MitoEM Dataset: Large-scale 3D Mitochondria Instance Segmentation from EM Images.
Med Image Comput Comput Assist Interv
; 12265: 66-76, 2020 Oct.
Article
em En
| MEDLINE
| ID: mdl-33283212
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.
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Base de dados:
MEDLINE
Idioma:
En
Revista:
Med Image Comput Comput Assist Interv
Assunto da revista:
DIAGNOSTICO POR IMAGEM
/
INFORMATICA MEDICA
Ano de publicação:
2020
Tipo de documento:
Article