| Literature DB >> 32754723 |
Sarthak Pati1, Ashish Singh1, Saima Rathore1,2, Aimilia Gastounioti1,2, Mark Bergman1, Phuc Ngo1,2, Sung Min Ha1,2, Dimitrios Bounias1, James Minock1, Grayson Murphy1, Hongming Li1,2, Amit Bhattarai1, Adam Wolf1, Patmaa Sridaran1, Ratheesh Kalarot1, Hamed Akbari1,2, Aristeidis Sotiras1,3, Siddhesh P Thakur1, Ragini Verma1,2, Russell T Shinohara1,4, Paul Yushkevich1,2,5, Yong Fan1,2, Despina Kontos1,2, Christos Davatzikos1,2, Spyridon Bakas1,2,6.
Abstract
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.Entities:
Keywords: Brain tumor; Breast cancer; CaPTk; Cancer; Deep learning; Glioblastoma; Glioma; ITCR; Imaging; Lung cancer; Phenomics; Radiogenomics; Radiomics; Radiophenotype; Segmentation; Toolkit
Year: 2020 PMID: 32754723 PMCID: PMC7402244 DOI: 10.1007/978-3-030-46643-5_38
Source DB: PubMed Journal: Brainlesion