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

ABSTRACT: It is now possible to generate large volumes of high-quality images of biomolecules at near-atomic resolution and in near-native states using cryogenic electron microscopy/electron tomography (Cryo-EM/ET). However, the precise annotation of structures like filaments and membranes remains a major barrier towards applying these methods in high-throughput. To address this, we present TARDIS (Transformer-based Rapid Dimensionless Instance Segmentation), a machine-learning framework for fast and accurate annotation of micrographs and tomograms. TARDIS combines deep learning for semantic segmentation with a novel geometric model for precise instance segmentation of various macromolecules. We develop pre-trained models within TARDIS for segmenting microtu

SUBMITTER: Kiewisz R 

PROVIDER: S-EPMC11702698 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature