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Automated Segmentation of 3D Cytoskeletal Filaments from Electron Micrographs with TARDIS

2023; Oxford University Press; Volume: 29; Issue: Supplement_1 Linguagem: Inglês

10.1093/micmic/ozad067.485

1435-8115

Robert Kiewisz, Gunar Fabig, Thomas Müller‐Reichert, Tristan Bepler,

Molecular Biology Techniques and Applications

3D segmentation of cytoskeletal filaments and organelles is crucial for studying these structures in cellular (cryo-) electron microscopy (EM) and tomography (ET).Manual annotation remains the gold standard for labeling these objects, due to the limited accuracy of available tools.Existing semi-automatic [1] or fully automatic approaches (e.g.[2]-[4]) can speed up the process, but often require extensive case-by-case tuning by users or significant manual correction of their outputs.In order to scale analysis to the growing number of micrographs and tomograms and enable precise quantification of biological structures, high-accuracy automatic segmentation algorithms are required.Segmentation is commonly separated into two categories: semantic segmentation, in which objects of interest are separated from other uninteresting signals, and instance segmentation, in which multiple objects of interest are distinguished from each other.Existing segmentation methods primarily focus on the semantic segmentation problem and do not distinguish between individual instances such as microtubule (MT) filament or contiguous membrane.While simple approaches to filament instance segmentation have been proposed [5], they are inadequate in complex environments and sensitive to semantic segmentation errors.Therefore, there is a need not only for more accurate semantic segmentation algorithms but also instance segmentation methods.Here, we present TARDIS (Transformer and Rapid Dimensionless Instance Segmentation), a fully automatic segmentation workflow designed to overcome these challenges.TARDIS provides modular solutions for semantic and instance segmentation, enabling complete annotation of micrographs and tomograms.For semantic segmentation, we propose an Unet variant that enables fast and accurate pixel-level classification.For instance segmentation, we propose a graph formulation to identify filamentlike structures from point clouds obtained from semantic segmentation.This network allows flexible geometry specifications and TARDIS will be extended in the future to include pre-trained instance segmentation networks for organelles.Semantic and instance segmentation modules allow for new semantic segmentation models for organelles, or other structures of interest to be easily inserted into the pipeline without the need to retrain the instance segmentation network, and vice versa.The TARDIS workflow consists of three main steps: semantic segmentation which produces a semantic mask, post-processing of the semantic mask into a point cloud representation of the objects, and instance segmentation of the point cloud (Figure 1A).