Boundary-aware geometric Mamba network for breast tumor segmentation in 3D MRI.

Authors

Zhejun Kuang, Yuxuan Zhu, Dawen Sun, Jian Zhao

Published in

Frontiers in oncology. Volume 16. Pages 1972153. Epub Oct 09, 2026.

Abstract

Accurate breast tumor segmentation in Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) is important for reliable diagnosis and treatment planning. However, complex tumor morphology, diverse spatial structures, and indistinct boundaries remain challenging for existing segmentation methods. Convolutional neural network-based methods have limited ability to model long-range dependencies, while Transformer-based methods often introduce substantial computational overhead. Although Mamba-based approaches provide an efficient alternative, conventional scanning strategies may suffer from directional bias and insufficiently capture three-dimensional structural variations.
We propose a Boundary-Aware Geometric Mamba Network (BAGMNet) for three-dimensional breast tumor segmentation. BAGMNet establishes a structural representation learning framework that combines geometric modeling, multi-directional state-space propagation, and boundary-aware feature refinement. The Geometric Mamba Encoder constructs complementary spatial representations through multiple geometric transformations, while the Multi-directional Selective State Space module enhances three-dimensional dependency modeling and alleviates the directional bias of conventional Mamba scanning. The Gated State-space Fusion and Adaptive Skip Recalibration modules further integrate local structural details with global contextual information and enhance boundary-related representations during feature propagation.
Extensive experiments were conducted on the ISPY1 and Yunnan breast MRI datasets. BAGMNet achieved Dice scores of 70.34% and 79.14%, with average surface distances of 9.79 mm and 8.96 mm on the ISPY1 and Yunnan datasets, respectively. Compared with representative convolutional neural network-, Transformer-, and Mamba-based methods, BAGMNet achieved competitive performance in both regional segmentation accuracy and boundary localization.

PMID:
42920584
Bibliographic data and abstract were imported from PubMed on 24 Oct 2026.

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