{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T23:51:43Z","timestamp":1787010703862,"version":"build-2736575974"},"posted":{"date-parts":[[2025,11,13]]},"group-title":"Neuroscience","reference-count":13,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T00:00:00Z","timestamp":1762992000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"award":["NSF-IIS-2239688"],"award-info":[{"award-number":["NSF-IIS-2239688"]}],"id":[{"id":"https:\/\/ror.org\/021nxhr62","id-type":"ROR","asserted-by":"publisher"}]},{"award":["PID2024-157485NB-I00"],"award-info":[{"award-number":["PID2024-157485NB-I00"]}],"id":[{"id":"https:\/\/ror.org\/05r0vyz12","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2025,12,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>We present MitoEM 2.0, a curated data resource for training and evaluating three-dimensional (3D) mitochondria instance segmentation in volume electron microscopy. The collection assembles multiscale vEM datasets (FIB-SEM, SBF-SEM, ssSEM) spanning diverse tissues and species, with expert-verified instance labels emphasizing biologically difficult scenarios, including dense mitochondrial packing, hyperfused networks, and thin filamentous connections with ambiguous boundaries. All releases include native-resolution volumes and standardized processed versions, per-volume metadata (voxel size, modality, tissue, splits), and official train\/validation\/test partitions to enable reproducible benchmarking. Annotations follow a consistent protocol with quality checks and instance reindexing. Data are provided in NIfTI with nnU-Net\u2013compatible layout, alongside machine-readable split files and checksums. Baseline scripts support common training pipelines and size-stratified evaluation. By consolidating challenging volumes and harmonized labels, MitoEM 2.0 facilitates robust model development and fair comparison across methods while supporting reuse in bioimage analysis, algorithm benchmarking, and teaching.<\/jats:p>","DOI":"10.1101\/2025.11.12.687478","type":"posted-content","created":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T04:10:12Z","timestamp":1763093412000},"source":"Crossref","is-referenced-by-count":1,"title":["MitoEM 2.0: A Benchmark for Challenging 3D Mitochondria Instance Segmentation from EM Images"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5437-1294","authenticated-orcid":false,"given":"Peng","family":"Liu","sequence":"first","affiliation":[{"name":"Boston College"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Boyu","family":"Shen","sequence":"additional","affiliation":[{"name":"Boston College"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liyuan","family":"Liu","sequence":"additional","affiliation":[{"name":"Boston College"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiong","family":"Wang","sequence":"additional","affiliation":[{"name":"Boston College"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shulin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Stanford University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abhishek","family":"Bhardwaj","sequence":"additional","affiliation":[{"name":"National Cancer Institute"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ignacio","family":"Arganda-Carreras","sequence":"additional","affiliation":[{"name":"Donostia International Physics Center"},{"name":"University of the Basque Country"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7982-6494","authenticated-orcid":false,"given":"Kedar","family":"Narayan","sequence":"additional","affiliation":[{"name":"National Cancer Institute"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2329-5484","authenticated-orcid":false,"given":"Donglai","family":"Wei","sequence":"additional","affiliation":[{"name":"Boston College"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"54368","reference":[{"issue":"5","key":"2026010105262287000_2025.11.12.687478v3.1","first-page":"1096","article-title":"Learning structured models for segmentation of 2-D and 3-D imagery","volume":"34","year":"2014","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"2026010105262287000_2025.11.12.687478v3.2","unstructured":"V. Casser , K. Kang , H. Pfister , and D. Haehn , \u201cFast mitochondria detection for connectomics,\u201d in Medical Imaging with Deep Learning, 2020."},{"issue":"1","key":"2026010105262287000_2025.11.12.687478v3.3","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.cels.2022.12.006","article-title":"Instance segmentation of mitochondria in electron microscopy images with a generalist deep learning model trained on a diverse dataset","volume":"14","year":"2023","journal-title":"Cell Systems"},{"key":"2026010105262287000_2025.11.12.687478v3.4","doi-asserted-by":"crossref","unstructured":"D. Wei , Z. Lin , D. Franco-Barranco , N. Wendt , X. Liu , W. Yin , X. Huang , A. Gupta , W.-D. Jang , X. Wang et al., \u201cMitoEM dataset: Large-scale 3D mitochondria instance segmentation from EM images,\u201d in International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). 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