Auswahl der wissenschaftlichen Literatur zum Thema „Automated Segmentation Method“
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Zeitschriftenartikel zum Thema "Automated Segmentation Method"
Harkey, Matthew S., Nicholas Michel, Christopher Kuenze, Ryan Fajardo, Matt Salzler, Jeffrey B. Driban und Ilker Hacihaliloglu. „Validating a Semi-Automated Technique for Segmenting Femoral Articular Cartilage on Ultrasound Images“. CARTILAGE 13, Nr. 2 (April 2022): 194760352210930. http://dx.doi.org/10.1177/19476035221093069.
Der volle Inhalt der QuelleWang, Yang, Yihao Chen, Hao Yuan und Cheng Wu. „An automated learning method of semantic segmentation for train autonomous driving environment understanding“. International Journal of Advances in Intelligent Informatics 10, Nr. 1 (29.02.2024): 148. http://dx.doi.org/10.26555/ijain.v10i1.1521.
Der volle Inhalt der QuelleKemnitz, Jana, Christian F. Baumgartner, Felix Eckstein, Akshay Chaudhari, Anja Ruhdorfer, Wolfgang Wirth, Sebastian K. Eder und 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, Nr. 4 (23.12.2019): 483–93. http://dx.doi.org/10.1007/s10334-019-00816-5.
Der volle Inhalt der QuelleBuser, 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 und Bas H. M. van der Velden. „Radiologic versus Segmentation Measurements to Quantify Wilms Tumor Volume on MRI in Pediatric Patients“. Cancers 15, Nr. 7 (01.04.2023): 2115. http://dx.doi.org/10.3390/cancers15072115.
Der volle Inhalt der QuelleMatin-Mann, Farnaz, Ziwen Gao, Chunjiang Wei, Felix Repp, Eralp-Niyazi Artukarslan, Samuel John, Dorian Alcacer Labrador, Thomas Lenarz und 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, Nr. 2 (20.02.2023): 51. http://dx.doi.org/10.3390/jimaging9020051.
Der volle Inhalt der QuelleSunoqrot, Mohammed R. S., Kirsten M. Selnæs, Elise Sandsmark, Gabriel A. Nketiah, Olmo Zavala-Romero, Radka Stoyanova, Tone F. Bathen und Mattijs Elschot. „A Quality Control System for Automated Prostate Segmentation on T2-Weighted MRI“. Diagnostics 10, Nr. 9 (18.09.2020): 714. http://dx.doi.org/10.3390/diagnostics10090714.
Der volle Inhalt der QuelleClark, A. E., B. Biffi, R. Sivera, A. Dall'Asta, L. Fessey, T. L. Wong, G. Paramasivam, D. Dunaway, S. Schievano und 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, Nr. 11 (November 2020): 201342. http://dx.doi.org/10.1098/rsos.201342.
Der volle Inhalt der QuelleNguyen, Philon, Thanh An Nguyen und Yong Zeng. „Segmentation of design protocol using EEG“. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 33, Nr. 1 (03.04.2018): 11–23. http://dx.doi.org/10.1017/s0890060417000622.
Der volle Inhalt der QuelleNishiyama, Daisuke, Hiroshi Iwasaki, Takaya Taniguchi, Daisuke Fukui, Manabu Yamanaka, Teiji Harada und Hiroshi Yamada. „Deep generative models for automated muscle segmentation in computed tomography scanning“. PLOS ONE 16, Nr. 9 (10.09.2021): e0257371. http://dx.doi.org/10.1371/journal.pone.0257371.
Der volle Inhalt der QuelleG, Mohandass, Hari Krishnan G und Hemalatha R J. „An approach to automated retinal layer segmentation in SDOCT images“. International Journal of Engineering & Technology 7, Nr. 2.25 (03.05.2018): 56. http://dx.doi.org/10.14419/ijet.v7i2.25.12371.
Der volle Inhalt der QuelleDissertationen zum Thema "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.
Der volle Inhalt der QuelleWhite 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.
