Python package to parse Tomocube TCF (Tomocube Common Format) files to OME-TIFF format. Link to this repo
Holotomography is a microscopy technique to produce label-free 3D images of cells using quantitative phase imaging. The Tomocube HT-2H is based on this principle. The images produced with the software provided (TomoStudio) are stored as TCF files (Tomocube Common Format). This project provides a parser that translates this format into a standardized OME-TIFF file where the image is stored as tiff and metadata is compliant with OME-XML specifications.
python -m pip install .
Example data can be found in the examples folder.
An overall config file is provided as examples/overall_config.txt. See description further below.
-
Parse a single file that resides in folder folder with the same name as the folder and the extension .TCF:
python tcf_to_ometiff/cli.py parse <folder> <overall config file path> -
Parse multiple files that reside in subfolders of folder top_folder, each one having the same name as the subfolder and the extension .TCF:
python tcf_to_ometiff/cli.py parse-multiple <top_folder> <overall config file path> -
If you want to output the
.ome.xmlfor the image as a separate file, append the option--output-xmlto the command from above.
- For a single file that resides in folder 20220131.150824.759.Default-001 with the same name as the folder and the extension .TCF:
import tcf_to_ometiff
config_file_path = "./examples/overall_config.txt"
folder = "20220131.150824.759.Default-001"
overall_md = tcf_to_ometiff.create_overall_config(config_file_path)
tcf_to_ometiff.transform_tcf(folder, overall_md, output_xml=False)
- For multiple files, each coming in one folder that resides in a top folder (here:
examples):
import tcf_to_ometiff
config_file_path = "./examples/overall_config.txt"
top_folder = "examples/"
tcf_to_ometiff.transform_folder(top_folder, config_file_path, output_xml=False)
- If you want to output the
.ome.xmlfor the image(s) as a separate file, change theoutput_xmlparameter toTrue.
The overall config file provides values for different meta data such as details about the experimenter and the project
that do not follow from the per-image metadata that are automatically created. An exemplary config file with all
necessary values is provided as examples/overall_config.txt.
This file is not to be confused with the other config files called config.dat and JobParameter.tcp that reside in
the image folder and that are automatically generated by TomoStudio during image acquisition. Both the overall and the
per-image config files are needed for the parsing.
The validation of the correct format of an OME-TIFF XML header is described here.
aicsimageio==4.14.0
h5py==3.10.0
numpy==1.26.4
ome-types==0.4.5
typer==0.9.0
- Add support for tiling
Thanks to the OME team for their work on imaging standardization and reproducibility. Special thanks to Josh Moore, Talley Lambert, Dan, Sébastien Besson and Christoph Gohlke, who provided support to help me understand the OME XML schema in this thread on the image.sc forum.
If this work is helpful for your research, you can cite the corresponding paper:
@article{ZWIRNMANN20236477,
title = {Towards End-to-End Automated Microscopy Control using Holotomography: Workflow Design and Data Management},
journal = {IFAC-PapersOnLine},
volume = {56},
number = {2},
pages = {6477-6483},
year = {2023},
note = {22nd IFAC World Congress},
issn = {2405-8963},
doi = {https://doi.org/10.1016/j.ifacol.2023.10.862},
url = {https://www.sciencedirect.com/science/article/pii/S2405896323012429},
author = {Henning Zwirnmann and Dennis Knobbe and Sami Haddadin},
keywords = {Biomedical and medical image processing and systems, Bio-signals analysis and interpretation, Bioinformatics, Human centred automation},
abstract = {Microscopy has been a key tool involved in many discoveries in the life sciences over the past centuries. In the last 30 years in particular, enormous progress has been made in developing this measurement technique further to make researchers working with it more effective. To combine gains in reproducibility and efficiency resulting from these advancements in different research areas, we present for the first time a unified and comprehensive concept for an end-to-end automated microscopy workflow. To this end, we employ both robotic and computational methods as well as holotomography microscopy. Considering the physical preparation and cleanup of a measurement, the image acquisition, and the management and analysis of the resulting data, we give a fine-grained workflow description. We present the robotic system to perform the manual process steps and a Python package to standardize the resulting proprietary image (meta)data. For the other tasks, we identify suitable open-source tools to execute them and apply them to our setup. The choice of holotomography as a suitable microscopy technique to realize this workflow is elucidated. We envision that the adoption of an automated workflow paves the way toward a future life science laboratory where microscopy-based research is carried out more efficiently and reproducibly than in the past.}
}
MIT