Automatic liver volume segmentation and fibrosis classification

Evgeny Bal; Eyal Klang; Michal Amitai; Hayit Greenspan

doi:10.1117/12.2294555

27 February 2018 Automatic liver volume segmentation and fibrosis classification

Evgeny Bal, Eyal Klang, Michal Amitai, Hayit Greenspan

Proceedings Volume 10575, Medical Imaging 2018: Computer-Aided Diagnosis; 1057506 (2018) https://doi.org/10.1117/12.2294555
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

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.

Conference Presentation

Citation Download Citation

Evgeny Bal, Eyal Klang, Michal Amitai, and Hayit Greenspan "Automatic liver volume segmentation and fibrosis classification", Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 1057506 (27 February 2018); https://doi.org/10.1117/12.2294555

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