Weakly Supervised Segmentation of Plasma Cells in Bone Marrow via Scribble Annotations

Multiple Myeloma (MM) is a type of white blood cell cancer involving the abnormal multiplication of plasma cells. Performing nucleus and cytoplasm segmentation is a critical step in the analysis of MM. Compared to traditional semantic segmentation methods, weakly supervised semantic segmentation models based on scribble annotations require less labeling information, which can reduce the workload of manual annotation. However, due to the lack of explicit supervisory information for contours, these methods perform poorly at object boundaries, particularly in cell segmentation. Therefore, we propose a tri-branch network that incorporates a contour pseudo label supervision module for multitask learning and a contour attention mechanism to enhance the capability of segmenting cell contours. We constructed the Multiple Myeloma Plasma Cells scribble-annotated dataset based on SegPC-2021 and validated the effectiveness of our method on this dataset. Experimental results demonstrate that our method surpasses other weakly supervised approaches based on scribble annotations.