Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
466-01-013 ISMRM Abstract

Segmenting Rectal Cancer from Multi-Sequence MR Images that Were Inconsistently Labelled

Primary: Body - Pelvis
Secondary: Analysis Methods - Segmentation and Detection
466-01-013 · Rectal Cancer · Tuesday, 12 May, 8:20 AM–9:15 AM · Digital Posters Row G
Keywords: Segmentation Rectal cancer Multi-sequence MRI
Accepted
Rongli Zhang 1, Wen Zhou2, Mohamad Koohi-Moghadam1, zhongbiao xu3, Reza Safdari1, Dariush Lotfi1, Mahmoud Kazemi Haji Abadi1, Ka Chun Lam1, Zhihua Chen4, Qi Yong H Ai1, Kyongtae Ty Bae1
1Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong
2Department of Radiology, Peking University Shenzhen Hospital, Shenzhen, China
3Department of Radiotherapy, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, People's Republic of China, Guangzhou, China
4Department of Radiology, Binhaiwan Central Hospital of Dongguan, China
Presenting Author: Rongli Zhang

Synopsis

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References

1. Xia S, Li Q, Zhu HT, Zhang XY, Shi YJ, Yang D, et al. Fully semantic segmentation for rectal cancer based on post-nCRT MRl modality and deep learning framework. BMC Cancer. 2024;24:315. doi: https://doi.org/10.1186/s12885-024-11997-1 [doi]
2. Gu R, Zhang J, Wang G, Lei W, Song T, Zhang X, et al. Contrastive Semi-Supervised Learning for Domain Adaptive Segmentation Across Similar Anatomical Structures. IEEE Trans Med Imaging. 2023;42:245-56. doi:10.1109/TMI.2022.3209798 [doi]
3. Horvat N, Carlos Tavares Rocha C, Clemente Oliveira B, Petkovska I, Gollub MJ. MRI of rectal cancer: tumor staging, imaging techniques, and management. Radiographics. 2019;39:367-87.doi:10.1148/rg.2019180114 [doi]
4. Chen X, Zhou H-Y, Liu F, Guo J, Wang L, Yu Y. MASS: Modality-collaborative semi-supervised segmentation by exploiting cross-modal consistency from unpaired CT and MRI images. Med Image Anal. 2022;80:102506.doi:10.1016/j.media.2022.102506 [doi]
5. Zhang S, Zhang J, Tian B, Lukasiewicz T, Xu Z. Multi-modal contrastive mutual learning and pseudo-label re-learning for semi-supervised medical image segmentation. Med Image Anal. 2023;83:102656. doi:10.1016/j.media.2022.102656 [doi]
6. Zhou X, Sun Y, Deng M, Chu WCW, Dou Q. Robust semi-supervised multimodal medical image segmentation via cross modality collaboration. International Conference on Medical Image Computing and Computer-Assisted Intervention: Springer; 2024. p. 57-67.doi:https://doi.org/10.1007/978-3-031-72378-0_6 [doi]
7. Wang H, Guo S, Ye J, Deng Z, Cheng J, Li T, et al. SAM-Med3D: A Vision Foundation Model for General-Purpose Segmentation on Volumetric Medical Images. IEEE Transactions on Neural Networks and Learning Systems. 2025.doi: 10.1109/TNNLS.2025.3586694 [doi]

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http://echo.ismrm.org/p/ISMRM2026/466-01-013