中圖分類號：TTP391P 文獻標志碼：A DOI: 10.16157/j.issn.0258-7998.256494
中文引用格式： 方云翔，王路路，李英娜，等. 融合多模態(tài)分組特征處理模塊的腦腫瘤分割方法[J]. 電子技術應用，2025，51(11)：46-52.
英文引用格式： Fang Yunxiang，Wang Lulu，Li Yingna，et al. A brain tumor segmentation method with multi-modal grouped feature processing module[J]. Application of Electronic Technique，2025，51(11)：46-52.

A brain tumor segmentation method with multi-modal grouped feature processing module

Abstract： Current brain tumor segmentation models typically concatenate multi-modal MRI images along the channel dimension, failing to fully exploit their multi-modal information. To address this limitation, this paper proposes MGF-UX Net, a model based on 3D UX-Net that integrates a multi-modal grouped feature processing module. The model consists of two key components: a modality feature extraction module and a modality feature fusion module. The feature extraction module employs specialized networks tailored to the characteristics of four MRI modalities to maximize the retention of effective features. The feature fusion module, guided by prior knowledge, groups the four modalities into two sets and applies a Modality-Grouped Cross-Attention (MGCA) mechanism for deep feature fusion. Experimental results on the BraTS2021 dataset demonstrate that MGF-UX Net achieves an average DSC of 91.01% and an HD95 of 4.975 mm, outperforming existing models in brain tumor segmentation tasks.

Key words : brain tumor segmentation；U-Net；MRI；multimodal medical imaging