Automatic Segmentation of Liver Metastases Based on a Deep Learning: Assessment of Tumor Treatment Response According to the RECIST 1.1 Criteria

This retrospective study aims to develop an automated algorithm for segmentation of liver metastases based on a deep learning method and assess its efficacy for treatment response assessment according to the RECIST 1.1 criteria. One hundred and sixteen treated patients with clinically confirmed liver metastases were enrolled. A 3D U-Net algorithm was trained for automated liver metastases segmentation and treatment response assessment. The results demonstrated that the automated liver metastases segmentation was capable of evaluating treatment response, with comparable results to the junior radiologist and superior to that of the fellow radiologist.