Computer-aided liver tumor segmentation can effectively reduce the workload of doctors and improve the success rate of surgery, and it has important clinical diagnosis and treatment value. Meanwhile, recently proposed U-Net model has achieved great success in the field of medical image segmentation. To obtain more accurate liver tumor segmentation results, this paper proposed an improved U-net model, i.e., cascaded separable and dilated residual U-Net (CSDResU-Net), for this medical application. CSDResU-Net utilizes cascade operation to solve the problem of unbalanced data in tumor segmentation due to the small proportion of tumors in the whole image. Besides, residual unit, depthwise separable convolution and dilated convolution are integrated into a single network to increase the convolution kernel receptive field, which can quickly extract more discriminative liver image features and lead to the performance improvement of liver tumor segmentation. Experimental results on the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) liver tumor segmentation (LiTS) benchmark dataset show that CSDResU-Net is relative to the baseline. The method improves the performance of the Dice coefficient by 1.3%, and at the same time proves that different void ratios have a greater impact on the performance of the segmentation network.
YU Qun, ZHANG Jianxin, WEI Xiaopeng, ZHANG Qiang
. Cascaded Separable and Dilated Residual U-Net for Liver Tumor Segmentation[J]. Journal of Applied Sciences, 2021
, 39(3)
: 378
-377
.
DOI: 10.3969/j.issn.0255-8297.2021.03.004
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