Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
507-04-001 / 271-01-041 ISMRM Abstract

Physics-Guided Few-Shot Learnable Active Contour Model for Quantitative Body Composition Analysis on MRI PDFF images

Primary: Body - Body Composition
Secondary: Analysis Methods - Segmentation and Detection
507-04-001 · Advanced Segmentation in MRI Across Scales and Domains · Wednesday, 13 May, 4:00 PM–5:50 PM · Meeting Room 1.40
271-01-041 · ISMRM AMPC Selected Posters · Sunday, 10 May, 7:00 AM–2:00 PM · Traditional Posters
Keywords: Segmentation Magnetic resonance imaging Deep learning Proton density fat fraction (PDFF) Body Composition Analysis
Accepted
Lin Yang1, Chuanli Cheng1, Zhanli Hu2, Xin Liu1, Hairong Zheng3, Chao Zou1
1Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
2Research Center for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
3Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
Presenting Author: Dongnan Zhao

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References

1. Borga M, West J, Bell J D, et al. Advanced body composition assessment: from body mass index to body composition profiling[J]. Journal of Investigative Medicine, 2018, 66(5): 1-9. doi:10.1136/jim-2018-000722 [doi]
2. Chan T F, Vese L A. Active contours without edges[J]. IEEE Transactions on image processing, 2001, 10(2): 266-277. doi:10.1109/83.902291 [doi]
3. Li C, Kao C Y, Gore J C, et al. Minimization of region-scalable fitting energy for image segmentation[J]. IEEE Transactions on Image Processing, 2008, 17(10): 1940-1949. doi:10.1109/TIP.2008.2002304 [doi]
4. Çiçek Ö, Abdulkadir A, Lienkamp S S, et al. 3D U-Net: learning dense volumetric segmentation from sparse annotation[C]//International conference on medical image computing and computer-assisted intervention. Cham: Springer International Publishing, 2016: 424-432. https://doi.org/10.1007/978-3-319-46723-8_49 [doi]
5. Hatamizadeh A, Tang Y, Nath V, et al. Unetr: Transformers for 3d medical image segmentation[C]//Proceedings of the IEEE/CVF winter conference on applications of computer vision. 2022: 574-584. doi:10.1109/WACV51458.2022.00181 [doi]
6. Tang Y, Yang D, Li W, et al. Self-supervised pre-training of swin transformers for 3d medical image analysis[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022: 20730-20740. doi:10.1109/CVPR52688.2022.02007 [doi]

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http://echo.ismrm.org/p/ISMRM2026/507-04-001