Artigos de revistas sobre o tema "Automated Segmentation Method"
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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 fonteYang, Xin, Chaoyue Liu, Hung Le Minh, Zhiwei Wang, Aichi Chien e Kwang-Ting (Tim) Cheng. "An automated method for accurate vessel segmentation". Physics in Medicine and Biology 62, n.º 9 (6 de abril de 2017): 3757–78. http://dx.doi.org/10.1088/1361-6560/aa6418.
Texto completo da fonteJaware, Tushar H., K. B. Khanchandani e Anita Zurani. "An Accurate Automated Local Similarity Factor-Based Neural Tree Approach toward Tissue Segmentation of Newborn Brain MRI". American Journal of Perinatology 36, n.º 11 (15 de dezembro de 2018): 1157–70. http://dx.doi.org/10.1055/s-0038-1675375.
Texto completo da fonteHalawa, Abdelrahman, Shehab Gamalel-Din e Abdurrahman Nasr. "EXPLOITING BERT FOR MALFORMED SEGMENTATION DETECTION TO IMPROVE SCIENTIFIC WRITINGS". Applied Computer Science 19, n.º 2 (30 de junho de 2023): 126–41. http://dx.doi.org/10.35784/acs-2023-20.
Texto completo da fonteBowes, Michael Antony, Gwenael Alain Guillard, Graham Richard Vincent, Alan Donald Brett, Christopher Brian Hartley Wolstenholme e 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, n.º 2 (15 de abril de 2019): 282–89. http://dx.doi.org/10.3899/jrheum.180541.
Texto completo da fontePociask, Elżbieta, Krzysztof Piotr Malinowski, Magdalena Ślęzak, Joanna Jaworek-Korjakowska, Wojciech Wojakowski e Tomasz Roleder. "Fully Automated Lumen Segmentation Method for Intracoronary Optical Coherence Tomography". Journal of Healthcare Engineering 2018 (26 de dezembro de 2018): 1–13. http://dx.doi.org/10.1155/2018/1414076.
Texto completo da fonteMASSOPTIER, LAURENT, AVISHKAR MISRA, ARCOT SOWMYA e 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, n.º 04 (outubro de 2011): 509–29. http://dx.doi.org/10.1142/s0219467811004202.
Texto completo da fonteBouzid-Daho, Abdellatif, Naima Sofi, Schahrazad Soltane e Patrick Siarry. "Automated detection in microscopic images using segmentation". Brazilian Journal of Technology 7, n.º 2 (30 de abril de 2024): e69317. http://dx.doi.org/10.38152/bjtv7n2-003.
Texto completo da fonteSun, Yusen, Xingji Jin, Timo Pukkala e Fengri Li. "A Comparison of Four Methods for Automatic Delineation of Tree Stands from Grids of LiDAR Metrics". Remote Sensing 14, n.º 24 (7 de dezembro de 2022): 6192. http://dx.doi.org/10.3390/rs14246192.
Texto completo da fonteXiong, Hui, Laith R. Sultan, Theodore W. Cary, Susan M. Schultz, Ghizlane Bouzghar e Chandra M. Sehgal. "The diagnostic performance of leak-plugging automated segmentation versus manual tracing of breast lesions on ultrasound images". Ultrasound 25, n.º 2 (25 de janeiro de 2017): 98–106. http://dx.doi.org/10.1177/1742271x17690425.
Texto completo da fonteGolkar, Ehsan, Hossein Rabbani e Ashrani Aizzuddin Abd. Rahni. "Inter-subject Registration-based Segmentation of Thoracic-Abdominal Organs in 4 Dimensional Magnetic Resonance Imaging". Jurnal Kejuruteraan 33, n.º 4 (30 de novembro de 2021): 1045–51. http://dx.doi.org/10.17576/jkukm-2021-33(4)-26.
Texto completo da fonteTran, Carol, Orit Glenn, Christopher Hess e Andreas Rauschecker. "4252 Automated Fetal Brain Volumetry on Clinical Fetal MRI Using Convolutional Neural Network". Journal of Clinical and Translational Science 4, s1 (junho de 2020): 45–46. http://dx.doi.org/10.1017/cts.2020.169.
Texto completo da fonteJin, Felix Q., Anna E. Knight, Adela R. Cardones, Kathryn R. Nightingale e Mark L. Palmeri. "Semi-automated weak annotation for deep neural network skin thickness measurement". Ultrasonic Imaging 43, n.º 4 (11 de maio de 2021): 167–74. http://dx.doi.org/10.1177/01617346211014138.
