Accurate and fast segmentation of filaments and membranes in micrographs and tomograms with TARDIS

Abstract

Segmentation of macromolecular structures is the primary bottleneck for studying biomolecules and their organization with electron microscopy in 2D/3D - requiring months of manual effort. Transformer-based Rapid Dimensionless Instance Segmentation (TARDIS) is a deep learning framework that automatically and accurately annotates membranes and filaments. Pre-trained TARDIS models can segment electron tomography (ET) reconstructions from both 3D and 2D electron micrographs of cryo and plastic-embedded samples. Furthermore, by implementing a novel geometric transformer architecture, TARDIS is the only method to provide accurate instance segmentations of these structures. Reducing the annotation time for ET data from months to minutes, we demonstrate segmentation of membranes and filaments in over 13,000 tomograms in the CZII Data Portal. TARDIS thus enables quantitative biophysical analysis at scale for the first time. We show this in application to kinetochore-microtubule attachment and viral-membrane interactions. TARDIS can be extended to new biomolecules and applications and open-source at https://github.com/SMLC-NYSBC/TARDIS.