# ETSAM: Effectively Segmenting Cell Membranes in cryo-Electron Tomograms

## TL;DR

This paper introduces ETSAM, an AI method that improves the segmentation of cell membranes in cryo-electron tomograms, overcoming challenges like noise and missing data.

## Contribution

The novel contribution is ETSAM, a two-stage AI model based on SAM2, which achieves state-of-the-art performance in segmenting cell membranes from cryo-ET data.

## Key findings

- ETSAM was trained on 83 experimental and 28 simulated cryo-ET tomograms, achieving high sensitivity and precision.
- It outperforms existing deep learning methods in segmenting cell membranes in cryo-ET tomograms.
- ETSAM demonstrates robust performance on an independent test set of 10 experimental tomograms with ground-truth annotations.

## 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 Segment Anything Model 2 (SAM2)-based fine-tuned AI method that effectively segments cell membranes in cryo-ET tomograms. It is trained on a diverse dataset comprising 83 experimental tomograms from the CryoET Data Portal (CDP) database and 28 simulated tomograms generated using PolNet. ETSAM achieves state-of-the-art performance on an independent test set comprising 10 experimental tomograms for which ground-truth annotations are available. It robustly segments cell membranes with high sensitivity and precision, significantly outperforming existing deep learning methods.