A Green Solution for Breast Region Segmentation Using Deep Active Learning

Purpose: Annotation of medical breast images is an essential step toward better diagnostic but a time consuming task. This research aims to focus on different selecting sample strategies within deep active learning on Breast Region Segmentation (BRS) to lessen computational cost of training and effective use of resources. Methods: The Stavanger breast MRI dataset containing 59 patients was used in this study, with FCN-ResNet50 adopted as a sustainable deep learning (DL) model. A novel sample selection approach based on Breast Anatomy Geometry (BAG) analysis was introduced to group data with similar informative features for DL. Patient positioning and Breast Size were considered the key selection criteria in this process. Four selection strategies including Random Selection, Nearest Point, Breast Size, and a hybrid of all three strategies were evaluated using an active learning framework. Four training data proportions of 10%, 20%, 30%, and 40% were used for model training, with the remaining data reserved for testing. Model performance was assessed using Dice score, Intersection over Union, precision, and recall, along with 5-fold cross-validation to enhance generalizability. Results: Increasing the training data proportion from 10% to 40% improved segmentation performance for nearly all strategies, except for Random Selection. The Nearest Point strategy consistently achieved the lowest carbon footprint at 30% and 40% data proportions. Overall, combining the Nearest Point strategy with 30% of the training data provided the best balance between segmentation performance, efficiency, and environmental sustainability. Keywords: Deep Active Learning, Breast Region Segmentation, Human-center analysis

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.