Sustainable deep learning-based breast lesion segmentation: impact of breast region segmentation on performance.
Authors
Affiliations (13)
Affiliations (13)
- Department of Physics, Norwegian University of Science and Technology, Trondheim, Norway. [email protected].
- Research and Development Department, More og Romsdal Hospital Trust, Aalesund, Norway. [email protected].
- Department of Radiology, More og Romsdal Hospital Trust, Aalesund, Norway. [email protected].
- Department of Radiology, More og Romsdal Hospital Trust, Aalesund, Norway.
- Department of Health Sciences, Norwegian University of Science and Technology, Aalesund, Norway.
- Stavanger Medical Imaging Group, Radiology Department, Stavanger University Hospital, Stavanger, Norway.
- Department of Electrical Engineering and Computer Science, The University of Stavanger, Stavanger, Norway.
- Department of Diagnostic Imaging, Akershus University Hospital, Lorenskog, Norway.
- NordicCAD AS, Aalesund, Norway.
- Institute of Clinical Medicine, University of Oslo, Lorenskog, Norway.
- Department of Oncology, Akershus University Hospital, Lorenskog, Norway.
- Department of Physics, Norwegian University of Science and Technology, Trondheim, Norway.
- Research and Development Department, More og Romsdal Hospital Trust, Aalesund, Norway.
Abstract
Segmentation of breast lesions in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is critical for effective diagnosis. This study investigates the impact of breast region segmentation (BRS) on the performance of deep learning-based breast lesion segmentation (BLS) in breast DCE-MRI. The study utilized the Stavanger Dataset, comprising 59 DCE-MRI scans, and employed the UNet++ architecture as the segmentation model. Four experimental approaches were designed to assess the influence of BRS on BLS: (1) Whole Volume (WV) without BRS, (2) WV with BRS, (3) BRS applied only to Selected Lesion-containing Slices (SLS), and (4) BRS applied to an Optimal Volume (OV). Data augmentation and oversampling techniques were implemented to address dataset limitations and enhance model generalizability. A systematic method was employed to determine OV sizes for patient's DCE-MRI images ensuring full lesion inclusion. Model training and validation were conducted using a hybrid loss function-comprising Dice loss, focal loss, and cross-entropy loss-and a five-fold cross-validation strategy. Final evaluations were performed on a randomly split test dataset for each of the four approaches. The findings indicate that applying BRS significantly enhances model performance. The most notable improvement was observed in the fourth approach, BRS with OV, which achieved approximately a 50% increase in segmentation accuracy compared to the non-BRS baseline. Furthermore, the BRS with OV approach resulted in a substantial reduction in computational energy consumption-up to 450%, highlighting its potential as an environmentally sustainable solution for large-scale applications.