Region of Interest Segmentation and Morphological Analysis for Membranes in Cryo-Electron Tomography

TL;DR

This paper introduces TomoROIS-SurfORA, a novel framework combining deep learning-based ROI segmentation and morphological surface analysis for membranes in cryo-electron tomography, enabling automated, shape-agnostic, quantitative structural analysis.

Contribution

The authors present a two-step framework that performs direct ROI segmentation and morphological analysis, adaptable to diverse data with small annotated datasets and capable of analyzing complex membrane geometries.

Findings

Effective segmentation of membrane ROIs using deep learning.

Quantitative analysis of membrane morphology including curvature and roughness.

Application to complex membrane systems demonstrates utility in biological imaging.

Abstract

Cryo-electron tomography (cryo-ET) enables high resolution, three-dimensional reconstruction of biological structures, including membranes and membrane proteins. Identification of regions of interest (ROIs) is central to scientific imaging, as it enables isolation and quantitative analysis of specific structural features within complex datasets. In practice, however, ROIs are typically derived indirectly through full structure segmentation followed by post hoc analysis. This limitation is especially apparent for continuous and geometrically complex structures such as membranes, which are segmented as single entities. Here, we developed TomoROIS-SurfORA, a two step framework for direct, shape-agnostic ROI segmentation and morphological surface analysis. TomoROIS performs deep learning-based ROI segmentation and can be trained from scratch using small annotated datasets, enabling practical application across diverse imaging data. SurfORA processes segmented structures as point clouds and surface meshes to extract quantitative morphological features, including inter-membrane distances, curvature, and surface roughness. It supports both closed and open surfaces, with specific considerations for open surfaces, which are common in cryo-ET due to the missing wedge effect. We demonstrate both tools using in vitro reconstituted membrane systems containing deformable vesicles with complex geometries, enabling automatic quantitative analysis of membrane contact sites and remodeling events such as invagination. While demonstrated here on cryo-ET membrane data, the combined approach is applicable to ROI detection and surface analysis in broader scientific imaging contexts.