biostudies-literatureUnknownSelvaraj JNIGMS NIH HHSCryogenic 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 experimbioRxiv : the preprint server for biology2025.11.23.689996https://www.ebi.ac.uk/biostudies/studies/S-EPMC12697732biostudies-literatureETSAM: Effectively Segmenting Cell Membranes in cryo-Electron Tomograms.PMC12697732R01 GM146340Selvaraj JCheng JfalseETSAM: Effectively Segmenting Cell Membranes in cryo-Electron Tomograms.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 experim2025-01-01T00:00:00Z2025 Nov2026-06-04T03:18:33.769Z2026-06-04T03:12:08.073ZS-EPMC126977324139469710.1101/2025.11.23.689996