Browse by author
Lookup NU author(s): Dr Shidong WangORCiD
Full text for this publication is not currently held within this repository. Alternative links are provided below where available.
© 2025 Elsevier Inc.Few-Shot Medical Image Segmentation ( FSMIS ) aims to achieve precise segmentation of different organs using minimal annotated data. Current prototype-based FSMIS methods primarily extract prototypes from support samples through random sampling or local averaging. However, due to the extremely small proportion of boundary features, traditional methods have difficulty generating boundary prototypes, resulting in poorly delineated boundaries in segmentation results. Moreover, their reliance on a single support image for segmenting all query images leads to significant performance degradation when substantial discrepancies exist between support and query images. To address these challenges, we propose an innovative solution namely Boundary-extended Prototypes and Momentum Inference ( BePMI ), which includes two key modules: a Boundary-extended Prototypes ( BePro ) module and a Momentum Inference ( MoIf ) module. BePro constructs boundary prototypes by explicitly clustering the internal and external boundary features to alleviate the problem of boundary ambiguity. MoIf employs the spatial consistency of adjacent slices in 3D medical images to dynamically optimize the prototype representation, thereby reducing the reliance on a single sample. Extensive experiments on three publicly available medical image datasets demonstrate that our method outperforms the state-of-the-art methods. Code is available at https://github.com/xubin471/BePMI .
Author(s): Xu B, Zhu Y, Wang S, Long Y, Zhang H
Publication type: Article
Publication status: Published
Journal: Computer Vision and Image Understanding
Year: 2026
Volume: 263
Print publication date: 01/01/2026
Online publication date: 20/11/2025
Acceptance date: 17/11/2025
ISSN (print): 1077-3142
ISSN (electronic): 1090-235X
Publisher: Academic Press Inc.
URL: https://doi.org/10.1016/j.cviu.2025.104571
DOI: 10.1016/j.cviu.2025.104571
Altmetrics provided by Altmetric
