Simple Python Module for Conversions Between DICOM Images and Radiation Therapy Structures, Masks, and Prediction Arrays.
Simple Python Module for Conversions Between DICOM Images and Radiation Therapy Structures, Masks, and Prediction Arrays.
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DOI：
10.1016/j.prro.2021.02.003
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发表时间：
2021-05
影响因子：
3.3
通讯作者：
Brock KK
中科院分区：
文献类型：
--
作者：
Anderson BM;Wahid KA;Brock KK
深度学习正变得越来越受欢迎，并可供新用户使用，特别是在医疗领域。深度学习图像分割、结果分析和生成器依赖于医学数字成像和通信（DICOM）图像的呈现，并且通常依赖于放射治疗（RT）结构作为掩模。虽然存在将DICOM图像和RT结构转换为其他数据类型的技术，但不存在用于将NumPyarray转换为RT结构的专用Python模块。两个最流行的深度学习库，Tensorflow和PyTorch，都是在Python中实现的，我们相信在Python中构建一套用于操作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。我们的模块通过识别独特的感兴趣区域（ROI）名称、ROI结构位置并允许多个ROI名称表示同一结构，为RT结构文件的快速数据管理提供了直观的方法。它还能够将DICOM图像和RT结构转换为NumPy阵列和SimpleITK图像，这是图像分析和输入深度学习架构和放射组学特征计算的最常用格式。此外，该工具提供了一种简单的方法，用于从预测的NumPy数组创建DICOM RTStructure，这些数组通常是语义分割深度学习模型的输出。DicomRTTool通过公共Github项目邀请开放式协作，而我们在PyPi中的模块部署确保了无痛的分发和安装。我们相信，随着深度学习在医学领域的发展，我们的工具将越来越有用。
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. While the technology to convert DICOM images and RT-Structures into other data types exists, no purpose-built Python module for converting NumPyarrays into RT-Structures exists. The two 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 pre-processing and prediction steps. Our module provides intuitive methods for rapid data curation of RT-Structure files by identifying unique region of interest (ROI) names, 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 RTStructure 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, while 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.
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影响因子：
64.8
作者：
Harris CR;Millman KJ;van der Walt SJ;Gommers R;Virtanen P;Cournapeau D;Wieser E;Taylor J;Berg S;Smith NJ;Kern R;Picus M;Hoyer S;van Kerkwijk MH;Brett M;Haldane A;Del Río JF;Wiebe M;Peterson P;Gérard-Marchant P;Sheppard K;Reddy T;Weckesser W;Abbasi H;Gohlke C;Oliphant TE
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通讯作者：
Aerts HJWL
DOI：
10.1007/s11548-018-1841-4
发表时间：
2018-11
影响因子：
3
作者：
To MNN;Vu DQ;Turkbey B;Choyke PL;Kwak JT
通讯作者：
Kwak JT
影响因子：
10.6
作者：
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通讯作者：
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影响因子：
2.3
作者：
Anderson BM;Lin EY;Cardenas CE;Gress DA;Erwin WD;Odisio BC;Koay EJ;Brock KK
通讯作者：
Brock KK