Zeitschriftenartikel zum Thema „Automated Segmentation Method“
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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 QuelleYang, Xin, Chaoyue Liu, Hung Le Minh, Zhiwei Wang, Aichi Chien und Kwang-Ting (Tim) Cheng. „An automated method for accurate vessel segmentation“. Physics in Medicine and Biology 62, Nr. 9 (06.04.2017): 3757–78. http://dx.doi.org/10.1088/1361-6560/aa6418.
Der volle Inhalt der QuelleJaware, Tushar H., K. B. Khanchandani und Anita Zurani. „An Accurate Automated Local Similarity Factor-Based Neural Tree Approach toward Tissue Segmentation of Newborn Brain MRI“. American Journal of Perinatology 36, Nr. 11 (15.12.2018): 1157–70. http://dx.doi.org/10.1055/s-0038-1675375.
Der volle Inhalt der QuelleHalawa, Abdelrahman, Shehab Gamalel-Din und Abdurrahman Nasr. „EXPLOITING BERT FOR MALFORMED SEGMENTATION DETECTION TO IMPROVE SCIENTIFIC WRITINGS“. Applied Computer Science 19, Nr. 2 (30.06.2023): 126–41. http://dx.doi.org/10.35784/acs-2023-20.
Der volle Inhalt der QuelleBowes, Michael Antony, Gwenael Alain Guillard, Graham Richard Vincent, Alan Donald Brett, Christopher Brian Hartley Wolstenholme und Philip Gerard Conaghan. „Precision, Reliability, and Responsiveness of a Novel Automated Quantification Tool for Cartilage Thickness: Data from the Osteoarthritis Initiative“. Journal of Rheumatology 47, Nr. 2 (15.04.2019): 282–89. http://dx.doi.org/10.3899/jrheum.180541.
Der volle Inhalt der QuellePociask, Elżbieta, Krzysztof Piotr Malinowski, Magdalena Ślęzak, Joanna Jaworek-Korjakowska, Wojciech Wojakowski und Tomasz Roleder. „Fully Automated Lumen Segmentation Method for Intracoronary Optical Coherence Tomography“. Journal of Healthcare Engineering 2018 (26.12.2018): 1–13. http://dx.doi.org/10.1155/2018/1414076.
Der volle Inhalt der QuelleMASSOPTIER, LAURENT, AVISHKAR MISRA, ARCOT SOWMYA und SERGIO CASCIARO. „COMBINING GRAPH-CUT TECHNIQUE AND ANATOMICAL KNOWLEDGE FOR AUTOMATIC SEGMENTATION OF LUNGS AFFECTED BY DIFFUSE PARENCHYMAL DISEASE IN HRCT IMAGES“. International Journal of Image and Graphics 11, Nr. 04 (Oktober 2011): 509–29. http://dx.doi.org/10.1142/s0219467811004202.
Der volle Inhalt der QuelleBouzid-Daho, Abdellatif, Naima Sofi, Schahrazad Soltane und Patrick Siarry. „Automated detection in microscopic images using segmentation“. Brazilian Journal of Technology 7, Nr. 2 (30.04.2024): e69317. http://dx.doi.org/10.38152/bjtv7n2-003.
Der volle Inhalt der QuelleSun, Yusen, Xingji Jin, Timo Pukkala und Fengri Li. „A Comparison of Four Methods for Automatic Delineation of Tree Stands from Grids of LiDAR Metrics“. Remote Sensing 14, Nr. 24 (07.12.2022): 6192. http://dx.doi.org/10.3390/rs14246192.
Der volle Inhalt der QuelleXiong, Hui, Laith R. Sultan, Theodore W. Cary, Susan M. Schultz, Ghizlane Bouzghar und Chandra M. Sehgal. „The diagnostic performance of leak-plugging automated segmentation versus manual tracing of breast lesions on ultrasound images“. Ultrasound 25, Nr. 2 (25.01.2017): 98–106. http://dx.doi.org/10.1177/1742271x17690425.
Der volle Inhalt der QuelleGolkar, Ehsan, Hossein Rabbani und Ashrani Aizzuddin Abd. Rahni. „Inter-subject Registration-based Segmentation of Thoracic-Abdominal Organs in 4 Dimensional Magnetic Resonance Imaging“. Jurnal Kejuruteraan 33, Nr. 4 (30.11.2021): 1045–51. http://dx.doi.org/10.17576/jkukm-2021-33(4)-26.
