Graph-based automatic detection and classification of lesion changes in pairs of CT studies for oncology follow-up

Shalom Rochman, Adi Szeskin, Richard Lederman, Jacob Sosna, Leo Joskowicz

Abstract: Radiological follow-up of oncology patients requires the quantitative analysis of lesion changes in longitudinal imaging studies, which is time-consuming, requires expertise, and is subject to variability. This paper presents a comprehensive graph-based method for the automatic detection and classification of lesion changes in current and prior CT scans.