Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
368-02-014 ISMRM Abstract

Leveraging Organ Co-Occurrence Loss to Enhance Multi-Organ Segmentation in MRI

Primary: Analysis Methods - Segmentation and Detection
Secondary: Analysis Methods - Software Tools
368-02-014 · Segmentation for Musculoskeletal MRI Applications · Monday, 11 May, 9:15 AM–10:10 AM · Digital Posters Row I
Keywords: T1 Multi-Organ Segmentation Organ co-occurrence loss U-Net T2
Accepted
Qianqian Qi 1, Runsheng Chang2, Yanan Wu3, Xiaoyun Liang4
1Institute of Research and Clinical Innovations, Neusoft Medical Systems Co. Ltd, Shenyang, China
2College of Basic Medical Sciences, ChinaMedical University, Shenyang, China
3School of Health Management, China Medical University, Shenyang, China
4Institute of Research and Clinical Innovations, Neusoft Medical Systems Co. Ltd, Shanghai, China
Presenting Author: Qianqian Qi

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References

1. Li X, Qin X, Huang C, et al. SUnet: A multi-organ segmentation network based on multiple attention [J]. Computers in Biology and Medicine, 2023, 167: 107596. doi: https://doi.org/10.1016/j.compbiomed.2023.107596 [doi]
2. Bobo M F, Bao S, Huo Y, et al. Fully convolutional neural networks improve abdominal organ segmentation[C]//Proceedings of SPIE--the International Society for Optical Engineering. 2018, 10574: 105742V. doi: 10.1117/12.2293751 [doi]
3. Kupelian P, Sonke J J. Magnetic resonance–guided adaptive radiotherapy: a solution to the future[C]//Seminars in radiation oncology. WB Saunders, 2014, 24(3): 227-232. doi: https://doi.org/10.1016/j.semradonc.2014.02.013 [doi]
4. Jiang J, Veeraraghavan H. Unified cross-modality feature disentangler for unsupervised multi-domain MRI abdomen organs segmentation[C]//International conference on medical image computing and computer-assisted intervention. Cham: Springer International Publishing, 2020: 347-358.
5. Zhao X, Huang M, Li L, et al. Multi-to-binary network (MTBNet) for automated multi-organ segmentation on multi-sequence abdominal MRI images [J]. Physics in Medicine & Biology, 2020, 65(16): 165013. doi: 10.1088/1361-6560/ab9453 [doi]
6. Fu H, Zhang J, Li B, et al. Abdominal multi-organ segmentation in Multi-sequence MRIs based on visual attention guided network and knowledge distillation [J]. Physica Medica, 2024, 122: 103385. doi: https://doi.org/10.1016/j.ejmp.2024.103385 [doi]

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http://echo.ismrm.org/p/ISMRM2026/368-02-014