{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"submitter":["Selvaraj J"],"funding":["NIGMS NIH HHS"],"pubmed_abstract":["Cryogenic Electron Tomography (cryo-ET) is an emerging experimental technique to visualize cell structures and macromolecules in their native cellular environment. Accurate segmentation of cell structures in cryo-ET tomograms, such as cell membranes, is crucial to advance our understanding of cellular organization and function. However, several inherent limitations in cryo-ET tomograms, including the very low signal-to-noise ratio, missing wedge artifacts from limited tilt angles, and other noise artifacts, collectively hinder the reliable identification and delineation of these structures. In this study, we introduce ETSAM - a two-stage SAM2-based (1) fine-tuned AI model that effectively segments cell membranes in cryo-ET tomograms. It is trained on a diverse dataset comprising 83 experim"],"journal":["bioRxiv : the preprint server for biology"],"pagination":["2025.11.23.689996"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12697732"],"repository":["biostudies-literature"],"pubmed_title":["ETSAM: Effectively Segmenting Cell Membranes in cryo-Electron Tomograms."],"pmcid":["PMC12697732"],"funding_grant_id":["R01 GM146340"],"pubmed_authors":["Selvaraj J","Cheng J"],"additional_accession":[]},"is_claimable":false,"name":"ETSAM: Effectively Segmenting Cell Membranes in cryo-Electron Tomograms.","description":"Cryogenic Electron Tomography (cryo-ET) is an emerging experimental technique to visualize cell structures and macromolecules in their native cellular environment. Accurate segmentation of cell structures in cryo-ET tomograms, such as cell membranes, is crucial to advance our understanding of cellular organization and function. However, several inherent limitations in cryo-ET tomograms, including the very low signal-to-noise ratio, missing wedge artifacts from limited tilt angles, and other noise artifacts, collectively hinder the reliable identification and delineation of these structures. In this study, we introduce ETSAM - a two-stage SAM2-based (1) fine-tuned AI model that effectively segments cell membranes in cryo-ET tomograms. It is trained on a diverse dataset comprising 83 experim","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Nov","modification":"2026-06-04T03:18:33.769Z","creation":"2026-06-04T03:12:08.073Z"},"accession":"S-EPMC12697732","cross_references":{"pubmed":["41394697"],"doi":["10.1101/2025.11.23.689996"]}}