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.
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 (Presented at SPIE Medical Imaging: February 12, 2018; Published: 27 February 2018); https://doi.org/10.1117/12.2294555.
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