Texto completo da fonteJiang, Huiyan, Shaojie Li e Siqi Li. "Registration-Based Organ Positioning and Joint Segmentation Method for Liver and Tumor Segmentation". BioMed Research International 2018 (24 de setembro de 2018): 1–11. http://dx.doi.org/10.1155/2018/8536854.
Texto completo da fonteChoi, Woorim, Chul-Ho Kim, Hyein Yoo, Hee Rim Yun, Da-Wit Kim e 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, n.º 5 (maio de 2024): e079417. http://dx.doi.org/10.1136/bmjopen-2023-079417.
Texto completo da fonteLee, 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, n.º 6_Supplement (22 de março de 2024): 2595. http://dx.doi.org/10.1158/1538-7445.am2024-2595.
Texto completo da fonteZemborain, Zane Zenon, Matias Soifer, Nadim S. Azar, Sofia Murillo, Hazem M. Mousa, Victor L. Perez e 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, n.º 10 (7 de julho de 2023): 1309–19. http://dx.doi.org/10.1097/ico.0000000000003319.
Texto completo da fonteMoëll, Mattias K., e Lloyd A. Donaldson. "COMPARISON OF SEGMENTATION METHODS FOR DIGITAL IMAGE ANALYSIS OF CONFOCAL MICROSCOPE IMAGES TO MEASURE TRACHEID CELL DIMENSIONS". IAWA Journal 22, n.º 3 (2001): 267–88. http://dx.doi.org/10.1163/22941932-90000284.
Texto completo da fonteYu, Zechen, Zhongping Chen, Yang Yu, Haichen Zhu, Dan Tong e Yang Chen. "An automated ASPECTS method with atlas-based segmentation". Computer Methods and Programs in Biomedicine 210 (outubro de 2021): 106376. http://dx.doi.org/10.1016/j.cmpb.2021.106376.
Texto completo da fonteWeiwei, Xing, Wang Weiqiang, Bao Peng, Sun Liya e Tong Leiming. "A novel method for automated human behavior segmentation". Computer Animation and Virtual Worlds 27, n.º 5 (12 de abril de 2016): 501–14. http://dx.doi.org/10.1002/cav.1690.
Texto completo da fonteWan, Guo Chun, Meng Meng Li, He Xu, Wen Hao Kang, Jin Wen Rui e Mei Song Tong. "XFinger-Net: Pixel-Wise Segmentation Method for Partially Defective Fingerprint Based on Attention Gates and U-Net". Sensors 20, n.º 16 (10 de agosto de 2020): 4473. http://dx.doi.org/10.3390/s20164473.
Texto completo da fonteWang, Yuliang, Tongda Lu, Xiaolai Li, Shuai Ren e Shusheng Bi. "Robust nanobubble and nanodroplet segmentation in atomic force microscope images using the spherical Hough transform". Beilstein Journal of Nanotechnology 8 (1 de dezembro de 2017): 2572–82. http://dx.doi.org/10.3762/bjnano.8.257.
Texto completo da fonteMukondiwa, Daisy Thembelihle, YongTao Shi e Chao Gao. "A Prostate Boundary Localization and Edge Denoising Algorithm". East African Journal of Information Technology 7, n.º 1 (30 de abril de 2024): 108–20. http://dx.doi.org/10.37284/eajit.7.1.1900.
Texto completo da fonteIglesias-Rey, Sara, Felipe Antunes-Santos, Cathleen Hagemann, David Gómez-Cabrero, Humberto Bustince, Rickie Patani, Andrea Serio, Bernard De Baets e Carlos Lopez-Molina. "Unsupervised Cell Segmentation and Labelling in Neural Tissue Images". Applied Sciences 11, n.º 9 (21 de abril de 2021): 3733. http://dx.doi.org/10.3390/app11093733.
Texto completo da fonteArafati, Arghavan, Daisuke Morisawa, Michael R. Avendi, M. Reza Amini, Ramin A. Assadi, Hamid Jafarkhani e 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, n.º 169 (agosto de 2020): 20200267. http://dx.doi.org/10.1098/rsif.2020.0267.
Texto completo da fonteKazerooni, 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 (1 de junho de 2023): i47. http://dx.doi.org/10.1093/neuonc/noad073.182.
Texto completo da fonteAgnihotri, Aditya. "An Efficient and Clinical-Oriented 3D Liver Segmentation Method". Turkish Journal of Computer and Mathematics Education (TURCOMAT) 10, n.º 2 (10 de setembro de 2019): 1015–21. http://dx.doi.org/10.17762/turcomat.v10i2.13584.
