The Cancer Imaging Phenomics Toolkit (CaPTk): Technical Overview
Open accessAbstract
The purpose of this manuscript is to provide an overview of the technical specifications and architecture of the Ca ncer imaging P henomics T ool k it (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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The BraTS challenge provides the largest publicly available and manually-curated mpMRI dataset of diffuse glioma patients and a harmonized preprocessing pipeline to handle inter-site acquisition protocol differences.
“The BraTS challenge35–38, seeking methodological advancements in the domain of neuro-oncology, has been providing the community with (i) the largest publicly available and manually-curated mpMRI dataset of diffuse glioma patients (an example of which is illustrated in Fig. 1b), and (ii) a harmonized preprocessing pipeline51,70,71 to handle differences in inter-site acquisition protocols.”
From Federated learning enables big data for rare cancer boundary detection (2022) · cites this paper as [71]
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Federated learning enables big data for rare cancer boundary detection
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- Authors
- Pati, S., Singh, A., Rathore, S., Gastounioti, A., Bergman, M., Ngo, P., Ha, S. M., Bounias, D., Minock, J., Murphy, G., Li, H., Bhattarai, A., Wolf, A., Sridaran, P., Kalarot, R., Akbari, H., Sotiras, A., Thakur, S. P., Verma, R., Shinohara, R. T., Yushkevich, P., Fan, Y., Kontos, D., Davatzikos, C., & Bakas, S.
- Affiliations
- University of Pennsylvania (US)
- Washington University in St. Louis (US)
- Corresponding author
- Bakas, Spyridon
- ORCID
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2020
- Volume
- 11993
- Pages
- 380-394
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- PMC
- PMC7402244
- OpenAlex
- W3026062881
- Cited by
- 94
- References
- 41
- Citations by year
- 2026: 6 · 2025: 19 · 2024: 15 · 2023: 21 · 2022: 17 · 2021: 15 · 2020: 1
- FWCI
- 14.78
- Type
- conference paper
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (1s10od023495-01, UL1TR001878)
- National Cancer Institute (U24CA189523)
- National Institute of Neurological Disorders and Stroke (R01NS042645)
- National Center for Advancing Translational Sciences
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
- Keywords
- medical image analysis software, brain cancer, breast cancer, lung cancer, radiography, Insight Toolkit, OpenCV, image viewer, clinical translation
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