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Instance segmentation of mitochondria in electron microscopy images with a generalist deep learning model trained on a diverse dataset.

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.

SUBMITTER: Conrad R

PROVIDER: S-EPMC9883049 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

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Instance segmentation of mitochondria in electron microscopy images with a generalist deep learning model trained on a diverse dataset.

Conrad Ryan R Narayan Kedar K

Cell systems 20230101 1

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 × 10<sup>6</sup> image 2D unlabeled cellular EM dataset and segmented ∼135,000 mitochondrial instances therein. MitoNe ...[more]

PMID: 36657391

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