Lightweight Mamba Model for 3D Tumor Segmentation in Automated Breast Ultrasounds

Background: Recently, the adoption of AI-based technologies has been accelerating in the field of medical image analysis. For the early diagnosis and treatment planning of breast cancer, Automated Breast Ultrasound (ABUS) has emerged as a safe and non-invasive imaging method, especially for women with dense breasts. However, the increasing computational cost due to the minute size and complexity of 3D ABUS data remains a major challenge. Methods: In this study, we propose a novel model based on the Mamba state–space model architecture for 3D tumor segmentation in ABUS images. The model uses Mamba blocks to effectively capture the volumetric spatial features of tumors, and integrates a deep spatial pyramid pooling (DASPP) module to extract multiscale contextual information from lesions of different sizes. Results: On the TDSC-2023 ABUS dataset, the proposed model achieved a Dice Similarity Coefficient (DSC) of 0.8062, and Intersection over Union (IoU) of 0.6831, using only 3.08 million parameters. Conclusions: These results show that the proposed model improves the performance of tumor segmentation in ABUS, offering both diagnostic precision and computational efficiency. The reduced computational space suggests a strong potential for real-world medical applications, where accurate early diagnosis can reduce costs and improve patient survival.