AI summaryⓘ
The authors developed a new method called TomoROIS-SurfORA to better find and study specific parts of 3D biological images, especially tricky and complex shapes like membranes. Instead of analyzing the whole structure at once, their method directly identifies important areas and measures features like curve and roughness using computer algorithms. This works even with small examples for training and handles common data gaps in the imaging process. They tested it on lab-made membrane systems with complex shapes and showed it can automatically analyze how membranes interact and change shape. Although designed for cryo-electron tomography, their approach could help analyze images in other scientific fields too.
Cryo-electron tomographyRegion of interest (ROI)Membrane segmentationDeep learningSurface meshPoint cloudMembrane curvatureMissing wedge effectMembrane contact sitesMorphological analysis
Authors
Xingyi Cheng, Julien Maufront, Aurélie Di Cicco, Daniël M. Pelt, Manuela Dezi, Daniel Lévy
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.