Pushing the limits of cell segmentation models for imaging mass
  cytometry
Kimberley M. Bird Xujiong Ye Alan M. Race James M. Brown
Imaging mass cytometry (IMC) is a relatively new technique for imaging
biological tissue at subcellular resolution. In recent years, learning-based
segmentation methods have enabled precise quantification of cell type and
morphology, but typically rely on large datasets with fully annotated ground
truth (GT) labels. This paper explores the effects of imperfect labels on
learning-based segmentation models and evaluates the generalisability of these
models to different tissue types. Our results show that removing 50% of cell
annotations from GT masks only reduces the dice similarity coefficient (DSC)
score to 0.874 (from 0.889 achieved by a model trained on fully annotated GT
masks). This implies that annotation time can in fact be reduced by at least
half without detrimentally affecting performance. Furthermore, training our
single-tissue model on imperfect labels only decreases DSC by 0.031 on an
unseen tissue type compared to its multi-tissue counterpart, with negligible
qualitative differences in segmentation. Additionally, bootstrapping the
worst-performing model (with 5% of cell annotations) a total of ten times
improves its original DSC score of 0.720 to 0.829. These findings imply that
less time and work can be put into the process of producing comparable
segmentation models; this includes eliminating the need for multiple IMC tissue
types during training, whilst also providing the potential for models with very
few labels to improve on themselves. Source code is available on GitHub:
https://github.com/kimberley/ISBI2024.