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Cited by 105Cell Image Analysis TechniquesAdvanced Neural Network ApplicationsMedical Image Segmentation Techniques

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

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Wei, Donglai, Lin, Zudi, Franco-Barranco, Daniel, Wendt, Nils, Liu, Xingyu, Yin, Wenjie, Huang, Xin, Gupta, Aarush +5 more·2020·Lecture Notes in Computer Science
DOI 10.1007/978-3-030-59722-1_7PMC PMC7713709
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Abstract

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 (30 μ m)3 volumes from human and rat cortices respectively, 3, 600× 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 45× 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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  • SynapseMNIST3D is a new 3D volume dataset to classify whether a synapse is excitatory or inhibitory, using a 3D image volume of an adult rat acquired by a multi-beam scanning electron microscope.

    “The SynapseMNIST3D is a new 3D volume dataset to classify whether a synapse is excitatory or inhibitory. It uses a 3D image volume of an adult rat acquired by a multi-beam scanning electron microscope.”

    From MedMNIST v2 - A large-scale lightweight benchmark for 2D and 3D biomedical image classification (2023) · cites this paper as [37]

    The Dataset Acquisition section (page unavailable) says the adult-rat tissue used for MitoEM-R was imaged with a multi-beam scanning electron microscope. But the Introduction identifies MitoEM as a 3D mitochondria instance-segmentation dataset; our copy does not identify SynapseMNIST3D or describe excitatory-versus-inhibitory synapse classification. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 32

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Authors
Wei, D., Lin, Z., Franco-Barranco, D., Wendt, N., Liu, X., Yin, W., Huang, X., Gupta, A., Jang, W.-D., Wang, X., Arganda-Carreras, I., Lichtman, J. W., & Pfister, H.
Affiliations
  • Harvard University (US)
  • University of the Basque Country (ES)
  • Technical University of Munich (DE)
  • Shanghai Jiao Tong University (CN)
  • Northeastern University (US)
  • Indian Institute of Technology Roorkee (IN)
Corresponding author
Wei, Donglai
ORCID
Venue
Lecture Notes in Computer Science
Publisher
Springer International Publishing
Published
2020
Volume
12265
Pages
66-76
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
DOI
10.1007/978-3-030-59722-1_7
PMC
PMC7713709
OpenAlex
W3089720110
Cited by
105
References
3
Citations by year
2026: 9 · 2025: 20 · 2024: 18 · 2023: 23 · 2022: 20 · 2021: 13 · 2020: 1
FWCI
36.16
Type
conference paper
Language
en
Open access
Licence
Locations
  • Lecture notes in computer science (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
National Cancer Institute (U54CA225088)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
Keywords
mitochondria, instance segmentation, 3D segmentation, electron microscopy images, cortical tissue, intracellular organelles, benchmark dataset

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