计算机科学
分割
公制(单位)
编码(集合论)
人工智能
比例(比率)
线粒体
模式识别(心理学)
数据挖掘
生物
地图学
程序设计语言
集合(抽象数据类型)
细胞生物学
经济
地理
运营管理
作者
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
标识
DOI:10.1007/978-3-030-59722-1_7
摘要
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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