Rüfenacht Elias, Kamath Amith, Suter Yannick, Poel Robert, Ermiş Ekin, Scheib Stefan, Reyes Mauricio
ARTORG Center for Biomedical Engineering Research, University of Bern, Murtenstrasse 50, Bern 3008, Switzerland.
ARTORG Center for Biomedical Engineering Research, University of Bern, Murtenstrasse 50, Bern 3008, Switzerland.
Comput Methods Programs Biomed. 2023 Apr;231:107374. doi: 10.1016/j.cmpb.2023.107374. Epub 2023 Jan 28.
Despite fast evolution cycles in deep learning methodologies for medical imaging in radiotherapy, auto-segmentation solutions rarely run in clinics due to the lack of open-source frameworks feasible for processing DICOM RT Structure Sets. Besides this shortage, available open-source DICOM RT Structure Set converters rely exclusively on 2D reconstruction approaches leading to pixelated contours with potentially low acceptance by healthcare professionals. PyRaDiSe, an open-source, deep learning framework independent Python package, addresses these issues by providing a framework for building auto-segmentation solutions feasible to operate directly on DICOM data. In addition, PyRaDiSe provides profound DICOM RT Structure Set conversion and processing capabilities; thus, it applies also to auto-segmentation-related tasks, such as dataset construction for deep learning model training.
The PyRaDiSe package follows a holistic approach and provides DICOM data handling, deep learning model inference, pre-processing, and post-processing functionalities. The DICOM data handling allows for highly automated and flexible handling of DICOM image series, DICOM RT Structure Sets, and DICOM registrations, including 2D-based and 3D-based conversion from and to DICOM RT Structure Sets. For deep learning model inference, extending given skeleton classes is straightforwardly achieved, allowing for employing any deep learning framework. Furthermore, a profound set of pre-processing and post-processing routines is included that incorporate partial invertibility for restoring spatial properties, such as image origin or orientation.
The PyRaDiSe package, characterized by its flexibility and automated routines, allows for fast deployment and prototyping, reducing efforts for auto-segmentation pipeline implementation. Furthermore, while deep learning model inference is independent of the deep learning framework, it can easily be integrated into famous deep learning frameworks such as PyTorch or Tensorflow. The developed package has successfully demonstrated its capabilities in a research project at our institution for organs-at-risk segmentation in brain tumor patients. Furthermore, PyRaDiSe has shown its conversion performance for dataset construction.
The PyRaDiSe package closes the gap between data science and clinical radiotherapy by enabling deep learning segmentation models to be easily transferred into clinical research practice. PyRaDiSe is available on https://github.com/ubern-mia/pyradise and can be installed directly from the Python Package Index using pip install pyradise.
尽管放射治疗中用于医学成像的深度学习方法的发展周期很快，但由于缺乏适用于处理DICOM RT结构集的开源框架，自动分割解决方案很少在临床中运行。除了这一不足之外，现有的开源DICOM RT结构集转换器完全依赖二维重建方法，导致轮廓呈像素化，医疗保健专业人员的接受度可能较低。PyRaDiSe是一个独立于深度学习框架的开源Python包，通过提供一个用于构建可直接对DICOM数据进行操作的自动分割解决方案的框架来解决这些问题。此外，PyRaDiSe还提供了强大的DICOM RT结构集转换和处理能力；因此，它也适用于与自动分割相关的任务，如深度学习模型训练的数据集构建。
PyRaDiSe包采用整体方法，提供DICOM数据处理、深度学习模型推理、预处理和后处理功能。DICOM数据处理允许对DICOM图像序列、DICOM RT结构集和DICOM注册进行高度自动化和灵活的处理，包括基于二维和三维的DICOM RT结构集的转换。对于深度学习模型推理，扩展给定的骨架类很容易实现，允许使用任何深度学习框架。此外，还包括一组强大的预处理和后处理例程，这些例程包含部分可逆性以恢复空间属性，如图像原点或方向。
PyRaDiSe包以其灵活性和自动化例程为特点，允许快速部署和原型设计，减少了自动分割管道实施的工作量。此外，虽然深度学习模型推理独立于深度学习框架，但它可以很容易地集成到著名的深度学习框架中，如PyTorch或TensorFlow。开发的包已在我们机构的一个研究项目中成功展示了其在脑肿瘤患者危险器官分割方面的能力。此外，PyRaDiSe还展示了其在数据集构建方面的转换性能。
PyRaDiSe包通过使深度学习分割模型能够轻松转移到临床研究实践中，弥合了数据科学与临床放射治疗之间的差距。PyRaDiSe可在https://github.com/ubern-mia/pyradise上获取，可使用pip install pyradise直接从Python包索引中安装。
