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PyRaDiSe: A Python package for DICOM-RT-based auto-segmentation pipeline construction and DICOM-RT data conversion.

Elias Rüfenacht¹, Amith Kamath¹, Yannick Suter¹

¹ARTORG Center for Biomedical Engineering Research, University of Bern, Murtenstrasse 50, Bern 3008, Switzerland.

Computer Methods and Programs in Biomedicine

|February 4, 2023

Summary

This summary is machine-generated.

PyRaDiSe is an open-source Python package that facilitates the clinical application of deep learning for medical image auto-segmentation. It enables seamless processing of DICOM RT Structure Sets, bridging the gap between data science and radiotherapy practice.

Keywords:

Auto-segmentation DICOM DICOM RT structure sets DICOM RTSS conversion Deep learning Radiotherapy

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Area of Science:

Radiotherapy
Medical Imaging
Deep Learning
Data Science

Background:

Deep learning in radiotherapy auto-segmentation faces challenges due to limited open-source frameworks for DICOM RT Structure Sets.
Existing open-source converters often use 2D reconstruction, resulting in pixelated contours unsuitable for clinical use.

Purpose of the Study:

To introduce PyRaDiSe, an open-source Python package designed to enable deep learning auto-segmentation solutions directly on DICOM data.
To provide robust DICOM RT Structure Set conversion and processing capabilities for tasks like dataset construction.

Main Methods:

PyRaDiSe offers a holistic approach including DICOM data handling (2D/3D conversion), deep learning model inference, and pre/post-processing.
The package supports any deep learning framework and includes routines for restoring spatial properties.
It facilitates automated and flexible handling of DICOM image series, RT Structure Sets, and registrations.

Main Results:

PyRaDiSe enables fast deployment and prototyping of auto-segmentation pipelines, reducing implementation efforts.
Deep learning model inference is framework-independent and integrates with frameworks like PyTorch and TensorFlow.
Successfully demonstrated in a research project for organs-at-risk segmentation in brain tumor patients and for dataset construction.

Conclusions:

PyRaDiSe bridges the gap between data science and clinical radiotherapy, facilitating the transfer of deep learning segmentation models into practice.
The package is available on GitHub and installable via pip.