Sparse Dynamic Volume TransUNet with multi-level edge fusion for brain tumor segmentation

计算机科学 分割 体素 人工智能 编码器 特征(语言学) 模式识别(心理学) GSM演进的增强数据速率 计算机视觉 空间分析 编码(内存) 融合 数学 哲学 语言学 统计 操作系统
作者
Zhiqin Zhu,Mengwei Sun,Guanqiu Qi,Yuanyuan Li,Xinbo Gao,Yü Liu
出处
期刊:Computers in Biology and Medicine [Elsevier BV]
卷期号:172: 108284-108284 被引量:73
标识
DOI:10.1016/j.compbiomed.2024.108284
摘要

3D MRI Brain Tumor Segmentation is of great significance in clinical diagnosis and treatment. Accurate segmentation results are critical for localization and spatial distribution of brain tumors using 3D MRI. However, most existing methods mainly focus on extracting global semantic features from the spatial and depth dimensions of a 3D volume, while ignoring voxel information, inter-layer connections, and detailed features. A 3D brain tumor segmentation network SDV-TUNet (Sparse Dynamic Volume TransUNet) based on an encoder–decoder architecture is proposed to achieve accurate segmentation by effectively combining voxel information, inter-layer feature connections, and intra-axis information. Volumetric data is fed into a 3D network consisting of extended depth modeling for dense prediction by using two modules: sparse dynamic (SD) encoder–decoder module and multi-level edge feature fusion (MEFF) module. The SD encoder–decoder module is utilized to extract global spatial semantic features for brain tumor segmentation, which employs multi-head self-attention and sparse dynamic adaptive fusion in a 3D extended shifted window strategy. In the encoding stage, dynamic perception of regional connections and multi-axis information interactions are realized through local tight correlations and long-range sparse correlations. The MEFF module achieves the fusion of multi-level local edge information in a layer-by-layer incremental manner and connects the fusion to the decoder module through skip connections to enhance the propagation ability of spatial edge information. The proposed method is applied to the BraTS2020 and BraTS2021 benchmarks, and the experimental results show its superior performance compared with state-of-the-art brain tumor segmentation methods. The source codes of the proposed method are available at https://github.com/SunMengw/SDV-TUNet.
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