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Proceedings Paper

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

Paper Details

Date Published: 16 March 2020
PDF: 6 pages
Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 1131423 (16 March 2020); doi: 10.1117/12.2549585
Show Author Affiliations
Stijn De Buck, UZ Leuven (Belgium)
KU Leuven (Belgium)
Jeroen Bertels, KU Leuven (Belgium)
Chelsey Vanbilsen, Ziekenhuis Oost-Limburg (Belgium)
Tanguy Dewaele, ZOL (Belgium)
Chantal Van Ongeval, UZ Leuven (Belgium)
Hilde Bosmans, UZ Leuven (Belgium)
Jan Vandevenne, ZOL (Belgium)
Paul Suetens, KU Leuven (Belgium)

Published in SPIE Proceedings Vol. 11314:
Medical Imaging 2020: Computer-Aided Diagnosis
Horst K. Hahn; Maciej A. Mazurowski, Editor(s)

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