为了更精确地对CT图像中的肝脏肿瘤边界进行分割,基于TransUNet分割网络,结合注意力模块(CBAM)以及混合注意力空洞空间金字塔池化模块(HA-ASPP),提出HA-TUNet级联分割网络,在提高卷积核感受野的同时,突出有用特征并抑制不重要特征,分割精度与肿瘤边缘的分割准确度优于改进前的TransUNet网络.基于LiTs公共数据集进行实验,HA-TUNet 级联分割网络在肝脏与肿瘤分割中的Dice相似性系数指标较TransUNet网络分别提高了 3.75％和3.39％,达到95.78％和73.35％,同时豪斯多夫距离95％相比TransUNet分别减少了 0.56 mm和0.48 mm.
HA-TUNet Cascaded Segmentation for Liver Tumor CT Images
In order to more precisely segment the boundaries of liver tumors in CT images,the HA-TUNet cascaded segmentation network was proposed,based on TransUNet segmentation network,and incorporating the Convolutional Block Attention Module(CBAM)as well as the Hybrid Attention-Atrous Spatial Pyramid Pooling module(HA-ASPP).This network is designed to increase the receptive field while highlighting useful features and suppressing unimportant ones,achieving segmentation precision and tu-mor edge accuracy superior to the original TransUNet network.Experiments conducted on the LiTs public dataset show that the HA-TUNet cascaded segmentation network improves the DSC metric for liver and tumor segmentation by 3.75％and 3.39％respec-tively over the TransUNet network,reaching 95.78％and 73.35％.Additionally,the HD95 metric decreased by 0.56 mm and 0.48 mm respectively compared to TransUNet.
medical image segmentationCT imagesliver tumor segmentationcascaded attention network
万方数据
维普
