关键词: 肝脏, 病灶, CT图像, 分割算法

Abstract:

Accurate segmentation of liver and lesions is an important premise for computer-aided doctors to diagnose liver cancer and formulate corresponding treatment plans. Aiming at the problem of high-precision segmentation algorithm that the process is complicated and the segmentation needs to be completed in multiple steps, end-to-end U-net segmentation algorithm for predicting liver and lesion areas is proposed. Firstly, it improves the basic U-net model and adds feature reuse ideas to improve the efficiency of the network model for feature utilization. Secondly, the loss function is improved, and the cross entropy and the Dice coefficient are combined. At the same time, the under-division penalty factor is fine-tuned to improve the model effect and the detection ratio of the lesion. Finally, the optimization results are processed before and after the addition. Experiments in the MICCAI_2017_LiTS dataset show that the end-to-end network can still achieve the algorithmic accuracy of complex networks and multi-step segmentation networks.

Key words: liver, lesion, CT image, segmentation algorithm