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2020 | OriginalPaper | Chapter

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

Authors : 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 W. Lichtman, Hanspeter Pfister

Published in: Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

Publisher: Springer International Publishing

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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 \(\upmu \)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.

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Title: MitoEM Dataset: Large-Scale 3D Mitochondria Instance Segmentation from EM Images
Authors: 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 W. Lichtman
Hanspeter Pfister
Publisher: Springer International Publishing
Book: Medical Image Computing and Computer Assisted Intervention – MICCAI 2020
Print ISBN: 978-3-030-59721-4

Electronic ISBN: 978-3-030-59722-1

Copyright Year: 2020
DOI: https://doi.org/10.1007/978-3-030-59722-1_7

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