A novel weakly supervised immunohistochemical cell segmentation method via counting labels

• We propose a weakly-supervised segmentation method for IHC staining pathological cell images, which only uses positive cell counting labels. • It transforms image level counting results into superpixel level classification results as coarse segmentation masks via the designed adaptive top-k instance selection strategy. • It further generates refined pixel-wise cell segmentation masks via the proposed superpixel mask refining approach based on IHC prior knowledge. • Experiments on two public datasets show that our method performs excellently in cell counting and segmentation tasks, and keeps strong generalization. Immunohistochemistry (IHC) can specifically stain immune cells, and quantitative analysis of tumor-infiltrating immune cells is crucial for the diagnosis of malignant tumors and the prognosis of immunotherapy. However, for current deep learning-based immuno-quantitative methods, it is difficult to obtain labeled data for fine-grained analysis tasks, such as cell segmentation, because manual labeling is laborious. Moreover, information contained in coarse-grained image-level labels, such as positive cell counting labels, is difficult to leverage. Here, we propose a novel multi-stage automated immuno-quantitative analysis model for cell counting and segmentation, which is solely supervised by positive cell counting labels. In order to excavate the potential information of counting labels for cell segmentation, based on the idea of Multiple Instance Learning (MIL), we transform entire image (bag) level counting results into superpixel (instance) level classification results as coarse segmentation masks, using a specifically designed adaptive top-k instance selection strategy. Furthermore, a mask refinement approach is proposed, which can refine coarse masks generated by superpixel instance classification into pixel-level pseudo masks as supervision of the segmentation network. Our method enables all-in-one immuno-quantitative analysis of cell counting and segmentation with only positive cell counting labels, alleviating the dependence on fine-grained labels and economizing labeling cost. Our work is the first to apply MIL to small-scale IHC-stained cell image segmentation task. We trained and tested our model on the public LYSTO dataset and conducted generalization tests on the NuClick IHC dataset. Our model achieved segmentation dice scores of 0.809 and 0.711, respectively, demonstrating approximately 7.44% and 3.95% performance improvements over the state-of-the-art (SOTA) methods. The experiments show that our model performs excellently in cell counting and cell segmentation tasks while maintaining strong generalization capabilities.