify these lesions on a large scale, in order to better understand the underlying mechanisms of these pathologies. However, to our knowledge, no automated method has reached consensus today for the segmentation of WMH, and no method has been validated on these two types of subjects. This thesis introduces several tools and their validations in order to better characterize WMH. First of all, WHASA-3D (Tran et al. 2022) is a new automated method for WMH segmentation adapted for 3D T2-FLAIR data and MS patients in a multicenter setting. It is a major improvement of WHASA (Samaille et al. 2012). WHASA-3D's performances are here compared with six state-of-the-art methods with their default parameters and optimized settings, when possible. Two extensions have then been developped to support the clinician for patient diagnosis and clinical monitoring. WHASA-Spatial is an extension for the automatic spatial characterization of WMH provided by WHASA-3D according to four classes (periventricular, infratentorial, juxtacortical/cortical, deep). The visual assessment on 104 MS subjects showed that the global classification was very satisfactory. Finally, WHASA-Longitudinal, is an extension that allows the automatic segmentation of new or enlarged lesions between two successive acquisitions. The performance of this method was satisfactory for volume agreement and a solution is proposed and needs to be investigated to improve new lesion count. These results need to be confirmed on a larger number of subjects
Shan, Juan. "A Fully Automatic Segmentation Method for Breast Ultrasound Images". DigitalCommons@USU, 2011. https://digitalcommons.usu.edu/etd/905.
Texto completo da fonteVestergren, Sara, e 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.
Texto completo da fonteBenhabiles, 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.
Texto completo da fonteSun, 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.
Texto completo da fonteReavy, 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.
Texto completo da fonteLi, 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.
Texto completo da fonteArif, Omar. "Robust target localization and segmentation using statistical methods". Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/33882.
Texto completo da fonteKolesov, 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.
Texto completo da fonteMcCormick, 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.
Texto completo da fonteLivros sobre o assunto "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.
Encontre o texto completo da fonteCapítulos de livros sobre o assunto "Automated Segmentation Method"
Li, Zhihui, Fenggang Huang e Yongmei Liu. "A Method of Motion Segmentation Based on Region Shrinking". In Intelligent Data Engineering and Automated Learning – IDEAL 2006, 275–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11875581_33.
Texto completo da fonteTsagaan, Baigalmaa, Akinobu Shimizu, Hidefumi Kobatake e Kunihisa Miyakawa. "An Automated Segmentation Method of Kidney Using Statistical Information". In Medical Image Computing and Computer-Assisted Intervention — MICCAI 2002, 556–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45786-0_69.
Texto completo da fonteChan, Robin, Svenja Uhlemeyer, Matthias Rottmann e Hanno Gottschalk. "Detecting and Learning the Unknown in Semantic Segmentation". In Deep Neural Networks and Data for Automated Driving, 277–313. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-01233-4_10.
Texto completo da fonteHashemi, Atiye Sadat, Andreas Bär, Saeed Mozaffari e Tim Fingscheidt. "Improving Transferability of Generated Universal Adversarial Perturbations for Image Classification and Segmentation". In Deep Neural Networks and Data for Automated Driving, 171–96. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-01233-4_6.
Texto completo da fonteKlingner, Marvin, e Tim Fingscheidt. "Improved DNN Robustness by Multi-task Training with an Auxiliary Self-Supervised Task". In Deep Neural Networks and Data for Automated Driving, 149–70. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-01233-4_5.
Texto completo da fontePopescu, Iulia A., Alessandra Borlotti, Erica Dall’Armellina e Vicente Grau. "Automated LGE Myocardial Scar Segmentation Using MaskSLIC Supervoxels - Replicating the Clinical Method". In Communications in Computer and Information Science, 229–36. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60964-5_20.
Texto completo da fonteSchneider, Zofia, e Elżbieta Pociask. "Automated External Contour-Segmentation Method for Vertebrae in Lateral Cervical Spine Radiographs". In Advances in Intelligent Systems and Computing, 118–26. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88976-0_16.
Texto completo da fonteZhan, Yiqiang, e Dinggang Shen. "Automated Segmentation of 3D US Prostate Images Using Statistical Texture-Based Matching Method". In Lecture Notes in Computer Science, 688–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39899-8_84.
Texto completo da fonteDuda, Julia, Izabela Cywińska e Elżbieta Pociask. "Fully Automated Lumen Segmentation Method and BVS Stent Struts Detection in OCT Images". In Communications in Computer and Information Science, 353–67. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-19647-8_25.
Texto completo da fonteDuan, Lijuan, Xuan Feng, Jie Chen e Fan Xu. "An Automated Method with Feature Pyramid Encoder and Dual-Path Decoder for Nuclei Segmentation". In Pattern Recognition and Computer Vision, 341–52. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60633-6_28.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Automated Segmentation Method"
Renner, Johan, Roland Gårdhagen e 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.
Texto completo da fonteKhouaja, Sourour, Hajer Jlassi e 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.
Texto completo da fonteNarote, Sandipan P., Abhilasha S. Narote, Laxman M. Waghmare e 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.
Texto completo da fonteHuo, Zhimin, e Maryellen L. Giger. "Evaluation of an automated segmentation method based on performances of an automated classification method". In Medical Imaging 2000, editado por Elizabeth A. Krupinski. SPIE, 2000. http://dx.doi.org/10.1117/12.383111.
Texto completo da fonteHuang, Jida, e 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.
Texto completo da fonteKalka, Nathan, Nick Bartlow e 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.
Texto completo da fonteChakraborty, Shouvik, Kalyani Mali, Sankhadeep Chatterjee, Soumen Banerjee, Kyamelia Roy, Kamelia Deb, Sayan Sarkar e Neha Prasad. "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.
Texto completo da fonteWenjun Tan, Jinzhu Yang, Dazhe Zhao, Shuang Ma, Li Qu e Jinchi Wang. "A novel method for automated segmentation of airway tree". In 2012 24th Chinese Control and Decision Conference (CCDC). IEEE, 2012. http://dx.doi.org/10.1109/ccdc.2012.6244152.
Texto completo da fonteRosidi, Rasyiqah Annani Mohd, Aida Syafiqah Ahmad Khaizi, Hong-Seng Gan e Hafiz Basarudin. "Boundary correction in semi-automated segmentation using scribbling method". In 2017 International Conference on Engineering Technology and Technopreneurship (ICE2T). IEEE, 2017. http://dx.doi.org/10.1109/ice2t.2017.8215986.
Texto completo da fonteParanjape, Amit S., Badr Elmaanaoui, Jordan Dewelle, H. Grady Rylander e Thomas E. Milner. "Automated method for RNFL segmentation in spectral domain OCT". In Biomedical Optics (BiOS) 2008, editado por Tuan Vo-Dinh, Warren S. Grundfest, David A. Benaron e Gerald E. Cohn. SPIE, 2008. http://dx.doi.org/10.1117/12.763491.
Texto completo da fonteRelatórios de organizações sobre o assunto "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.
Texto completo da fonteBurks, Thomas F., Victor Alchanatis e Warren Dixon. Enhancement of Sensing Technologies for Selective Tree Fruit Identification and Targeting in Robotic Harvesting Systems. United States Department of Agriculture, outubro de 2009. http://dx.doi.org/10.32747/2009.7591739.bard.
Texto completo da fonte