Multi-Modal Brain Tumor Segmentation via 3D Multi-Scale Self-attention and Cross-attention

• URL: http://arxiv.org/abs/2504.09088v1
• Date: Sat, 12 Apr 2025 05:53:59 GMT
• Title: Multi-Modal Brain Tumor Segmentation via 3D Multi-Scale Self-attention and Cross-attention
• Authors: Yonghao Huang, Leiting Chen, Chuan Zhou,
• Abstract summary: Introducing Transformer brings long-range dependent information modeling ability in 3D medical images to hybrid models via the self-attention mechanism.<n>We propose a CNN-Transformer hybrid 3D medical image segmentation model, named TMA-TransBTS, based on an encoder-decoder structure.
• Score: 4.076237636695921
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Due to the success of CNN-based and Transformer-based models in various computer vision tasks, recent works study the applicability of CNN-Transformer hybrid architecture models in 3D multi-modality medical segmentation tasks. Introducing Transformer brings long-range dependent information modeling ability in 3D medical images to hybrid models via the self-attention mechanism. However, these models usually employ fixed receptive fields of 3D volumetric features within each self-attention layer, ignoring the multi-scale volumetric lesion features. To address this issue, we propose a CNN-Transformer hybrid 3D medical image segmentation model, named TMA-TransBTS, based on an encoder-decoder structure. TMA-TransBTS realizes simultaneous extraction of multi-scale 3D features and modeling of long-distance dependencies by multi-scale division and aggregation of 3D tokens in a self-attention layer. Furthermore, TMA-TransBTS proposes a 3D multi-scale cross-attention module to establish a link between the encoder and the decoder for extracting rich volume representations by exploiting the mutual attention mechanism of cross-attention and multi-scale aggregation of 3D tokens. Extensive experimental results on three public 3D medical segmentation datasets show that TMA-TransBTS achieves higher averaged segmentation results than previous state-of-the-art CNN-based 3D methods and CNN-Transform hybrid 3D methods for the segmentation of 3D multi-modality brain tumors.

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