Der volle Inhalt der QuelleTran, Carol, Orit Glenn, Christopher Hess und Andreas Rauschecker. „4252 Automated Fetal Brain Volumetry on Clinical Fetal MRI Using Convolutional Neural Network“. Journal of Clinical and Translational Science 4, s1 (Juni 2020): 45–46. http://dx.doi.org/10.1017/cts.2020.169.
Der volle Inhalt der QuelleJin, Felix Q., Anna E. Knight, Adela R. Cardones, Kathryn R. Nightingale und Mark L. Palmeri. „Semi-automated weak annotation for deep neural network skin thickness measurement“. Ultrasonic Imaging 43, Nr. 4 (11.05.2021): 167–74. http://dx.doi.org/10.1177/01617346211014138.
Der volle Inhalt der QuelleJiang, Huiyan, Shaojie Li und Siqi Li. „Registration-Based Organ Positioning and Joint Segmentation Method for Liver and Tumor Segmentation“. BioMed Research International 2018 (24.09.2018): 1–11. http://dx.doi.org/10.1155/2018/8536854.
Der volle Inhalt der QuelleChoi, Woorim, Chul-Ho Kim, Hyein Yoo, Hee Rim Yun, Da-Wit Kim und Ji Wan Kim. „Development and validation of a reliable method for automated measurements of psoas muscle volume in CT scans using deep learning-based segmentation: a cross-sectional study“. BMJ Open 14, Nr. 5 (Mai 2024): e079417. http://dx.doi.org/10.1136/bmjopen-2023-079417.
Der volle Inhalt der QuelleLee, Seyoung, Kai Zhang, Jeeyeon Lee, Peter Haseok Kim, Amogh Hiremath, Salie Lee, Monica Yadav et al. „Abstract 2595: Accelerated and precise tumor segmentation in NSCLC: A comparative analysis of automated ClickSeg and manual annotation for radiomics“. Cancer Research 84, Nr. 6_Supplement (22.03.2024): 2595. http://dx.doi.org/10.1158/1538-7445.am2024-2595.
Der volle Inhalt der QuelleZemborain, Zane Zenon, Matias Soifer, Nadim S. Azar, Sofia Murillo, Hazem M. Mousa, Victor L. Perez und Sina Farsiu. „Open-Source Automated Segmentation of Neuronal Structures in Corneal Confocal Microscopy Images of the Subbasal Nerve Plexus With Accuracy on Par With Human Segmentation“. Cornea 42, Nr. 10 (07.07.2023): 1309–19. http://dx.doi.org/10.1097/ico.0000000000003319.
Der volle Inhalt der QuelleMoëll, Mattias K., und Lloyd A. Donaldson. „COMPARISON OF SEGMENTATION METHODS FOR DIGITAL IMAGE ANALYSIS OF CONFOCAL MICROSCOPE IMAGES TO MEASURE TRACHEID CELL DIMENSIONS“. IAWA Journal 22, Nr. 3 (2001): 267–88. http://dx.doi.org/10.1163/22941932-90000284.
Der volle Inhalt der QuelleYu, Zechen, Zhongping Chen, Yang Yu, Haichen Zhu, Dan Tong und Yang Chen. „An automated ASPECTS method with atlas-based segmentation“. Computer Methods and Programs in Biomedicine 210 (Oktober 2021): 106376. http://dx.doi.org/10.1016/j.cmpb.2021.106376.
Der volle Inhalt der QuelleWeiwei, Xing, Wang Weiqiang, Bao Peng, Sun Liya und Tong Leiming. „A novel method for automated human behavior segmentation“. Computer Animation and Virtual Worlds 27, Nr. 5 (12.04.2016): 501–14. http://dx.doi.org/10.1002/cav.1690.
Der volle Inhalt der QuelleWan, Guo Chun, Meng Meng Li, He Xu, Wen Hao Kang, Jin Wen Rui und Mei Song Tong. „XFinger-Net: Pixel-Wise Segmentation Method for Partially Defective Fingerprint Based on Attention Gates and U-Net“. Sensors 20, Nr. 16 (10.08.2020): 4473. http://dx.doi.org/10.3390/s20164473.
