Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas; UTHealth Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, Texas.
UTHealth Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, Texas; Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
Pract Radiat Oncol. 2021 May-Jun;11(3):226-229. doi: 10.1016/j.prro.2021.02.003. Epub 2021 Feb 17.
Deep learning is becoming increasingly popular and available to new users, particularly in the medical field. Deep learning image segmentation, outcome analysis, and generators rely on presentation of Digital Imaging and Communications in Medicine (DICOM) images and often radiation therapy (RT) structures as masks. Although the technology to convert DICOM images and RT structures into other data types exists, no purpose-built Python module for converting NumPy arrays into RT structures exists. The 2 most popular deep learning libraries, Tensorflow and PyTorch, are both implemented within Python, and we believe a set of tools built in Python for manipulating DICOM images and RT structures would be useful and could save medical researchers large amounts of time and effort during the preprocessing and prediction steps. Our module provides intuitive methods for rapid data curation of RT-structure files by identifying unique region of interest (ROI) names and ROI structure locations and allowing multiple ROI names to represent the same structure. It is also capable of converting DICOM images and RT structures into NumPy arrays and SimpleITK Images, the most commonly used formats for image analysis and inputs into deep learning architectures and radiomic feature calculations. Furthermore, the tool provides a simple method for creating a DICOM RT-structure from predicted NumPy arrays, which are commonly the output of semantic segmentation deep learning models. Accessing DicomRTTool via the public Github project invites open collaboration, and the deployment of our module in PyPi ensures painless distribution and installation. We believe our tool will be increasingly useful as deep learning in medicine progresses.
深度学习越来越受到新用户的欢迎，尤其是在医学领域。深度学习的图像分割、结果分析和生成器依赖于数字成像和通信医学（DICOM）图像的呈现，并且通常依赖于放射治疗（RT）结构作为掩模。虽然将 DICOM 图像和 RT 结构转换为其他数据类型的技术已经存在，但没有专门用于将 NumPy 数组转换为 RT 结构的 Python 模块。两个最流行的深度学习库，Tensorflow 和 PyTorch，都在 Python 内部实现，我们相信在 Python 中构建一组用于处理 DICOM 图像和 RT 结构的工具将是有用的，可以在预处理和预测步骤中为医学研究人员节省大量的时间和精力。我们的模块提供了直观的方法，通过识别唯一的感兴趣区域（ROI）名称和 ROI 结构位置，快速管理 RT 结构文件的数据，允许多个 ROI 名称表示相同的结构。它还能够将 DICOM 图像和 RT 结构转换为 NumPy 数组和 SimpleITK Images，这是图像分析和深度学习架构输入以及放射组学特征计算中最常用的格式。此外，该工具提供了一种从预测的 NumPy 数组创建 DICOM RT 结构的简单方法，这些数组通常是语义分割深度学习模型的输出。通过公共 Github 项目访问 DicomRTTool 可以邀请开放协作，并且在 PyPi 中部署我们的模块可以确保轻松分发和安装。我们相信，随着医学领域深度学习的发展，我们的工具将变得越来越有用。
