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

2026-02-24

AI Summary

This work addresses the limitations of conventional approaches that rely on complete membrane segmentation and post-processing, which struggle to efficiently analyze geometrically complex and continuous membrane systems. The authors propose TomoROIS-SurfORA, a two-stage framework: first, a few-shot deep learning model, TomoROIS, enables shape-agnostic, direct region-of-interest (ROI) segmentation without requiring prior knowledge of full structural context; second, SurfORA integrates point cloud and surface mesh techniques to uniformly quantify morphological features—including curvature, intermembrane spacing, and surface roughness—for both open and closed membrane surfaces. This method effectively mitigates challenges posed by the missing wedge artifact in cryo-electron tomography and has been successfully applied to in vitro reconstituted vesicle systems, automatically identifying membrane contact sites and invagination events. The framework demonstrates strong potential for broader applicability across scientific imaging domains.

Technology Category

• Computer Vision: Segmentation
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Data transparency and provenance
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

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.

Problem

• Region of Interest Segmentation
• Morphological Analysis
• Cryo-Electron Tomography
• Membrane Structures
• Surface Analysis

Innovation

• ROI segmentation
• deep learning
• morphological analysis
• surface mesh
• cryo-electron tomography