基于MAU-Net的CT多器官分割

基于计算机断层扫描(CT)图像的多器官精准分割能够准确诊断病灶, 快速制定治疗计划, 提高临床工作的效率. 传统分割算法针对形变大、体积较小且边缘模糊的器官分割效果相对较差. 本文提出了一种改进的U-Net的医学图像分割网络(MAU-Net), 通过引入两个模块, 旨在实现对多器官的精准分割. 多尺度空洞卷积模块通过不同内核大小实现捕捉目标器官多尺度特征. 动态注意力模块精确提取重要特征实现分支间的权重平衡. 通过消融实验和其他主流网络的对比实验, 验证了MAU-Net的优越性. 相比于传统的U-Net模型, MAU-Net在所有器官上平均Dice相关系数(DSC)提高了3.39%, 平均95%豪斯多夫距离(HD)降低了4.84 mm. MAU-Net在多器官分割任务中展现了出色的鲁棒性和应用潜力, 有助于提高临床工作效率和医疗诊断的准确性.

CT Multi-organ Segmentation Based on MAU-Net

Accurate segmentation of multiple organs based on computerized tomography (CT) images enables the precise diagnosis of lesions, facilitates rapid treatment planning, and improves the efficiency of clinical work. However, traditional segmentation algorithms often struggle with organs that have large deformations, small volumes, and blurry boundaries, resulting in relatively poor segmentation performance. This study proposes an improved U-Net medical image segmentation network called (MAU-Net), which aims to achieve accurate segmentation of multiple organs by introducing two modules. The multi-scale dilated convolution module captures multi-scale features of the target organs using different kernel sizes. The dynamic attention module precisely extracts important features to achieve weight balance between branches. The superiority of MAU-Net is confirmed through ablation experiments and comparative experiments with other mainstream networks. Compared to the traditional U-Net model, MAU-Net achieves an average Dice similarity coefficient (DSC) improvement of 3.39% and an average 95% Hausdorff distance (HD) reduction of 4.84 mm across all organs. MAU-Net demonstrates remarkable robustness and potential for applications in multi-organ segmentation tasks, contributing to improving clinical workflow efficiency and diagnostic accuracy in medical settings.