Zusammenfassung
Diffusion-weighted imaging (DWI) is a rapidly emerging unenhanced MRI technique in oncologic breast imaging. This IRB approved study included n=818 patients (with n=618 malignant lesions in n=268 patients). All patients underwent a clinically indicated multiparametric breast 3T MRI examination, including a multi-b-value DWI acquisition (50,750,1500). We utilized nnDetection, a state-of-the-art self-configuring object detection model, with certain breast cancer-specific extensions to train a detection model. The model was trained with the following extensions: (i) apparent diffusion coefficient (ADC) as additional input, (ii) random bias field, random spike, and random ghosting augmentations, (iii) a size-balanced data loader to ensure that the fewer large lesions were given an equal chance to be picked in a mini-batch and (iv) replacement of the loss function with a size-adjusted focal loss, to prioritize finding primary lesions while disincentivizing small indeterminate false positives. The model was able to achieve an AUC of 0.88 in 5-fold cross-validation using only the DWI acquisition, and compares favorably against multireader performance metrics reported for screening mammography in large studies in the literature (0.81, 0.87, 0.81). It also achieved 0.70 FROC for primary lesions, indicating a relevant localization ability. This study shows that AI has the ability to complement breast cancer screening assessment in DWI-based examinations. This work was originally published at RSNA 2023 [1].
Chapter PDF
Similar content being viewed by others
References
Bounias D, Baumgartner M, Neher P, Kovacs B, Floca R, Jaeger PF et al. AI for Diffusionweighted Breast MRI. RSNA. 2023
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
About this paper
Cite this paper
Bounias, D. et al. (2024). Abstract: Object Detection for Breast Diffusion-weighted Imaging. In: Maier, A., Deserno, T.M., Handels, H., Maier-Hein, K., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2024. BVM 2024. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-44037-4_84
Download citation
DOI: https://doi.org/10.1007/978-3-658-44037-4_84
Published:
Publisher Name: Springer Vieweg, Wiesbaden
Print ISBN: 978-3-658-44036-7
Online ISBN: 978-3-658-44037-4
eBook Packages: Computer Science and Engineering (German Language)