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Simultaneous segmentation and classification of nuclei in digital histologyplay an essential role in computer-assisted cancer diagnosis; however, itremains challenging. The highest achieved binary and multi-class PanopticQuality (PQ) remains as low as 0.68 bPQ and 0.49 mPQ, respectively. It is dueto the higher staining variability, variability across the tissue, roughclinical conditions, overlapping nuclei, and nuclear class imbalance. Thegeneric deep-learning methods usually rely on end-to-end models, which fail toaddress these problems associated explicitly with digital histology. In ourprevious work, DAN-NucNet, we resolved these issues for semantic segmentationwith an end-to-end model. This work extends our previous model to simultaneousinstance segmentation and classification. We introduce additional decoder headswith independent weighted losses, which produce semantic segmentation, edgeproposals, and classification maps. We use the outputs from the three-headmodel to apply post-processing to produce the final segmentation andclassification. Our multi-stage approach utilizes edge proposals and semanticsegmentations compared to direct segmentation and classification strategiesfollowed by most state-of-the-art methods. Due to this, we demonstrate asignificant performance improvement in producing high-quality instancesegmentation and nuclei classification. We have achieved a 0.841 Dice score forsemantic segmentation, 0.713 bPQ scores for instance segmentation, and 0.633mPQ for nuclei classification. Our proposed framework is generalized across 19types of tissues. Furthermore, the framework is less complex compared to thestate-of-the-art.

A three in one bottom-up framework for simultaneous semantic  segmentation, instance segmentation and classification of multi-organ nuclei  in digital cancer histology