Boundary as the Bridge: Toward Heterogeneous Partially-Labeled Medical Image Segmentation and Landmark Detection

Medical landmark detection and segmentation are crucial elements for computer-aided diagnosis and treatment. However, a common challenge arises because many datasets are exclusively annotated with either landmarks or segmentation masks: a situation we term the 'heterogeneous partially-labeled' problem. To address this, we propose a novel yet effective 'Boundary-as-Bridge' Loss (BaBLoss) that models the interplay between landmark detection and segmentation tasks. Specifically, our loss function is designed to maximize the correlation between the boundary distance map of the segmentation area and the heatmap deployed for landmark detection. Moreover, we introduce a prompt pipeline to use a segment anything model and landmarks to generate pseudo-segmentation labels for data with landmark annotation. To evaluate the effectiveness of our method, we collect and build two heterogeneous partially-labeled datasets on the brain and knee. Extensive experiments on these datasets using various backbone structures have shown the effectiveness of our method. Code is available at https://github.com/lhaof/HPL.