Der volle Inhalt der QuelleWang, Yuliang, Tongda Lu, Xiaolai Li, Shuai Ren und Shusheng Bi. „Robust nanobubble and nanodroplet segmentation in atomic force microscope images using the spherical Hough transform“. Beilstein Journal of Nanotechnology 8 (01.12.2017): 2572–82. http://dx.doi.org/10.3762/bjnano.8.257.
Der volle Inhalt der QuelleMukondiwa, Daisy Thembelihle, YongTao Shi und Chao Gao. „A Prostate Boundary Localization and Edge Denoising Algorithm“. East African Journal of Information Technology 7, Nr. 1 (30.04.2024): 108–20. http://dx.doi.org/10.37284/eajit.7.1.1900.
Der volle Inhalt der QuelleIglesias-Rey, Sara, Felipe Antunes-Santos, Cathleen Hagemann, David Gómez-Cabrero, Humberto Bustince, Rickie Patani, Andrea Serio, Bernard De Baets und Carlos Lopez-Molina. „Unsupervised Cell Segmentation and Labelling in Neural Tissue Images“. Applied Sciences 11, Nr. 9 (21.04.2021): 3733. http://dx.doi.org/10.3390/app11093733.
Der volle Inhalt der QuelleArafati, Arghavan, Daisuke Morisawa, Michael R. Avendi, M. Reza Amini, Ramin A. Assadi, Hamid Jafarkhani und Arash Kheradvar. „Generalizable fully automated multi-label segmentation of four-chamber view echocardiograms based on deep convolutional adversarial networks“. Journal of The Royal Society Interface 17, Nr. 169 (August 2020): 20200267. http://dx.doi.org/10.1098/rsif.2020.0267.
Der volle Inhalt der QuelleKazerooni, Anahita Fathi, Nastaran Khalili, Debanjan Haldar, Karthik Viswanathan, Ariana Familiar, Sina Bagheri, Hannah Anderson et al. „IMG-05. A MULTI-INSTITUTIONAL AND MULTI-HISTOLOGY PEDIATRIC-SPECIFIC BRAIN TUMOR SUBREGION SEGMENTATION TOOL: FACILITATING RAPNO-BASED ASSESSMENT OF TREATMENT RESPONSE“. Neuro-Oncology 25, Supplement_1 (01.06.2023): i47. http://dx.doi.org/10.1093/neuonc/noad073.182.
Der volle Inhalt der QuelleAgnihotri, Aditya. „An Efficient and Clinical-Oriented 3D Liver Segmentation Method“. Turkish Journal of Computer and Mathematics Education (TURCOMAT) 10, Nr. 2 (10.09.2019): 1015–21. http://dx.doi.org/10.17762/turcomat.v10i2.13584.
Der volle Inhalt der QuelleQiu, Bingjiang, Jiapan Guo, Joep Kraeima, Haye Hendrik Glas, Weichuan Zhang, Ronald J. H. Borra, Max Johannes Hendrikus Witjes und Peter M. A. van Ooijen. „Recurrent Convolutional Neural Networks for 3D Mandible Segmentation in Computed Tomography“. Journal of Personalized Medicine 11, Nr. 6 (31.05.2021): 492. http://dx.doi.org/10.3390/jpm11060492.
Der volle Inhalt der QuelleKumar, S. Pramod, und Mrityunjaya V. Latte. „Fully Automated Segmentation of Lung Parenchyma Using Break and Repair Strategy“. Journal of Intelligent Systems 28, Nr. 2 (24.04.2019): 275–89. http://dx.doi.org/10.1515/jisys-2017-0020.
Der volle Inhalt der QuelleArsenescu, Tudor, Radu Chifor, Tiberiu Marita, Andrei Santoma, Andrei Lebovici, Daniel Duma, Vitalie Vacaras und Alexandru Florin Badea. „3D Ultrasound Reconstructions of the Carotid Artery and Thyroid Gland Using Artificial-Intelligence-Based Automatic Segmentation—Qualitative and Quantitative Evaluation of the Segmentation Results via Comparison with CT Angiography“. Sensors 23, Nr. 5 (03.03.2023): 2806. http://dx.doi.org/10.3390/s23052806.
