Automatic liver volume segmentation and fibrosis classification

Evgeny Bal, Eyal Klang, Michal Amitai, Hayit Greenspan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this work, we present an automatic method for liver segmentation and fibrosis classification in liver computed-tomography (CT) portal phase scans. The input is a full abdomen CT scan with an unknown number of slices, and the output is a liver volume segmentation mask and a fibrosis grade. A multi-stage analysis scheme is applied to each scan, including: Volume segmentation, texture features extraction and SVM based classification. Data contains portal phase CT examinations from 80 patients, taken with different scanners. Each examination has a matching Fibroscan grade. The dataset was subdivided into two groups: First group contains healthy cases and mild fibrosis, second group contains moderate fibrosis, severe fibrosis and cirrhosis. Using our automated algorithm, we achieved an average dice index of 0.93 ± 0.05 for segmentation and a sensitivity of 0.92 and specificity of 0.81for classification. To the best of our knowledge, this is a first end to end automatic framework for liver fibrosis classification; an approach that, once validated, can have a great potential value in the clinic.

Original languageEnglish
Title of host publicationMedical Imaging 2018
Subtitle of host publicationComputer-Aided Diagnosis
EditorsKensaku Mori, Nicholas Petrick
PublisherSPIE
ISBN (Electronic)9781510616394
DOIs
StatePublished - 2018
EventMedical Imaging 2018: Computer-Aided Diagnosis - Houston, United States
Duration: 12 Feb 201815 Feb 2018

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume10575
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2018: Computer-Aided Diagnosis
Country/TerritoryUnited States
CityHouston
Period12/02/1815/02/18

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