468-03-016 ISMRM Abstract

BCA-MT: Boundary-aware, Class-distribution-Aligned Mean-Teacher for Semi-supervised Cardiac MRI Segmentation on ACDC

Primary: Analysis Methods - Segmentation and Detection

Secondary: Analysis Methods - Radiomics

468-03-016 · Segmentation for Cardiac Applications · Tuesday, 12 May, 1:40 PM–2:35 PM · Digital Posters Row I

Keywords: Semi-Supervised Learning Mean-Teacher Cardiac MRI segmentation Boundary-aware consistency

Accepted

Pengchen Liang ¹, Zhifeng Chen², Haishan Huang³, Kaiting Wang^4,5, Shiwei Wang^4,5, Xiaoyun Liang¹

¹Institute of Research and Clinical Innovations, Neusoft Medical Systems Co. Ltd, Shanghai, China

²Neusoft Medical Systems Co., Ltd., Hangzhou, China

³School of Software Engineering, Sun Yat-sen University, Zhuhai, China

⁴The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China

⁵The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China

Presenting Author: Pengchen Liang

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References

1. Lu L, Wang T, Lai Z, et al. Uncertainty-aware Pseudo-label and Consistency for Semi-supervised Medical Image Segmentation [J]. Biomedical Signal Processing and Control, 2023, 84: 104607.

2. Wang K, Zhan B, Zu C, et al. Semi-supervised medical image segmentation via a triple-uncertainty guided mean teacher model with contrastive learning [J]. Medical Image Analysis, 2022, 79: 102447.

3. Rayed M E, Zhao L, Yang P, et al. Deep learning for medical image segmentation: State-of-the-art and future trends [J]. Patterns, 2024, 5(7): 100936.

4. Tang C, Zhang J, Wang Y, et al. Semi-supervised medical image segmentation via hard positives oriented contrastive learning [J]. Pattern Recognition, 2024, 146: 110067.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/468-03-016