Stable Deep Neural Network Architectures for Mitochondria Segmentation on Electron Microscopy Volumes.

ABSTRACT: 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 make neither the code nor the full training details public, leading to reproducibility issues and dubious model comparisons. Thus, following a recent code of best practices in the field, we present an extensive study of the state-of-the-art architectures and compare them to different variations of U-Net-like models for this task. To unveil the impact of architectural noveltie

SUBMITTER: Franco-Barranco D 

PROVIDER: S-EPMC9546980 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature