A Mutual Inclusion Mechanism for Precise Boundary Segmentation in Medical Images

arXiv:2404.08201v1 Announce Type: cross
Abstract: In medical imaging, accurate image segmentation is crucial for quantifying diseases, assessing prognosis, and evaluating treatment outcomes. However, existing methods lack an in-depth integration of global and local features, failing to pay special attention to abnormal regions and boundary details in medical images. To this end, we present a novel deep learning-based approach, MIPC-Net, for precise boundary segmentation in medical images. Our approach, inspired by radiologists' working patterns, features two distinct modules: (i) \textbf{Mutual Inclusion …

arxiv cs.cv eess.iv images inclusion medical segmentation type