Detection and prediction of background parenchymal enhancement on breast MRI using deep learning

Das BK, Kapsner L, Ohlmeyer S, Laun FB, Maier A, Uder M, Wenkel E, Bickelhaupt S, Liebert A (2023)

Publication Type: Conference contribution

Publication year: 2023

Event location: London GB

DOI: 10.58530/2022/3927

Abstract

The purpose of this work was to automatically classify BPE using T1-weighted subtraction volumes and diffusion-weighted imaging volumes in breast MRI. The dataset consisted of 621 routine breast MRI examination acquired at University Hospital Erlangen. 2D MIP and 3D T1-subtraction volumes were used for the automatic detection of BPE classes. Multi-b-value DWI (up to1500s/mm2) DWI images were used for automatic prediction. ResNet and DenseNet models were used for 2D and 3D data respectively. The study demonstrated an AUROC of 0.8107 on the test set using the T1-subtraction volumes. With DWI volumes, a slightly decreased AuROC of 0.78 was achieved.

Authors with CRIS profile

Badhan Kumar Das Lehrstuhl für Informatik 5 (Mustererkennung) Lorenz Kapsner Institute of Radiology Sabine Ohlmeyer Institute of Radiology Frederik Bernd Laun Professur für quantitative MR-Bildgebung (Heisenberg-Professur) Andreas Maier Lehrstuhl für Informatik 5 (Mustererkennung) Michael Uder Lehrstuhl für Diagnostische Radiologie Evelyn Wenkel Lehrstuhl für Diagnostische Radiologie Sebastian Bickelhaupt Institute of Radiology Andrzej Liebert Institute of Radiology

How to cite

APA:

Das, B.K., Kapsner, L., Ohlmeyer, S., Laun, F.B., Maier, A., Uder, M.,... Liebert, A. (2023). Detection and prediction of background parenchymal enhancement on breast MRI using deep learning. In Proceedings of the Joint Annual Meeting ISMRM-ESMRMB & ISMRT 31st Annual Meeting. London, GB.

MLA:

Das, Badhan Kumar, et al. "Detection and prediction of background parenchymal enhancement on breast MRI using deep learning." Proceedings of the Joint Annual Meeting ISMRM-ESMRMB & ISMRT 31st Annual Meeting, London 2023.

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