  Electron microscopy (EM) allows the identification of intracellular
organelles such as mitochondria, providing insights for clinical and scientific
studies. In recent years, a number of novel deep learning architectures have
been published reporting superior performance, or even human-level accuracy,
compared to previous approaches on public mitochondria segmentation datasets.
Unfortunately, many of these publications do not make neither the code nor the
full training details public to support the results obtained, leading to
reproducibility issues and dubious model comparisons. For that reason, and
following a recent code of best practices for reporting experimental results,
we present an extensive study of the state-of-the-art deep learning
architectures for the segmentation of mitochondria on EM volumes, and evaluate
the impact in performance of different variations of 2D and 3D U-Net-like
models for this task. To better understand the contribution of each component,
a common set of pre- and post-processing operations has been implemented and
tested with each approach. Moreover, an exhaustive sweep of hyperparameters
values for all architectures have been performed and each configuration has
been run multiple times to report the mean and standard deviation values of the
evaluation metrics. Using this methodology, we found very stable architectures
and hyperparameter configurations that consistently obtain state-of-the-art
results in the well-known EPFL Hippocampus mitochondria segmentation dataset.
Furthermore, we have benchmarked our proposed models on two other available
datasets, Lucchi++ and Kasthuri++, where they outperform all previous works.
The code derived from this research and its documentation are publicly
available.
