Automated breast cancer risk estimation on routine CT thorax scans by deep learning segmentation

Stijn De Buck; Jeroen Bertels; Chelsey Vanbilsen; Tanguy Dewaele; Chantal Van Ongeval; Hilde Bosmans; Jan Vandevenne; Paul Suetens

doi:10.1117/12.2549585

16 March 2020 Automated breast cancer risk estimation on routine CT thorax scans by deep learning segmentation

Stijn De Buck, Jeroen Bertels, Chelsey Vanbilsen, Tanguy Dewaele, Chantal Van Ongeval, Hilde Bosmans, Jan Vandevenne, Paul Suetens

Author Affiliations +

Proceedings Volume 11314, Medical Imaging 2020: Computer-Aided Diagnosis; 1131423 (2020) https://doi.org/10.1117/12.2549585
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Automation of systematic scoring of breast glandularity on CT thorax examinations performed for another clinical reason could aid in detecting postmenopausal women with increased breast cancer risk. We propose a novel method that combines automated deep learning based breast segmentation from CT thorax examinations with computation of breast glandularity based on radiodensity and volumetric breast density. Reasonable segmentation Dice scores were found as well as very strong correlation between the risk measures computed on the ground truth and with the proposed approach. Hence, the proposed method can offer reliable breast cancer risk measures with limited additional workload for the radiologist.

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Stijn De Buck, Jeroen Bertels, Chelsey Vanbilsen, Tanguy Dewaele, Chantal Van Ongeval, Hilde Bosmans, Jan Vandevenne, and Paul Suetens "Automated breast cancer risk estimation on routine CT thorax scans by deep learning segmentation", Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 1131423 (16 March 2020); https://doi.org/10.1117/12.2549585

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