Papers › MitoEM Dataset: Large-scale 3D Mitochondria Instance Segmentation from EM Images

MitoEM Dataset: Large-scale 3D Mitochondria Instance Segmentation from EM Images

4 Oct 2020Medical Image Computing and Computer Assisted Intervention 2020 10archive 2025-07-28

Donglai Wei, Zudi Lin, Daniel Franco-Barranco, Nils Wendt, Xingyu Liu, Wenjie Yin, Xin Huang, Aarush Gupta, Won-Dong Jang, Xueying Wang, Ignacio Arganda-Carreras, Jeff Lichtman, Hanspeter Pfister

Electron microscopy (EM) allows the identification of intracellular organelles such as mitochondria, providing insights for clinical and scientific studies. However, public mitochondria segmentation datasets only contain hundreds of instances with simple shapes. It is unclear if existing methods achieving human-level accuracy on these small datasets are robust in practice. To this end, we introduce the MitoEM dataset, a 3D mitochondria instance segmentation dataset with two (30um)^3 volumes from human and rat cortices respectively, 3,600x larger than previous benchmarks. With around 40K instances, we find a great diversity of mitochondria in terms of shape and density. For evaluation, we tailor the implementation of the average precision (AP) metric for 3D data with a 45x speedup. On MitoEM, we find existing instance segmentation methods often fail to correctly segment mitochondria with complex shapes or close contacts with other instances. Thus, our MitoEM dataset poses new challenges to the field. We release our code and data: https://donglaiw.github.io/page/mitoEM/index.html.

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zudi-lin/pytorch_connectomics pytorchMIT report

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Tasks

3D Instance Segmentation Diversity Instance Segmentation Segmentation Semantic Segmentation

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
3D Instance Segmentation	MitoEM	Unet3D	AP75-H-Test	0.804	#2 of 2	Archive leaderboard	report
3D Instance Segmentation	MitoEM	Unet3D	AP75-R-Test	0.816	#2 of 2	Archive leaderboard	report

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