352-02-006 / 352-02-006 ISMRM Abstract

Automated ESCC Segmentation on Free-Breathing 3D-GRE: A Comparison of nnUNet and UMamba

Primary: Analysis Methods - Segmentation and Detection

Secondary: Body - Chest/Thorax

352-02-006 · Segmentation: Automated, Scalable, and Clinically Oriented MRI Analysis · Monday, 11 May, 1:50 PM–3:26 PM · Power Pitch Theatre 2

Keywords: Esophageal cancer NnU-Net Image segmentation and detection

Accepted

Funing Chu ^1,2, Bingmei Bai^1,2, MENGZHU WANG³, Yue Wu^1,2, Jinrong Qu^1,2

¹Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China

²HNHC Key Laboratory of Oncology Medical Imaging Response Assessment, Zhengzhou, China

³MR Research Collaboration, Siemens Healthineers, Beijing China, China

Presenting Author: Funing Chu

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References

1. Isensee F, Jaeger PF, Kohl SAA, et al. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods. 2021;18:203-211.

2. Ma J, Li F, Wang B. U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation. arXiv preprint arXiv:2403.01791. 2024.

3. Qu J, Zhang H, Wang Z, et al. Comparison between free-breathing radial VIBE on 3-T MRI and endoscopic ultrasound for preoperative T staging of resectable oesophageal cancer, with histopathological correlation. Eur Radiol. 2018;28:780-787.

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http://echo.ismrm.org/p/ISMRM2026/352-02-006