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Updated: Aug 13, 2025
Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
Published on: March 3, 2023
Instance segmentation of mitochondria in electron microscopy images with a generalist deep learning model trained on
1Center for Molecular Microscopy, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA; Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA.
Abstract:
Mitochondria are extremely pleomorphic organelles. Automatically annotating each one accurately and precisely in any 2D or volume electron microscopy (EM) image is an unsolved computational challenge. Current deep learning-based approaches train models on images that provide limited cellular contexts, precluding generality. To address this, we amassed a highly heterogeneous ∼1.5 × 106 image 2D unlabeled cellular EM dataset and segmented ∼135,000 mitochondrial instances therein. MitoNet, a model trained on these resources, performs well on challenging benchmarks and on previously unseen volume EM datasets containing tens of thousands of mitochondria. We release a Python package and napari plugin, empanada, to rapidly run inference, visualize, and proofread instance segmentations. A record of this paper's transparent peer review process is included in the supplemental information.
