TY - GEN
T1 - The cancer imaging phenomics toolkit (CaPTk)
T2 - 5th International MICCAI Brainlesion Workshop, BrainLes 2019, held in conjunction with the Medical Image Computing for Computer Assisted Intervention, MICCAI 2019
AU - Pati, Sarthak
AU - Singh, Ashish
AU - Rathore, Saima
AU - Gastounioti, Aimilia
AU - Bergman, Mark
AU - Ngo, Phuc
AU - Ha, Sung Min
AU - Bounias, Dimitrios
AU - Minock, James
AU - Murphy, Grayson
AU - Li, Hongming
AU - Bhattarai, Amit
AU - Wolf, Adam
AU - Sridaran, Patmaa
AU - Kalarot, Ratheesh
AU - Akbari, Hamed
AU - Sotiras, Aristeidis
AU - Thakur, Siddhesh P.
AU - Verma, Ragini
AU - Shinohara, Russell T.
AU - Yushkevich, Paul
AU - Fan, Yong
AU - Kontos, Despina
AU - Davatzikos, Christos
AU - Bakas, Spyridon
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2020.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Brain tumor
KW - Breast cancer
KW - CaPTk
KW - Cancer
KW - Deep learning
KW - Glioblastoma
KW - Glioma
KW - ITCR
KW - Imaging
KW - Lung cancer
KW - Phenomics
KW - Radiogenomics
KW - Radiomics
KW - Radiophenotype
KW - Segmentation
KW - Toolkit
UR - http://www.scopus.com/inward/record.url?scp=85085506246&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-46643-5_38
DO - 10.1007/978-3-030-46643-5_38
M3 - Conference contribution
C2 - 32754723
AN - SCOPUS:85085506246
SN - 9783030466428
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 380
EP - 394
BT - Brainlesion
A2 - Crimi, Alessandro
A2 - Bakas, Spyridon
PB - Springer
Y2 - 17 October 2019 through 17 October 2019
ER -