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