Multi-modal brain tumor MRI segmentation based on modality reliability and deformable cross-scale co-attention(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2026年第7期
- Page:
- 930-938
- Research Field:
- 医学影像物理
- Publishing date:
Info
- Title:
- Multi-modal brain tumor MRI segmentation based on modality reliability and deformable cross-scale co-attention
- Author(s):
- SUO Guodong1; 2; ZHOU Yuee1; 2; JIN Fengqing1; 2; YANG Jianlan2
- 1. School of Medical Information Engineering, Gansu University of Chinese Medicine, Lanzhou 730000, China 2. Quanzhou Orthopedic-Traumatological Hospital, Quanzhou 362000, China
- Keywords:
- Keywords: brain tumor image segmentation modality reliability-aware fusion deformable cross-scale co-attention uncertainty-guided deep supervision
- PACS:
- R318
- DOI:
- DOI:10.3969/j.issn.1005-202X.2026.07.014
- Abstract:
- Abstract: Objective To propose a three-dimensional (3D) multi-modal brain tumor segmentation network built upon the U-Net backbone implemented in the Medical Imaging Segmentation Toolkit, which integrates modality reliability and deformable cross-scale co-attention. Methods Three lightweight improvements were designed for the network. Specifically, a modality reliability-aware fusion module was embedded at the input stage to adaptively suppress low-quality modality noise. A deformable cross-scale co-attention module was introduced within the decoder to enhance the representation of tumor boundaries and structurally deformed regions. An uncertainty-guided deep supervision module was used in the deep supervision branch to improve the optimization performance for small lesions and ambiguous regions. Results The proposed method was validated on the BraTS2021 dataset and compared with several 3D segmentation baseline models. Results showed that the proposed model achieved optimal performance across whole tumor, tumor core, and enhancing tumor subregions, with Dice similarity coefficients of 0.921, 0.895, and 0.891, respectively, and a mean 95% Hausdorff distance reaching 4.122 mm, which outperformed the other models. Conclusion The proposed 3D multi-modal brain tumor segmentation network effectively enhances multi-modal feature fusion, cross-scale feature alignment, and deep supervision optimization, while maintaining a lightweight architecture. This study provides a robust and fine-grained solution for automated brain tumor segmentation from MRI images.
Last Update: 2026-07-22