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Proceedings Paper

Automatic liver volume segmentation and fibrosis classification
Author(s): Evgeny Bal; Eyal Klang; Michal Amitai; Hayit Greenspan
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Paper 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.

Paper Details

Date Published: 27 February 2018
PDF: 6 pages
Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 1057506 (27 February 2018); doi: 10.1117/12.2294555
Show Author Affiliations
Evgeny Bal, Tel Aviv Univ. (Israel)
Eyal Klang, The Chaim Sheba Medical Ctr., Tel Hashomer (Israel)
Michal Amitai, The Chaim Sheba Medical Ctr., Tel Hashomer (Israel)
Hayit Greenspan, Tel Aviv Univ. (Israel)

Published in SPIE Proceedings Vol. 10575:
Medical Imaging 2018: Computer-Aided Diagnosis
Nicholas Petrick; Kensaku Mori, Editor(s)

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