Evaluation of cell segmentation methods without reference segmentations

Abstract

AbstractCell segmentation is a cornerstone of many bioimage informatics studies. Inaccurate segmentation introduces computational error in downstream cellular analysis. Evaluating segmentation results is thus a necessary step for developing segmentation methods as well as for choosing the most appropriate method for a particular type of tissue or image. The evaluation process has typically involved comparison of segmentations to those generated by humans, which can be expensive and subject to unknown bias. We present here an approach that seeks to evaluate cell segmentation methods without relying upon comparison to results from humans. For this, we defined a number of segmentation quality metrics that can be applied to multichannel fluorescence images. We calculated these metrics for 11 previously-described segmentation methods applied to datasets from 4 multiplexed microscope modalities covering 5 tissues. Using principal component analysis to combine the metrics we defined an overall cell segmentation quality score and ranked the segmentation methods. All code and data are available in a Reproducible Research Archive.