StAC-DA: Structure aware cross-modality domain adaptation framework with image and feature-level adaptation for medical image segmentation.

Autor: Baldeon-Calisto M; Departamento de Ingeniería Industrial, Colegio de Ciencias e Ingeniería, Instituto de Innovación en Productividad y Logística CATENA-USFQ, Universidad San Francisco de Quito, Quito, Ecuador., Lai-Yuen SK; Department of Industrial and Management Systems, University of South Florida, Tampa, FL, USA., Puente-Mejia B; Departamento de Ingeniería Industrial, Colegio de Ciencias e Ingeniería, Instituto de Innovación en Productividad y Logística CATENA-USFQ, Universidad San Francisco de Quito, Quito, Ecuador.

Publikováno v: Digital health [Digit Health] 2024 Sep 02; Vol. 10, pp. 20552076241277440. Date of Electronic Publication: 2024 Sep 02 (Print Publication: 2024).

CrossMoDA 2021 challenge: Benchmark of cross-modality domain adaptation techniques for vestibular schwannoma and cochlea segmentation.

Autor: Dorent R; School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom. Electronic address: reuben.dorent@kcl.ac.uk., Kujawa A; School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom., Ivory M; School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom., Bakas S; Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, USA; Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA., Rieke N; NVIDIA, Germany., Joutard S; School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom., Glocker B; Department of Computing, Imperial College London, Department of Computing, London, United Kingdom., Cardoso J; School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom., Modat M; School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom., Batmanghelich K; Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA., Belkov A; Moscow Institute of Physics and Technology, Moscow, Russia., Calisto MB; Universidad San Francisco de Quito, Quito, Ecuador., Choi JW; Department of Radiology, Armed Forces Yangju Hospital, Yangju, Republic of Korea., Dawant BM; Vanderbilt University, Nashville, USA., Dong H; Center for Data Science, Peking University, Beijing, China., Escalera S; Artificial Intelligence in Medicine Lab (BCN-AIM) and Human Behavior Analysis Lab (HuPBA), Universitat de Barcelona, Barcelona, Spain., Fan Y; Vanderbilt University, Nashville, USA., Hansen L; Institute of Medical Informatics, Universität zu Lübeck, Germany., Heinrich MP; Institute of Medical Informatics, Universität zu Lübeck, Germany., Joshi S; Artificial Intelligence in Medicine Lab (BCN-AIM) and Human Behavior Analysis Lab (HuPBA), Universitat de Barcelona, Barcelona, Spain., Kashtanova V; Inria, Université Côte d'Azur, Sophia Antipolis, France., Kim HG; School of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea., Kondo S; Muroran Institute of Technology, Muroran, Japan., Kruse CN; Institute of Medical Informatics, Universität zu Lübeck, Germany., Lai-Yuen SK; University of South Florida, Tampa, USA., Li H; Vanderbilt University, Nashville, USA., Liu H; Vanderbilt University, Nashville, USA., Ly B; Inria, Université Côte d'Azur, Sophia Antipolis, France., Oguz I; Vanderbilt University, Nashville, USA., Shin H; School of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea., Shirokikh B; Skolkovo Institute of Science and Technology, Moscow, Russia; Artificial Intelligence Research Institute (AIRI), Moscow, Russia., Su Z; University of Liverpool, Liverpool, United Kingdom; School of Advanced Technology, Xi'an Jiaotong-Liverpool University, Suzhou, China., Wang G; School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China., Wu J; School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China., Xu Y; Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA., Yao K; University of Liverpool, Liverpool, United Kingdom; School of Advanced Technology, Xi'an Jiaotong-Liverpool University, Suzhou, China., Zhang L; Center for Data Science, Peking University, Beijing, China., Ourselin S; School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom., Shapey J; School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom; Department of Neurosurgery, King's College Hospital, London, United Kingdom., Vercauteren T; School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.

Publikováno v: Medical image analysis [Med Image Anal] 2023 Jan; Vol. 83, pp. 102628. Date of Electronic Publication: 2022 Sep 21.

AdaEn-Net: An ensemble of adaptive 2D-3D Fully Convolutional Networks for medical image segmentation.

Autor: Baldeon Calisto M; University of South Florida, 4202 E. Fowler Avenue, Tampa, FL 33620, USA., Lai-Yuen SK; University of South Florida, 4202 E. Fowler Avenue, Tampa, FL 33620, USA. Electronic address: laiyuen@usf.edu.

Publikováno v: Neural networks : the official journal of the International Neural Network Society [Neural Netw] 2020 Jun; Vol. 126, pp. 76-94. Date of Electronic Publication: 2020 Mar 10.

MRI-based segmentation of pubic bone for evaluation of pelvic organ prolapse.

Autor: Onal S, Lai-Yuen SK, Bao P, Weitzenfeld A, Hart S

Publikováno v: IEEE journal of biomedical and health informatics [IEEE J Biomed Health Inform] 2014 Jul; Vol. 18 (4), pp. 1370-8.