Der volle Inhalt der QuelleVestergren, Sara, und 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.
Der volle Inhalt der QuelleBenhabiles, 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.
Der volle Inhalt der QuelleSun, 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.
Der volle Inhalt der QuelleReavy, 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.
Der volle Inhalt der QuelleLi, 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.
Der volle Inhalt der QuelleArif, Omar. „Robust target localization and segmentation using statistical methods“. Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/33882.
Der volle Inhalt der QuelleKolesov, 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.
Der volle Inhalt der QuelleMcCormick, 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.
Der volle Inhalt der QuelleBücher zum Thema "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.
Den vollen Inhalt der Quelle findenBuchteile zum Thema "Automated Segmentation Method"
Li, Zhihui, Fenggang Huang und 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.
Der volle Inhalt der QuelleTsagaan, Baigalmaa, Akinobu Shimizu, Hidefumi Kobatake und 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.
Der volle Inhalt der QuelleChan, Robin, Svenja Uhlemeyer, Matthias Rottmann und 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.
Der volle Inhalt der QuelleHashemi, Atiye Sadat, Andreas Bär, Saeed Mozaffari und 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.
Der volle Inhalt der QuelleKlingner, Marvin, und 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.
Der volle Inhalt der QuellePopescu, Iulia A., Alessandra Borlotti, Erica Dall’Armellina und 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.
Der volle Inhalt der QuelleSchneider, Zofia, und 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.
Der volle Inhalt der QuelleZhan, Yiqiang, und 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.
Der volle Inhalt der QuelleDuda, Julia, Izabela Cywińska und 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.
Der volle Inhalt der QuelleDuan, Lijuan, Xuan Feng, Jie Chen und 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.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Automated Segmentation Method"
Renner, Johan, Roland Gårdhagen und 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.
Der volle Inhalt der QuelleKhouaja, Sourour, Hajer Jlassi und 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.
Der volle Inhalt der QuelleNarote, Sandipan P., Abhilasha S. Narote, Laxman M. Waghmare und 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.
Der volle Inhalt der QuelleHuo, Zhimin, und Maryellen L. Giger. „Evaluation of an automated segmentation method based on performances of an automated classification method“. In Medical Imaging 2000, herausgegeben von Elizabeth A. Krupinski. SPIE, 2000. http://dx.doi.org/10.1117/12.383111.
Der volle Inhalt der QuelleHuang, Jida, und 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.
Der volle Inhalt der QuelleKalka, Nathan, Nick Bartlow und 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.
Der volle Inhalt der QuelleChakraborty, Shouvik, Kalyani Mali, Sankhadeep Chatterjee, Soumen Banerjee, Kyamelia Roy, Kamelia Deb, Sayan Sarkar und 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.
Der volle Inhalt der QuelleWenjun Tan, Jinzhu Yang, Dazhe Zhao, Shuang Ma, Li Qu und 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.
Der volle Inhalt der QuelleRosidi, Rasyiqah Annani Mohd, Aida Syafiqah Ahmad Khaizi, Hong-Seng Gan und 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.
Der volle Inhalt der QuelleParanjape, Amit S., Badr Elmaanaoui, Jordan Dewelle, H. Grady Rylander und Thomas E. Milner. „Automated method for RNFL segmentation in spectral domain OCT“. In Biomedical Optics (BiOS) 2008, herausgegeben von Tuan Vo-Dinh, Warren S. Grundfest, David A. Benaron und Gerald E. Cohn. SPIE, 2008. http://dx.doi.org/10.1117/12.763491.
Der volle Inhalt der QuelleBerichte der Organisationen zum Thema "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.
Der volle Inhalt der QuelleBurks, Thomas F., Victor Alchanatis und Warren Dixon. Enhancement of Sensing Technologies for Selective Tree Fruit Identification and Targeting in Robotic Harvesting Systems. United States Department of Agriculture, Oktober 2009. http://dx.doi.org/10.32747/2009.7591739.bard.
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