MitoEM Dataset: Large-scale 3D Mitochondria Instance Segmentation from EM Images.
MitoEM Dataset: Large-scale 3D Mitochondria Instance Segmentation from EM Images.
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DOI：
10.1007/978-3-030-59722-1_7
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发表时间：
2020-10
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影响因子：
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通讯作者：
Pfister H
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文献类型：
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作者：
Wei D;Lin Z;Franco-Barranco D;Wendt N;Liu X;Yin W;Huang X;Gupta A;Jang WD;Wang X;Arganda-Carreras I;Lichtman JW;Pfister H
电子显微镜(EM)可以识别细胞内的细胞器，如线粒体，为临床和科学研究提供了见解。然而，公开的线粒体分割数据集只包含数百个形状简单的实例。目前尚不清楚在这些小数据集上实现人类水平的准确性的现有方法是否在实践中是稳健的。为此，我们引入了MitoEM数据集，这是一个3D线粒体实例分割数据集，分别来自人和大鼠大脑皮层的两个(30μm)3体积，比以前的基准大3,600倍。在大约40K个实例中，我们发现线粒体在形状和密度方面具有极大的多样性。为了进行评估，我们针对3D数据定制了平均精度(AP)度量的实施，加速比为45倍。在MitoEM上，我们发现现有的实例分割方法往往无法正确分割形状复杂或与其他实例接触密切的线粒体。因此，我们的MitoEM数据集对该领域提出了新的挑战。我们发布我们的代码和数据：https://donglaiw.github.io/page/mitoEM/index.html.
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.