Editorial Revisado por pares

Editorial for “Collaborative Learning for Annotation‐Efficient Volumetric MR Image Segmentation”

2024; Wiley; Volume: 60; Issue: 4 Linguagem: Inglês

10.1002/jmri.29212

ISSN

1522-2586

Autores

Mohammad Sabati, Mingrui Yang, Anil Chauhan,

Tópico(s)

Advanced Neural Network Applications

Resumo

Journal of Magnetic Resonance ImagingEarly View Editorial Editorial for "Collaborative Learning for Annotation-Efficient Volumetric MR Image Segmentation" Mohammad Sabati PhD, CPEng, FIEAust, Corresponding Author Mohammad Sabati PhD, CPEng, FIEAust [email protected] orcid.org/0000-0001-9811-6893 Hoglund Biomedical Imaging Center, University of Kansas Medical Center, Kansas City, Kansas, USA Bioengineering Program, School of Engineering, University of Kansas, Lawrence, Kansas, USASearch for more papers by this authorMingrui Yang PhD, Mingrui Yang PhD Department of Biomedical Engineering, Program of Advanced Musculoskeletal Imaging, Cleveland Clinic, Cleveland, Ohio, USASearch for more papers by this authorAnil Chauhan MD, FSAR, FSRU, FAIUM, Anil Chauhan MD, FSAR, FSRU, FAIUM Department of Radiology, University of Kansas Medical Center, Kansas City, Kansas, USASearch for more papers by this author Mohammad Sabati PhD, CPEng, FIEAust, Corresponding Author Mohammad Sabati PhD, CPEng, FIEAust [email protected] orcid.org/0000-0001-9811-6893 Hoglund Biomedical Imaging Center, University of Kansas Medical Center, Kansas City, Kansas, USA Bioengineering Program, School of Engineering, University of Kansas, Lawrence, Kansas, USASearch for more papers by this authorMingrui Yang PhD, Mingrui Yang PhD Department of Biomedical Engineering, Program of Advanced Musculoskeletal Imaging, Cleveland Clinic, Cleveland, Ohio, USASearch for more papers by this authorAnil Chauhan MD, FSAR, FSRU, FAIUM, Anil Chauhan MD, FSAR, FSRU, FAIUM Department of Radiology, University of Kansas Medical Center, Kansas City, Kansas, USASearch for more papers by this author First published: 23 January 2024 https://doi.org/10.1002/jmri.29212 Evidence Level: 5 Technical Efficacy: Stage 1 Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. References 1Chen X, Wang X, Zhang K, et al. Recent advances and clinical applications of deep learning in medical image analysis. Med Image Anal 2022; 79:102444. https://doi.org/10.1016/j.media.2022.102444. 10.1016/j.media.2022.102444 PubMedWeb of Science®Google Scholar 2Kijowski R, Liu F, Caliva F, Pedoia V. Deep learning for lesion detection, progression, and prediction of musculoskeletal disease. J Magn Reson Imaging. 2020; 52(6): 1607–1619. https://doi.org/10.1002/jmri.27001 10.1002/jmri.27001 PubMedWeb of Science®Google Scholar 3Zhou Y, He X, Huang L, Liu L, Zhu F, Cui S, Shao L. Collaborative learning of semi-supervised segmentation and classification for medical images. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 2019, pp. 2074–2083. https://doi.org/10.1109/CVPR.2019.00218. 10.1109/CVPR.2019.00218 Google Scholar 4Wang M, Jiang H, Shi T, Yao YD. SCL-net: Structured collaborative learning for PET/CT based tumor segmentation. IEEE J Biomed Health Inform 2022; 27: 1048-1059. https://doi.org/10.1109/JBHI.2022.3226475. 10.1109/JBHI.2022.3226475 Web of Science®Google Scholar 5Zhou HY, Wang C, Li H, et al. SSMD: Semi-supervised medical image detection with adaptive consistency and heterogeneous perturbation. Med Image Anal 2021; 72:102117. 10.1016/j.media.2021.102117 PubMedWeb of Science®Google Scholar 6Bitarafan A, Nikdan M, Baghshah MS. 3D image segmentation with sparse annotation by self-training and internal registration. IEEE J Biomed Health Inform 2021; 25(7): 2665-2672. https://doi.org/10.1109/JBHI.2020.3038847. 10.1109/JBHI.2020.3038847 PubMedWeb of Science®Google Scholar 7Chartsias A, Papanastasiou G, Wang C, et al. Disentangle, align and fuse for multimodal and semi-supervised image segmentation. IEEE Trans Med Imaging 2021; 40: 781-792. 10.1109/TMI.2020.3036584 PubMedWeb of Science®Google Scholar 8Osman YBM, Li C, Huang W, Wang S. Collaborative learning for annotation-efficient volumetric MR image segmentation. J Magn Reson Imaging. https://doi.org/10.1002/jmri.29194. 10.1002/jmri.29194 Web of Science®Google Scholar 9Xiong Z, Xia Q, Hu Z, et al. A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging. Med Image Anal 2021; 67:101832. 10.1016/j.media.2020.101832 PubMedWeb of Science®Google Scholar 10Litjens G, Toth R, van de Ven W, et al. Evaluation of prostate segmentation algorithms for MRI: The PROMISE12 challenge. Med Image Anal 2014; 18: 359-373. 10.1016/j.media.2013.12.002 PubMedWeb of Science®Google Scholar Early ViewOnline Version of Record before inclusion in an issue ReferencesRelatedInformation

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