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:
this https URL

通过去除细胞注释中的 50% ，学习性的分割模型表现的结果只比完整的标签相应的模型降低了 DSC 指标，表明可以至少减少一半的注释时间而不影响性能，并且单一组织模型与多组织模型在新颖组织类型上的性能差异微乎其微，并且通过多次增加最差性能模型的训练可以提高其性能。