Parameter map guided explainable segmentation framework for breast cancer using amide proton transfer weighted imaging

Background: Amide proton transfer weighted (APTw) imaging has demonstrated extensive clinical applications in diagnosing, treating evaluating, and prognosis prediction of breast cancer. There is a pressing need to automatically segment breast lesions on APTw original images to facilitate downstream quantification, which is however challenging. Purpose: To build a segmentation model on the original images of APTw imaging sequence by leveraging the varying contrasts between breast lesions and their surrounding glandular and fat tissues displayed on the original images of APTw imaging at different frequency offsets. Methods: This paper proposes a network with multiple tasks, including a breast lesion segmentation model (task I) incorporating multiple images at different frequencies with different contrasts between tumor and surrounding tissues, an automatic classification of pathological task (task II), and an APTw parameter map fitting (task III). Results: Compared with these advanced segmentation methods such as U-Net, segment anything model (SAM), segment anything in medical images (Med-SAM), and transfomer for MRI brain tumor segmentation (TransBTS), our method achieves higher accuracy (ACC). Furthermore, the model's interpretability facilitates the evaluation of how maps with varying gray contrasts contribute to the segmentation. Moreover, improving the ACC of segmentation can be accomplished through tasks such as pathological classification and parametric map fitting. Conclusions: The pathological classification task and parameter fitting task could improve the ACC of segmentation.