癌症成像表型组学工具包(CaPTk):技术概述。

Sarthak Pati, Ashish Singh, Saima Rathore, Aimilia Gastounioti, Mark Bergman, Phuc Ngo, Sung Min Ha, Dimitrios Bounias, James Minock, Grayson Murphy, Hongming Li, Amit Bhattarai, Adam Wolf, Patmaa Sridaran, Ratheesh Kalarot, Hamed Akbari, Aristeidis Sotiras, Siddhesh P Thakur, Ragini Verma, Russell T Shinohara, Paul Yushkevich, Yong Fan, Despina Kontos, Christos Davatzikos, Spyridon Bakas
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引用次数: 0

摘要

本手稿旨在概述癌症成像表型组学工具包(CaPTk www.cbica.upenn.edu/captk)的技术规范和架构,该工具包是一个跨平台、开源、易用且可扩展的软件平台,用于分析二维和三维图像,目前主要用于脑癌、乳腺癌和肺癌的放射扫描。该平台的主要目的是将前沿学术研究迅速有效地转化为临床有用的工具,包括临床量化、分析、预测建模、决策和报告工作流程。CaPTk 以 Insight 工具包 (ITK) 和 OpenCV 等成熟的开源软件工具包为基础,汇集了先进的计算功能。这些功能描述了在积极的多学科合作研究过程中为满足实际临床需求而开发的专用和通用图像分析算法。CaPTk 的目标受众包括计算科学家和临床专家。对于前者,它提供了 i) 一个高效的图像查看器,能够整合新算法;ii) 一个随时可用的临床相关算法库,允许对多个受试者进行批量处理。对于后者,它通过友好的用户界面,为临床相关研究使用复杂算法提供了便利,消除了大量计算背景的先决条件。CaPTk 的长期目标是提供广泛使用的技术,以便在癌症预测、诊断和预后中使用先进的定量成像分析技术,从而更好地了解癌症发展的生物机制。
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The Cancer Imaging Phenomics Toolkit (CaPTk): Technical Overview.

The purpose of this manuscript is to provide an overview of the technical specifications and architecture of the Cancer imaging Phenomics Toolkit (CaPTk www.cbica.upenn.edu/captk), a cross-platform, open-source, easy-to-use, and extensible software platform for analyzing 2D and 3D images, currently focusing on radiographic scans of brain, breast, and lung cancer. The primary aim of this platform is to enable swift and efficient translation of cutting-edge academic research into clinically useful tools relating to clinical quantification, analysis, predictive modeling, decision-making, and reporting workflow. CaPTk builds upon established open-source software toolkits, such as the Insight Toolkit (ITK) and OpenCV, to bring together advanced computational functionality. This functionality describes specialized, as well as general-purpose, image analysis algorithms developed during active multi-disciplinary collaborative research studies to address real clinical requirements. The target audience of CaPTk consists of both computational scientists and clinical experts. For the former it provides i) an efficient image viewer offering the ability of integrating new algorithms, and ii) a library of readily-available clinically-relevant algorithms, allowing batch-processing of multiple subjects. For the latter it facilitates the use of complex algorithms for clinically-relevant studies through a user-friendly interface, eliminating the prerequisite of a substantial computational background. CaPTk's long-term goal is to provide widely-used technology to make use of advanced quantitative imaging analytics in cancer prediction, diagnosis and prognosis, leading toward a better understanding of the biological mechanisms of cancer development.

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Leveraging 2D Deep Learning ImageNet-trained models for Native 3D Medical Image Analysis. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 8th International Workshop, BrainLes 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Revised Selected Papers Optimization of Deep Learning Based Brain Extraction in MRI for Low Resource Environments. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 7th International Workshop, BrainLes 2021, Held in Conjunction with MICCAI 2021, Virtual Event, September 27, 2021, Revised Selected Papers, Part I Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 7th International Workshop, BrainLes 2021, Held in Conjunction with MICCAI 2021, Virtual Event, September 27, 2021, Revised Selected Papers, Part II
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