Nuclei instance segmentation and classification in histopathology images with StarDist
This work addresses a key problem in computational pathology for medical researchers and clinicians, but it is incremental as it adapts an existing method to a new domain.
The authors tackled nuclei instance segmentation and classification in histopathology images by extending StarDist, a method originally for fluorescence microscopy, achieving first place in the CoNIC challenge 2022 for both preliminary and final test phases.
Instance segmentation and classification of nuclei is an important task in computational pathology. We show that StarDist, a deep learning nuclei segmentation method originally developed for fluorescence microscopy, can be extended and successfully applied to histopathology images. This is substantiated by conducting experiments on the Lizard dataset, and through entering the Colon Nuclei Identification and Counting (CoNIC) challenge 2022, where our approach achieved the first spot on the leaderboard for the segmentation and classification task for both the preliminary and final test phase.