Hierarchical Convolutional-Deconvolutional Neural Networks for Automatic Liver and Tumor Segmentation

TL;DR

This paper presents a hierarchical deep learning framework using convolutional-deconvolutional neural networks for automatic liver and tumor segmentation in CT images, achieving top performance in the LiTS challenge.

Contribution

The authors introduce a novel hierarchical CDNN-based approach that improves segmentation accuracy by sequentially coarse and fine segmentation stages, with enhanced tumor segmentation using histogram equalization.

Findings

Achieved a DSC of 0.963 for liver segmentation.

Obtained a DSC of 0.657 for tumor segmentation.

Ranked first in liver segmentation, fifth in tumor segmentation, and third in tumor burden estimation.

Abstract

Automatic segmentation of liver and its tumors is an essential step for extracting quantitative imaging biomarkers for accurate tumor detection, diagnosis, prognosis and assessment of tumor response to treatment. MICCAI 2017 Liver Tumor Segmentation Challenge (LiTS) provides a common platform for comparing different automatic algorithms on contrast-enhanced abdominal CT images in tasks including 1) liver segmentation, 2) liver tumor segmentation, and 3) tumor burden estimation. We participate this challenge by developing a hierarchical framework based on deep fully convolutional-deconvolutional neural networks (CDNN). A simple CDNN model is firstly trained to provide a quick but coarse segmentation of the liver on the entire CT volume, then another CDNN is applied to the liver region for fine liver segmentation. At last, the segmented liver region, which is enhanced by histogram equalization, is employed as an additional input to the third CDNN for tumor segmentation. Jaccard distance is used as loss function when training CDNN models to eliminate the need of sample re-weighting. Our framework is trained using the 130 challenge training cases provided by LiTS. The evaluation on the 70 challenge testing cases resulted in a mean Dice Similarity Coefficient (DSC) of 0.963 for liver segmentation, a mean DSC of 0.657 for tumor segmentation, and a root mean square error (RMSE) of 0.017 for tumor burden estimation, which ranked our method in the first, fifth and third place, respectively