NOISe: Nuclei-Aware Osteoclast Instance Segmentation for Mouse-to-Human
  Domain Transfer
Sai Kumar Reddy Manne Brendan Martin Tyler Roy Ryan Neilson Rebecca Peters Meghana Chillara Christine W. Lary Katherine J. Motyl Michael Wan
Osteoclast cell image analysis plays a key role in osteoporosis research, but
it typically involves extensive manual image processing and hand annotations by
a trained expert. In the last few years, a handful of machine learning
approaches for osteoclast image analysis have been developed, but none have
addressed the full instance segmentation task required to produce the same
output as that of the human expert led process. Furthermore, none of the prior,
fully automated algorithms have publicly available code, pretrained models, or
annotated datasets, inhibiting reproduction and extension of their work. We
present a new dataset with ~2*10^5 expert annotated mouse osteoclast masks,
together with a deep learning instance segmentation method which works for both
in vitro mouse osteoclast cells on plastic tissue culture plates and human
osteoclast cells on bone chips. To our knowledge, this is the first work to
automate the full osteoclast instance segmentation task. Our method achieves a
performance of 0.82 mAP_0.5 (mean average precision at intersection-over-union
threshold of 0.5) in cross validation for mouse osteoclasts. We present a novel
nuclei-aware osteoclast instance segmentation training strategy (NOISe) based
on the unique biology of osteoclasts, to improve the model's generalizability
and boost the mAP_0.5 from 0.60 to 0.82 on human osteoclasts. We publish our
annotated mouse osteoclast image dataset, instance segmentation models, and
code at github.com/michaelwwan/noise to enable reproducibility and to provide a
public tool to accelerate osteoporosis research.