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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.

.. image:: resources/workflow.png

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 <https://github.com/SMLC-NYSBC/napari-tardis_em/>__

• Cloud computing [Coming soon]

Citation

DOI [BioRxiv] <http://doi.org/10.1101/2024.12.19.629196>__

Kiewisz R. et.al. 2024. Accurate and fast segmentation of filaments and membranes in micrographs and tomograms with TARDIS.

DOI [Microscopy and Microanalysis] <http://dx.doi.org/10.1093/micmic/ozad067.485>__

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 <https://www.mlsb.io/papers_2022/Membrane_and_microtubule_rapid_instance_segmentation_with_dimensionless_instance_segmentation_by_learning_graph_representations_of_point_clouds.pdf>__

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 <https://smlc-nysbc.github.io/TARDIS/HISTORY.html>__

TARDIS-em v0.3.10 (2025-01-07): * Models update * Progress with estimated ETA time * Small bugfixes

Quick Start

For more examples and advanced usage please find more details in our Documentation <https://smlc-nysbc.github.io/TARDIS/>__

1. Install TARDIS-em:

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

.. code-block:: bash

pip install tardis-em

2. Verifies installation:

.. code-block:: bash

tardis

3. Optional Napari plugin installation

.. code-block:: bash

pip install napari-tardis-em

Filaments Prediction

3D Actin prediction ^^^^^^^^^^^^^^^^^^^ Full tutorial: 3D Actin Prediction <https://smlc-nysbc.github.io/TARDIS/usage/3d_actin.html>__

Usage: """"""

.. code-block:: bash

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 <https://smlc-nysbc.github.io/TARDIS/usage/3d_mt.html>__

Example: """"""""

.. image:: resources/3d_mt.jpg

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

Usage: """"""

.. code-block:: bash

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 <https://smlc-nysbc.github.io/TARDIS/usage/tirf_mt.html>__

Example: """"""""

.. image:: resources/tirf_mt.png

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

Usage: """"""

.. code-block:: bash

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 <https://smlc-nysbc.github.io/TARDIS/usage/2d_membrane.html>__

Example: """"""""

.. image:: resources/2d_mem.jpg

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

Usage: """"""

.. code-block:: bash

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 <https://smlc-nysbc.github.io/TARDIS/usage/3d_membrane.html>__

Example: """"""""

.. image:: resources/3d_mem.jpg

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

Usage: """"""

.. code-block:: bash

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

MIT License

Copyright (c) 2022 Robert Kiewisz, Tristan Bepler

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.