Detecting liver cirrhosis in computed tomography scans using clinically-inspired and radiomic features

Krzysztof Kotowski, Damian Kucharski, Bartosz Machura, Szymon Adamski, Benjamín Gutiérrez-Becker, Agata Krason, Lukasz Zarudzki, Jean Tessier, Jakub Nalepa

Abstract: Highlights•We propose an end-to-end and reproducible approach for detecting cirrhosis from CT.•We propose clinically-inspired feature extractors to reflect liver characteristics.•We couple clinically-inspired and radiomic features for better classification.•We extract the region of interest corresponding to the rectified liver’s boundary.•We dramatically reduce the number of important features from thousands to tens.