  Automated analysis of electron microscopy datasets poses multiple challenges,
such as limitation in the size of the training dataset, variation in data
distribution induced by variation in sample quality and experiment conditions,
etc. It is crucial for the trained model to continue to provide acceptable
segmentation/classification performance on new data, and quantify the
uncertainty associated with its predictions. Among the broad applications of
machine learning, various approaches have been adopted to quantify uncertainty,
such as Bayesian modeling, Monte Carlo dropout, ensembles, etc. With the aim of
addressing the challenges specific to the data domain of electron microscopy,
two different types of ensembles of pre-trained neural networks were
implemented in this work. The ensembles performed semantic segmentation of ice
crystal within a two-phase mixture, thereby tracking its phase transformation
to water. The first ensemble (EA) is composed of U-net style networks having
different underlying architectures, whereas the second series of ensembles
(ER-i) are composed of randomly initialized U-net style networks, wherein each
base learner has the same underlying architecture 'i'. The encoders of the base
learners were pre-trained on the Imagenet dataset. The performance of EA and ER
were evaluated on three different metrics: accuracy, calibration, and
uncertainty. It is seen that EA exhibits a greater classification accuracy and
is better calibrated, as compared to ER. While the uncertainty quantification
of these two types of ensembles are comparable, the uncertainty scores
exhibited by ER were found to be dependent on the specific architecture of its
base member ('i') and not consistently better than EA. Thus, the challenges
posed for the analysis of electron microscopy datasets appear to be better
addressed by an ensemble design like EA, as compared to an ensemble design like
ER.
