Multi-Granularity Topology-Aware Cell Localization and Counting in Pathological Images

Cell localization and counting in pathological images play an important role in the diagnosis and treatment of life-threatening diseases (e.g., tumor). However, they still remain a challenging work, due to cell clustering and adhesion, blurred boundaries, deformation, and difficulty of annotation. In this work, we address these problems by introducing multi-granularity topological constraints in model training. First, a loss function of topological structure constraint for single cells is proposed, which encourages the trained model to avoid the wrong prediction of multiple cells within an instance (false positives). Second, a loss function of constraint of spatial topological structure distribution is proposed for clustered cells, which helps the trained model to reduce the wrong prediction of some crowded cells as one (false negative). Third, a loss is proposed from the expert check of annotation and inference errors, which enables positioning of difficult samples and facilitates the correction of errors. The multi-granularity loss under topological feature constraints enables a significant enhancement in the performance of the trained model. Experimental results on a self-collected COVID-19 pathological dataset and two public pathological datasets validate the performance advantages of the proposed method over some state-of-the-art methods. Our code will be available at https://github.com/MedicalYajieChen/MGTopology.