Texto completo da fonteQiu, Bingjiang, Jiapan Guo, Joep Kraeima, Haye Hendrik Glas, Weichuan Zhang, Ronald J. H. Borra, Max Johannes Hendrikus Witjes e Peter M. A. van Ooijen. "Recurrent Convolutional Neural Networks for 3D Mandible Segmentation in Computed Tomography". Journal of Personalized Medicine 11, n.º 6 (31 de maio de 2021): 492. http://dx.doi.org/10.3390/jpm11060492.
Texto completo da fonteKumar, S. Pramod, e Mrityunjaya V. Latte. "Fully Automated Segmentation of Lung Parenchyma Using Break and Repair Strategy". Journal of Intelligent Systems 28, n.º 2 (24 de abril de 2019): 275–89. http://dx.doi.org/10.1515/jisys-2017-0020.
Texto completo da fonteArsenescu, Tudor, Radu Chifor, Tiberiu Marita, Andrei Santoma, Andrei Lebovici, Daniel Duma, Vitalie Vacaras e 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, n.º 5 (3 de março de 2023): 2806. http://dx.doi.org/10.3390/s23052806.
Texto completo da fonteChen, Junjie, Qian Su, Yunbin Niu, Zongyu Zhang e Jinghao Liu. "A Handheld LiDAR-Based Semantic Automatic Segmentation Method for Complex Railroad Line Model Reconstruction". Remote Sensing 15, n.º 18 (13 de setembro de 2023): 4504. http://dx.doi.org/10.3390/rs15184504.
Texto completo da fonteP, Mathumetha, Sivakumar Rajagopal, Shailly Vaidya e Basim Alhadidi. "Automated Detection of Pneumothorax Using Frontal Chest X-rays". ECS Transactions 107, n.º 1 (24 de abril de 2022): 861–72. http://dx.doi.org/10.1149/10701.0861ecst.
Texto completo da fonteMihelic, Samuel A., William A. Sikora, Ahmed M. Hassan, Michael R. Williamson, Theresa A. Jones e Andrew K. Dunn. "Segmentation-Less, Automated, Vascular Vectorization". PLOS Computational Biology 17, n.º 10 (8 de outubro de 2021): e1009451. http://dx.doi.org/10.1371/journal.pcbi.1009451.
Texto completo da fonteDury, Richard, Rob Dineen, Anbarasu Lourdusamy e Richard Grundy. "Semi-automated medulloblastoma segmentation and influence of molecular subgroup on segmentation quality". Neuro-Oncology 21, Supplement_4 (outubro de 2019): iv14. http://dx.doi.org/10.1093/neuonc/noz167.060.
Texto completo da fonteYe, 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, n.º 12 (25 de novembro de 2023): 1355. http://dx.doi.org/10.3390/bioengineering10121355.
Texto completo da fonteChen, Yen Sheng, Chung Hua Chen, Yuh Ming Chang e 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 (novembro de 2012): 783–86. http://dx.doi.org/10.4028/www.scientific.net/amm.236-237.783.
Texto completo da fonteMeiburger, Kristen M., Massimo Salvi, Giulia Rotunno, Wolfgang Drexler e Mengyang Liu. "Automatic Segmentation and Classification Methods Using Optical Coherence Tomography Angiography (OCTA): A Review and Handbook". Applied Sciences 11, n.º 20 (18 de outubro de 2021): 9734. http://dx.doi.org/10.3390/app11209734.
Texto completo da fonteYe, Guochang, e 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, n.º 2 (18 de fevereiro de 2022): 81. http://dx.doi.org/10.3390/bioengineering9020081.
Texto completo da fonteAbdullah, Bassem A., Akmal A. Younis e Nigel M. John. "Multi-Sectional Views Textural Based SVM for MS Lesion Segmentation in Multi-Channels MRIs". Open Biomedical Engineering Journal 6, n.º 1 (9 de maio de 2012): 56–72. http://dx.doi.org/10.2174/1874120701206010056.
Texto completo da fontePaing, May Phu, Supan Tungjitkusolmun, Toan Huy Bui, Sarinporn Visitsattapongse e Chuchart Pintavirooj. "Automated Segmentation of Infarct Lesions in T1-Weighted MRI Scans Using Variational Mode Decomposition and Deep Learning". Sensors 21, n.º 6 (10 de março de 2021): 1952. http://dx.doi.org/10.3390/s21061952.
Texto completo da fonteGuo, Fan, Xin Zhao, Beiji Zou e Yixiong Liang. "Automatic Retinal Image Registration Using Blood Vessel Segmentation and SIFT Feature". International Journal of Pattern Recognition and Artificial Intelligence 31, n.º 11 (11 de abril de 2017): 1757006. http://dx.doi.org/10.1142/s0218001417570063.
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