PyDicer: PYthon Dicom Image ConvertER#

+SoftwareX

Welcome to PyDicer, a tool to ease the process of converting Radiotherapy DICOM data objects into a format typically used for research purposes. In addition to data conversion, functionality is provided to help analyse the data. This includes computing radiomic features, radiotherapy dose metrics and auto-segmentation metrics. PyDicer uses the NIfTI format to store data is a well defined file system structure. Tracking of these data objects in CSV files, also stored on the file system, provides an easy and flexible way to work with the converted data in your research.

The PyDicer documentation provides several examples and guides to help you get started with the tool. Here are a few PyDicer principles to keep in mind as you get started:

+PyDicer Working Directory structure +

PyDicer working directory structure (`Chlap, P. et al. SoftwareX <https://doi.org/10.1016/j.softx.2024.102010>`_)

Pipeline#

@@ -304,6 +307,11 @@

Getting Started +

How to Cite#

+

If you make use of PyDicer within your research work, please consider citing our SoftwareX paper:

+

**Chlap P, Al Mouiee D, Finnegan RN, et al. PyDicer: An open-source python library for conversion and analysis of radiotherapy DICOM data. SoftwareX. 2025;29:102010. doi:10.1016/j.softx.2024.102010**

+

Contributing#

PyDicer is an open-source tool and contributions are welcome! Here are some ways you might consider contributing to the project:

@@ -384,6 +392,7 @@

AuthorsDirectory Structure
  • Pipeline
  • Getting Started
    • +
  • How to Cite
  • Contributing
  • Authors
    diff --git a/objects.inv b/objects.inv index 0b1c9de..7159f75 100644 Binary files a/objects.inv and b/objects.inv differ diff --git a/searchindex.js b/searchindex.js index a8c2da2..a4f8b03 100644 --- a/searchindex.js +++ b/searchindex.js @@ -1 +1 @@ -Search.setIndex({"docnames": ["_examples/AutoSegmentation", "_examples/Configuration", "_examples/ConvertingData", "_examples/DatasetPreparation", "_examples/DoseMetrics", "_examples/GettingStarted", "_examples/ObjectGeneration", "_examples/Radiomics", "_examples/VisualiseData", "_examples/WorkingWithData", "_examples/WorkingWithStructures", "_examples/nnUNet", "analyse", "code_of_conduct", "config", "contributing", "convert", "dataset", "generate", "index", "input", "nnunet", "preprocess", "tool", "utils", "visualise"], "filenames": ["_examples/AutoSegmentation.ipynb", "_examples/Configuration.ipynb", "_examples/ConvertingData.ipynb", "_examples/DatasetPreparation.ipynb", "_examples/DoseMetrics.ipynb", "_examples/GettingStarted.ipynb", "_examples/ObjectGeneration.ipynb", "_examples/Radiomics.ipynb", "_examples/VisualiseData.ipynb", "_examples/WorkingWithData.ipynb", "_examples/WorkingWithStructures.ipynb", "_examples/nnUNet.ipynb", "analyse.rst", "code_of_conduct.rst", "config.rst", "contributing.rst", "convert.rst", "dataset.rst", "generate.rst", "index.rst", "input.rst", "nnunet.rst", "preprocess.rst", "tool.rst", "utils.rst", "visualise.rst"], "titles": ["Auto-segmentation Inference & Analysis", "Configuration", "Converting Data", "Dataset Preparation", "Dose Metrics", "Getting Started", "Generating Data Objects", "Compute Radiomics", "Visualise Data", "Working with Data", "Working with Structures", "nnUNet Data Preparation", "Analyse", "Code of Conduct", "Configuration", "Contributing", "Conversion", "Dataset Preparation", "Generation", "PyDicer: PYthon Dicom Image ConvertER", "Input", "nnUNet", "Preprocessing", "PyDicer", "Utils", "Visualisation"], "terms": {"A": [0, 1, 2, 3, 5, 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