MOM-BUS: A Multi-Output Framework for Precise Breast Lesion Segmentation in Ultrasound Images

Abstract Accurate breast tumor segmentation in ultrasound images is essential for cancer diagnosis and treatment planning. However, challenges such as low image contrast, irregular shapes and tumor boundary ambiguity often hinder the segmentation process. To address these issues, this study proposes a novel deep learning framework termed MOM-BUS, which utilizes a multi-tumoral area segmentation approach. It leverages shared characteristics among multiple segmentation tasks to enhance performance. Specifically, the framework delineates the intra-tumoral area (ITA), peri-tumoral area, and enlarged tumoral area (ETA) simultaneously, using their interconnected features to produce more accurate results. Furthermore, a conditional test-time ensemble approach is introduced to handle outliers and refine segmentation results by eliminating undesired elements from the network output. The effectiveness of the proposed framework has been validated through extensive experiments on two distinct datasets using five different backbone models. Experimental results consistently demonstrate that the proposed framework achieves superior segmentation performance compared to single-output counterparts, with improvements in Dice coefficient and Jaccard Index values of up to 5.35% and 5.39%, respectively. These improvement gains highlight the reliability of our framework in accurately delineating breast tumor, offering significant potential to improve subsequent malignancy assessment and clinical decision-making processes.