relation: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/3984/
title: Liver Segmentation on a Variety of Computed Tomography (CT) Images Based on Convolutional Neural Networks Combined with Connected Components
creator: Hoang, Hong Son
creator: Pham, Cam Phuong
creator: Walsum, Theo van
creator: Luu, Manh Ha
subject: Electronics and Computer Engineering
description: Liver segmentation is relevant for several clinical applications. Automatic liver segmentation using convolutional neural networks (CNNs) has been recently investigated. In this paper, we propose a new approach of combining a largest connected component (LCC) algorithm, as a post-processing step, with CNN approaches to improve liver segmentation accuracy. Specifically, in this study, the algorithm is combined with three well-known CNNs for liver segmentation: FCN-CRF, DRIU and V-net. We perform the experiment on a variety of liver CT images, ranging from non-contrast enhanced CT images to low-dose contrast enhanced CT images. The methods are evaluated using Dice score, Haudorff distance, mean surface distance, and false positive rate between the liver segmentation and the ground truth. The quantitative results demonstrate that the LCC algorithm statistically significantly improves results of the liver segmentation on non-contrast enhanced and low-dose images for all three CNNs. The combination with V-net shows the best performance in Dice score (higher than 90%), while the DRIU network achieves the smallest computation time (2 to 6 seconds) for a single segmentation on average. The source code of this study is publicly available at https://github.com/kennyha85/Liver-segmentation.
publisher: VNU
date: 2020
type: Article
type: PeerReviewed
format: text/html
language: en
identifier: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/3984/1/241
identifier:   Hoang, Hong Son and Pham, Cam Phuong and Walsum, Theo van and Luu, Manh Ha  (2020) Liver Segmentation on a Variety of Computed Tomography (CT) Images Based on Convolutional Neural Networks Combined with Connected Components.  VNU Journal of Science: Computer Science and Communication Engineering .    ISSN 2588-1086