Deep Learning for Automated Segmentation of Liver Lesions at CT in Patients with Colorectal Cancer Liver Metastases

To evaluate the performance, agreement, and efficiency of a fully convolutional network (FCN) for liver lesion detection and segmentation at CT examinations in patients with colorectal liver metastases (CLMs). This retrospective study evaluated an automated method using an FCN that was trained, validated, and tested with 115, 15, and 26 contrast material–enhanced CT examinations containing 261, 22, and 105 lesions, respectively. Manual detection and segmentation by a radiologist was the reference standard. Performance of fully automated and user-corrected segmentations was compared with that of manual segmentations. The interuser agreement and interaction time of manual and user-corrected segmentations were assessed. Analyses included sensitivity and positive predictive value of detection, segmentation accuracy, Cohen κ, Bland-Altman analyses, and analysis of variance. Automated detection and segmentation of CLM by using deep learning with convolutional neural networks, when manually corrected, improved efficiency but did not substantially change agreement on volumetric measurements. Supplemental material is available for this article. A deep learning method shows promise for facilitating detection and segmentation of colorectal liver metastases; user correction of three-dimensional automated segmentations can generally resolve deficiencies of fully automated segmentation for small metastases and is faster than manual three-dimensional segmentation. Per-lesion sensitivity for lesions smaller than 10 mm was very low with automated segmentation (0.10) but was higher for user-corrected segmentation (0.30–0.57) and manual segmentation (0.58–0.70).