Der volle Inhalt der QuelleChen, Junjie, Qian Su, Yunbin Niu, Zongyu Zhang und Jinghao Liu. „A Handheld LiDAR-Based Semantic Automatic Segmentation Method for Complex Railroad Line Model Reconstruction“. Remote Sensing 15, Nr. 18 (13.09.2023): 4504. http://dx.doi.org/10.3390/rs15184504.
Der volle Inhalt der QuelleP, Mathumetha, Sivakumar Rajagopal, Shailly Vaidya und Basim Alhadidi. „Automated Detection of Pneumothorax Using Frontal Chest X-rays“. ECS Transactions 107, Nr. 1 (24.04.2022): 861–72. http://dx.doi.org/10.1149/10701.0861ecst.
Der volle Inhalt der QuelleMihelic, Samuel A., William A. Sikora, Ahmed M. Hassan, Michael R. Williamson, Theresa A. Jones und Andrew K. Dunn. „Segmentation-Less, Automated, Vascular Vectorization“. PLOS Computational Biology 17, Nr. 10 (08.10.2021): e1009451. http://dx.doi.org/10.1371/journal.pcbi.1009451.
Der volle Inhalt der QuelleDury, Richard, Rob Dineen, Anbarasu Lourdusamy und Richard Grundy. „Semi-automated medulloblastoma segmentation and influence of molecular subgroup on segmentation quality“. Neuro-Oncology 21, Supplement_4 (Oktober 2019): iv14. http://dx.doi.org/10.1093/neuonc/noz167.060.
Der volle Inhalt der QuelleYe, Yaojiang, Zixin Luo, Zhengxuan Qiu, Kangyang Cao, Bingsheng Huang, Lei Deng, Weijing Zhang et al. „Radiomics Prediction of Muscle Invasion in Bladder Cancer Using Semi-Automatic Lesion Segmentation of MRI Compared with Manual Segmentation“. Bioengineering 10, Nr. 12 (25.11.2023): 1355. http://dx.doi.org/10.3390/bioengineering10121355.
Der volle Inhalt der QuelleChen, Yen Sheng, Chung Hua Chen, Yuh Ming Chang und Chun Chih Chang. „Comparing Level Set Method and Canny Algorithm for Edge Detection to Tongue Diagnosis in Traditional Chinese Medicine“. Applied Mechanics and Materials 236-237 (November 2012): 783–86. http://dx.doi.org/10.4028/www.scientific.net/amm.236-237.783.
Der volle Inhalt der QuelleMeiburger, Kristen M., Massimo Salvi, Giulia Rotunno, Wolfgang Drexler und Mengyang Liu. „Automatic Segmentation and Classification Methods Using Optical Coherence Tomography Angiography (OCTA): A Review and Handbook“. Applied Sciences 11, Nr. 20 (18.10.2021): 9734. http://dx.doi.org/10.3390/app11209734.
Der volle Inhalt der QuelleYe, Guochang, und Mehmet Kaya. „Automated Cell Foreground–Background Segmentation with Phase-Contrast Microscopy Images: An Alternative to Machine Learning Segmentation Methods with Small-Scale Data“. Bioengineering 9, Nr. 2 (18.02.2022): 81. http://dx.doi.org/10.3390/bioengineering9020081.
Der volle Inhalt der QuelleAbdullah, Bassem A., Akmal A. Younis und Nigel M. John. „Multi-Sectional Views Textural Based SVM for MS Lesion Segmentation in Multi-Channels MRIs“. Open Biomedical Engineering Journal 6, Nr. 1 (09.05.2012): 56–72. http://dx.doi.org/10.2174/1874120701206010056.
Der volle Inhalt der QuellePaing, May Phu, Supan Tungjitkusolmun, Toan Huy Bui, Sarinporn Visitsattapongse und Chuchart Pintavirooj. „Automated Segmentation of Infarct Lesions in T1-Weighted MRI Scans Using Variational Mode Decomposition and Deep Learning“. Sensors 21, Nr. 6 (10.03.2021): 1952. http://dx.doi.org/10.3390/s21061952.
Der volle Inhalt der QuelleGuo, Fan, Xin Zhao, Beiji Zou und Yixiong Liang. „Automatic Retinal Image Registration Using Blood Vessel Segmentation and SIFT Feature“. International Journal of Pattern Recognition and Artificial Intelligence 31, Nr. 11 (11.04.2017): 1757006. http://dx.doi.org/10.1142/s0218001417570063.
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