Automatic liver tumor segmentation in follow-up CT studies using Convolutional Neural Networks

We present a new, fully automatic algorithm for liver tumors segmentation in follow-up CT studies. The inputs are a baseline CT scan and a delineation of the tumors in it and a follow-up scan; the outputs are the tumors delineations in the follow-up CT scan. The algorithm consists of four steps: 1) deformable registration of the baseline scan and tumors delineations to the followup CT scan; 2) automatic segmentation of the liver; 3) training a Convolutional Neural Network (CNN) as a voxel classifier on all baseline; 4) segmentation of the tumor in the follow-up study with the learned classifier. The main novelty of our method is the combination of follow-up based detection with CNN-based segmentation. Our experimental results on 67 tumors from 21 patients with ground-truth segmentations approved by a radiologist yield an average overlap error of 16.26% (std=10.33).