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. 2020 Oct:12265:66-76.
doi: 10.1007/978-3-030-59722-1_7. Epub 2020 Sep 29.

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

Donglai Wei  1 , Zudi Lin  1 , Daniel Franco-Barranco  2   3 , Nils Wendt  4 , Xingyu Liu  5 , Wenjie Yin  1 , Xin Huang  6 , Aarush Gupta  7 , Won-Dong Jang  1 , Xueying Wang  1 , Ignacio Arganda-Carreras  2   3   8 , Jeff W Lichtman  1 , Hanspeter Pfister  1
Affiliations

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

Donglai Wei et al. Med Image Comput Comput Assist Interv. 2020 Oct.

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 (30μm)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.

Keywords: 3D Instance Segmentation; EM Dataset; Mitochondria.

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Figures

Fig. 1:
Fig. 1:
Comparison of mitochondria segmentation datasets. (Left) Distribution of instance sizes. (Right) 3D image volumes of our MitoEM and Lucchi [20]. Our MitoEM dataset has greater diversity in image appearance and instance sizes.
Fig. 2:
Fig. 2:
Complex mitochondria in our MitoEM dataset: (a) mitochondria-on-a-string (MOAS) [36], and (b) dense tangle of touching mitochondria. Those challenging cases are prevalent but not covered by existing labeled datasets.
Fig. 3:
Fig. 3:
Visualization of MitoEM-H and MitoEM-R datasets. (Top) 3D meshes of small and large mitochondria, where MitoEM-R has a higher presence of large mitochondria; (Bottom left) scatter plot of mitochondria by their skeleton length and volume; (Bottom right) 3D meshes of the mitochondria at the sampled positions.
Fig. 4:
Fig. 4:
Instance segmentation methods in two types: bottom-up and top-down.
Fig. 5:
Fig. 5:
Qualitative results on MitoEM. (a) The U3D-BC+MW method can capture complex mitochondria morphology. (b) Failure cases are resulted from ambiguous touching boundaries and highly overlapping cross sections.
See this image and copyright information in PMC

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