Sustainable Deep Learning-Based Breast Lesion Segmentation: Impact of Breast Region Segmentation on Performance

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.

@article{narimani2025_2503.15708,
  title={ Sustainable Deep Learning-Based Breast Lesion Segmentation: Impact of Breast Region Segmentation on Performance },
  author={ Sam Narimani and Solveig Roth Hoff and Kathinka Dahli Kurz and Kjell-Inge Gjesdal and Jurgen Geisler and Endre Grovik },
  journal={arXiv preprint arXiv:2503.15708},
  year={ 2025 }
}