Highdicom: a Python Library for Standardized Encoding of Image Annotations and Machine Learning Model Outputs in Pathology and Radiology.
Highdicom: a Python Library for Standardized Encoding of Image Annotations and Machine Learning Model Outputs in Pathology and Radiology.
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DOI：
10.1007/s10278-022-00683-y
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发表时间：
2022-12
影响因子：
4.4
通讯作者：
Herrmann, Markus D.
中科院分区：
文献类型：
--
作者：
Bridge, Christopher P.;Gorman, Chris;Pieper, Steven;Doyle, Sean W.;Lennerz, Jochen K.;Kalpathy-Cramer, Jayashree;Clunie, David A.;Fedorov, Andriy Y.;Herrmann, Markus D.
机器学习（ML）正在彻底改变病理学和放射学中基于图像的诊断。ML模型在研究环境中显示出了很好的结果，但ML系统和企业医学成像系统之间缺乏互操作性一直是临床集成和评估的主要障碍。DICOM®标准规定了信息对象定义（IOD）和服务，用于数字图像和相关信息的表示和通信，包括图像衍生注释和分析结果。然而，该标准的复杂性是其在ML社区中采用的障碍，并需要简化DICOM格式数据集的软件库和工具。在这里，我们介绍了highdicom库，它为Python编程语言提供了一个高级应用程序编程接口（API），该编程语言抽象了标准的低级细节，并在几行Python代码中实现了DICOM格式的图像衍生信息的编码和解码。highdicom库利用NumPy数组进行有效的数据表示，并与广泛的Python生态系统结合起来进行图像处理和机器学习。同时，通过简化DICOM兼容文件的创建和解析，highdicom实现了与医学成像系统的互操作性，这些系统保存用于训练和运行ML模型的数据，并最终通信和存储模型输出供临床使用。我们通过载玻片显微镜和计算机断层扫描成像的实验证明，通过桥接这两个生态系统，highdicom使开发人员和研究人员能够在病理学和放射学中训练和评估最先进的ML模型，同时保持符合DICOM标准，并在所有阶段与临床系统互操作。为了促进ML研究的标准化并简化ML模型开发和部署过程，我们在https://github.com/herrmannlab/highdicom上提供了免费和开源的库。在线版本包含补充材料，可通过10.1007/s10278-022-00683-y获得。
Machine learning (ML) is revolutionizing image-based diagnostics in pathology and radiology. ML models have shown promising results in research settings, but the lack of interoperability between ML systems and enterprise medical imaging systems has been a major barrier for clinical integration and evaluation. The DICOM® standard specifies information object definitions (IODs) and services for the representation and communication of digital images and related information, including image-derived annotations and analysis results. However, the complexity of the standard represents an obstacle for its adoption in the ML community and creates a need for software libraries and tools that simplify working with datasets in DICOM format. Here we present the highdicom library, which provides a high-level application programming interface (API) for the Python programming language that abstracts low-level details of the standard and enables encoding and decoding of image-derived information in DICOM format in a few lines of Python code. The highdicom library leverages NumPy arrays for efficient data representation and ties into the extensive Python ecosystem for image processing and machine learning. Simultaneously, by simplifying creation and parsing of DICOM-compliant files, highdicom achieves interoperability with the medical imaging systems that hold the data used to train and run ML models, and ultimately communicate and store model outputs for clinical use. We demonstrate through experiments with slide microscopy and computed tomography imaging, that, by bridging these two ecosystems, highdicom enables developers and researchers to train and evaluate state-of-the-art ML models in pathology and radiology while remaining compliant with the DICOM standard and interoperable with clinical systems at all stages. To promote standardization of ML research and streamline the ML model development and deployment process, we made the library available free and open-source at https://github.com/herrmannlab/highdicom. The online version contains supplementary material available at 10.1007/s10278-022-00683-y.
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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
通讯作者：
Oliphant TE
影响因子：
4.4
作者：
Clark, Kenneth;Vendt, Bruce;Prior, Fred
通讯作者：
Prior, Fred
影响因子：
4.5
作者：
Allen, Bibb, Jr.;Seltzer, Steven E.;Kandarpa, Krishna
通讯作者：
Kandarpa, Krishna
DOI：
10.1007/s13735-020-00195-x
发表时间：
2020-07-03
影响因子：
5.6
作者：
Hafiz, Abdul Mueed;Bhat, Ghulam Mohiuddin
通讯作者：
Bhat, Ghulam Mohiuddin
影响因子：
11.2
作者：
Herz, Christian;Fillion-Robin, Jean-Christophe;Fedorov, Andriy
通讯作者：
Fedorov, Andriy