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 \(\upmu \)m)\(^3\) volumes from human and rat cortices respectively, 3,600\(\times \) 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\(\times \) 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.
N. Wendt, X. Liu, W. Yin, X. Huang, and A. Gupta—Works are done during internship at Harvard University.
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Acknowledgments
This work has been partially supported by NSF award IIS-1835231 and NIH award 5U54CA225088-03.
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Wei, D. et al. (2020). MitoEM Dataset: Large-Scale 3D Mitochondria Instance Segmentation from EM Images. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12265. Springer, Cham. https://doi.org/10.1007/978-3-030-59722-1_7
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