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

TL;DR

This paper introduces a cascaded fully convolutional neural network approach for automatic segmentation of liver and tumors in CT and MRI images, achieving high accuracy and robustness for clinical and research applications.

Contribution

It proposes a novel cascaded FCN framework for joint liver and lesion segmentation, trained on large datasets, with demonstrated high accuracy and efficiency.

Findings

Dice score over 94% for liver segmentation

Segmentation time below 100 seconds per volume

Robust performance on MRI and public datasets

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.