A three in one bottom-up framework for simultaneous semantic
  segmentation, instance segmentation and classification of multi-organ nuclei
  in digital cancer histology
Ibtihaj Ahmad Syed Muhammad Israr Zain Ul Islam
Simultaneous segmentation and classification of nuclei in digital histology
play an essential role in computer-assisted cancer diagnosis; however, it
remains challenging. The highest achieved binary and multi-class Panoptic
Quality (PQ) remains as low as 0.68 bPQ and 0.49 mPQ, respectively. It is due
to the higher staining variability, variability across the tissue, rough
clinical conditions, overlapping nuclei, and nuclear class imbalance. The
generic deep-learning methods usually rely on end-to-end models, which fail to
address these problems associated explicitly with digital histology. In our
previous work, DAN-NucNet, we resolved these issues for semantic segmentation
with an end-to-end model. This work extends our previous model to simultaneous
instance segmentation and classification. We introduce additional decoder heads
with independent weighted losses, which produce semantic segmentation, edge
proposals, and classification maps. We use the outputs from the three-head
model to apply post-processing to produce the final segmentation and
classification. Our multi-stage approach utilizes edge proposals and semantic
segmentations compared to direct segmentation and classification strategies
followed by most state-of-the-art methods. Due to this, we demonstrate a
significant performance improvement in producing high-quality instance
segmentation and nuclei classification. We have achieved a 0.841 Dice score for
semantic segmentation, 0.713 bPQ scores for instance segmentation, and 0.633
mPQ for nuclei classification. Our proposed framework is generalized across 19
types of tissues. Furthermore, the framework is less complex compared to the
state-of-the-art.