PyDicer: An open-source python library for conversion and analysis of radiotherapy DICOM data
Phillip Chlap,
Daniel Al Mouiee,
Robert N Finnegan,
Janet Cui,
Vicky Chin,
Shrikant Deshpande,
Lois Holloway
Affiliations
Phillip Chlap
South Western Sydney Clinical School, University of New South Wales, Sydney, Australia; Ingham Institute for Applied Medical Research, Sydney, Australia; Department of Radiation Oncology, Liverpool and Macarthur Cancer Therapy Centres, Sydney, Australia; Corresponding author.
Daniel Al Mouiee
South Western Sydney Clinical School, University of New South Wales, Sydney, Australia; Ingham Institute for Applied Medical Research, Sydney, Australia; Department of Radiation Oncology, Liverpool and Macarthur Cancer Therapy Centres, Sydney, Australia
Robert N Finnegan
Royal North Shore Hospital, Sydney, Australia; Institute of Medical Physics, University of Sydney, Australia
Janet Cui
Ingham Institute for Applied Medical Research, Sydney, Australia
Vicky Chin
South Western Sydney Clinical School, University of New South Wales, Sydney, Australia; Ingham Institute for Applied Medical Research, Sydney, Australia; Department of Radiation Oncology, Liverpool and Macarthur Cancer Therapy Centres, Sydney, Australia; Image X Institute, University of Sydney, Sydney, Australia
Shrikant Deshpande
South Western Sydney Clinical School, University of New South Wales, Sydney, Australia; Ingham Institute for Applied Medical Research, Sydney, Australia; Department of Radiation Oncology, Liverpool and Macarthur Cancer Therapy Centres, Sydney, Australia
Lois Holloway
South Western Sydney Clinical School, University of New South Wales, Sydney, Australia; Ingham Institute for Applied Medical Research, Sydney, Australia; Department of Radiation Oncology, Liverpool and Macarthur Cancer Therapy Centres, Sydney, Australia; Institute of Medical Physics, University of Sydney, Australia
The organisation, conversion, cleaning and processing of DICOM data is an ongoing challenge across medical image analysis research projects. PyDicer (PYthon Dicom Image ConvertER) was created as a generalisable tool for use across a variety of radiotherapy research projects. This includes the conversion of DICOM objects into a standardised form as well as functionality to visualise, clean and analyse the converted data. The generalisability of PyDicer has been demonstrated by its use across a range of projects including the analysis of radiotherapy dose metrics and radiomics features as well as auto-segmentation training, inference and validation.
