Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks

TLDR

Automatic segmentation of the liver and hepatic lesions is an important step toward deriving quantitative biomarkers for accurate clinical diagnosis and computer‑aided decision support systems. The study proposes a cascaded fully convolutional neural network approach to automatically segment liver and lesions in CT and MRI abdomen images. The method trains two FCNs in cascade: the first segments the liver to define a region of interest, and the second segments lesions within that ROI, using a dataset of 100 abdominal CT tumor volumes. Validation on additional datasets shows Dice scores above 94 % for liver segmentation with computation times under 100 s per volume, and the approach remains robust on 38 MRI tumor volumes and the public 3DIRCAD dataset.

Abstract

Automatic segmentation of the liver and hepatic lesions is an important step towards deriving quantitative biomarkers for accurate clinical diagnosis and computer-aided decision support systems. This paper presents a method to automatically segment liver and lesions in CT and MRI abdomen images using cascaded fully convolutional neural networks (CFCNs) enabling the segmentation of a large-scale medical trial or quantitative image analysis. We train and cascade two FCNs for a combined segmentation of the liver and its lesions. In the first step, we train a FCN to segment the liver as ROI input for a second FCN. The second FCN solely segments lesions within the predicted liver ROIs of step 1. CFCN models were trained on an abdominal CT dataset comprising 100 hepatic tumor volumes. Validations on further datasets show that CFCN-based semantic liver and lesion segmentation achieves Dice scores over 94% for liver with computation times below 100s per volume. We further experimentally demonstrate the robustness of the proposed method on an 38 MRI liver tumor volumes and the public 3DIRCAD dataset.