{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/mitoem-dataset-large-scale-3d-mitochondria","title":"MitoEM Dataset: Large-scale 3D Mitochondria Instance Segmentation from EM Images","arxiv_id":null,"date":"2020-10-04","proceeding":"Medical Image Computing and Computer Assisted Intervention 2020 10","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 Lichtman","Hanspeter Pfister"],"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 (30um)^3 volumes from human and rat cortices respectively, 3,600x 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 45x 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.","url_abs":"https://donglaiw.github.io/paper/2020_miccai_mitoEM.pdf","url_pdf":"https://donglaiw.github.io/paper/2020_miccai_mitoEM.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"mitoem-dataset-large-scale-3d-mitochondria","repo_url":"https://github.com/zudi-lin/pytorch_connectomics","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"3d-instance-segmentation-1","task_name":"3D Instance Segmentation"},{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"instance-segmentation","task_name":"Instance Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/3d-instance-segmentation-on-mitoem","task":"3D Instance Segmentation","dataset":"MitoEM","model":"Unet3D","rank_in_archive_order":2,"of":2,"metrics":{"AP75-H-Test":"0.804","AP75-R-Test":"0.816"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}