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Python-based software for generalized object instance segmentation from (cryo-)electron microscopy micrographs/tomograms. The software package is built on a general workflow where predicted semantic segmentation is used for instance segmentation of 2D/3D images.

Features

Robust and high-throughput semantic/instance segmentation of all microtubules:
- Supported file formats: [.tif, .mrc, .rec, .am]
- Supported modality: [ET, Cryo-ET]
- Supported Å resolution: [any best results in 1-40 Å range]
- 2D micrograph modality microtubule segmentation will come soon!
Robust and high-throughput semantic/instance segmentation of membranes:
- Supported file formats: [.tif, .mrc, .rec, .am]
- Supported modality: [EM, ET, Cryo-EM, Cryo-ET]
- Supported Å resolution: [all]
High-throughput semantic/instance segmentation of actin [Beta]
Fully automatic segmentation solution!
Napari plugin
Cloud computing [Coming soon]

Citation

DOI [Microscopy and Microanalysis]

Kiewisz R., Fabig G., Müller-Reichert T. Bepler T. 2023. Automated Segmentation of 3D Cytoskeletal Filaments from Electron Micrographs with TARDIS. Microscopy and Microanalysis 29(Supplement_1):970-972.

Link: NeurIPS 2022 MLSB Workshop

Kiewisz R., Bepler T. 2022. Membrane and microtubule rapid instance segmentation with dimensionless instance segmentation by learning graph representations of point clouds. Neurips 2022 - Machine Learning for Structural Biology Workshop.

What’s new?

Full History

TARDIS-em v0.3.0 (2024-09-11):

Added general predictor for microtubule filaments from fluorescent microscopes [TIRF]
Added Napari plugin support for training, predictions and corrections of filaments instances

Quick Start

For more examples and advanced usage please find more details in our Documentation

Install TARDIS-em:

Install pytorch with GPU support as per Pytorch official website: https://pytorch.org/get-started/locally/

pip install tardis-em

Verifies installation:

tardis

Optional Napari plugin installation

pip install napari-tardis-em

Filaments Prediction

3D Actin prediction

Full tutorial: 3D Actin Prediction

Usage:

recommended usage: tardis_actin [-dir path/to/folder/with/input/tomogram]
advance usage: tardis_actin [-dir str] [-out str] [-ps int] [-ct float] [-dt float]
                         [-pv int] [-px float] ...

2D Microtubule prediction

TBD

3D Microtubule prediction

Full tutorial: 3D Microtubules Prediction

Example:

Data source: Dr. Gunar Fabig and Prof. Dr. Thomas Müller-Reichert, TU Dresden

Usage:

recommended usage: tardis_mt [-dir path/to/folder/with/input/tomogram]
advance usage: tardis_mt [-dir str] [-out str] [-ps int] [-ct float] [-dt float]
                         [-pv int] [-px float] ...

TIRF Microtubule prediction

Full tutorial: TIRF Microtubules Prediction

Example:

Data source: RNDr. Cyril Bařinka, Ph.D, Biocev

Usage:

recommended usage: tardis_mt_tirf [-dir path/to/folder/with/input/data]
advance usage: tardis_mt [-dir str] [-out str] [-ps int] [-ct float] [-dt float]
                         [-pv int] ...

Membrane Prediction

2D prediction

Full tutorial: 2D Membrane Prediction

Example:

Data source: Dr. Victor Kostyuchenko and Prof. Dr. Shee-Mei Lok, DUKE-NUS Medical School Singapore

Usage:

recommended usage: tardis_mem2d [-dir path/to/folder/with/input/tomogram] -out mrc_csv
advance usage: tardis_mem [-dir str] [-out str] [-ps int] ...

3D prediction

Full tutorial: 3D Membrane Prediction

Example:

Data source: EMPIRE-10236, DOI: 10.1038/s41586-019-1089-3

Usage:

recommended usage: tardis_mem [-dir path/to/folder/with/input/tomogram] -out mrc_csv
advance usage: tardis_mem [-dir str] [-out str] [-ps int] ...

Usage:

Documentation: