Sustainable Deep Learning-Based Breast Lesion Segmentation: Impact of Breast Region Segmentation on Performance
Journal:
arXiv
Published Date:
Mar 19, 2025
Abstract
Purpose: Segmentation of the breast lesion in dynamic contrast-enhanced
magnetic resonance imaging (DCE-MRI) is an essential step to accurately
diagnose and plan treatment and monitor progress. This study aims to highlight
the impact of breast region segmentation (BRS) on deep learning-based breast
lesion segmentation (BLS) in breast DCE-MRI.
Methods Using the Stavanger Dataset containing primarily 59 DCE-MRI scans and
UNet++ as deep learning models, four different process were conducted to
compare effect of BRS on BLS. These four approaches included the whole volume
without BRS and with BRS, BRS with the selected lesion slices and lastly
optimal volume with BRS. Preprocessing methods like augmentation and
oversampling were used to enhance the small dataset, data shape uniformity and
improve model performance. Optimal volume size were investigated by a precise
process to ensure that all lesions existed in slices. To evaluate the model, a
hybrid loss function including dice, focal and cross entropy along with 5-fold
cross validation method were used and lastly a test dataset which was randomly
split used to evaluate the model performance on unseen data for each of four
mentioned approaches.
Results Results demonstrate that using BRS considerably improved model
performance and validation. Significant improvement in last approach -- optimal
volume with BRS -- compared to the approach without BRS counting around 50
percent demonstrating how effective BRS has been in BLS. Moreover, huge
improvement in energy consumption, decreasing up to 450 percent, introduces a
green solution toward a more environmentally sustainable approach for future
work on large dataset.
