  Fluorescence microscopy images contain several channels, each indicating a
marker staining the sample. Since many different marker combinations are
utilized in practice, it has been challenging to apply deep learning based
segmentation models, which expect a predefined channel combination for all
training samples as well as at inference for future application. Recent work
circumvents this problem using a modality attention approach to be effective
across any possible marker combination. However, for combinations that do not
exist in a labeled training dataset, one cannot have any estimation of
potential segmentation quality if that combination is encountered during
inference. Without this, not only one lacks quality assurance but one also does
not know where to put any additional imaging and labeling effort. We herein
propose a method to estimate segmentation quality on unlabeled images by (i)
estimating both aleatoric and epistemic uncertainties of convolutional neural
networks for image segmentation, and (ii) training a Random Forest model for
the interpretation of uncertainty features via regression to their
corresponding segmentation metrics. Additionally, we demonstrate that including
these uncertainty measures during training can provide an improvement on
segmentation performance.
