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.zenodo.json
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{
"description": "Kaapana release v0.3.5",
"title": "kaapana/kaapana: v0.3.5",
"version": "0.3.5",
"license": {
"id": "AGPL-3.0"
},
"upload_type": "software",
"publication_date": "2024-09-11",
"creators": [
{
"affiliation": "German Cancer Research Center",
"name": "Ünal Akünal"
},
{
"affiliation": "German Cancer Research Center",
"name": "Rajesh Baidya"
},
{
"affiliation": "German Cancer Research Center",
"name": "Mikulas Bankovic"
},
{
"affiliation": "German Cancer Research Center",
"name": "Jens Beyermann"
},
{
"affiliation": "German Cancer Research Center",
"name": "Markus Bujotzek"
},
{
"affiliation": "German Cancer Research Center",
"name": "Stefan Denner"
},
{
"affiliation": "German Cancer Research Center",
"name": "Lorenz Feineis"
},
{
"affiliation": "German Cancer Research Center",
"name": "Maximilian Fischer"
},
{
"affiliation": "German Cancer Research Center",
"name": "Ralf Floca"
},
{
"affiliation": "German Cancer Research Center",
"name": "Hanno Gao"
},
{
"affiliation": "German Cancer Research Center",
"name": "Benjamin Hamm"
},
{
"affiliation": "German Cancer Research Center",
"name": "Klaus Maier-Hein"
},
{
"affiliation": "German Cancer Research Center",
"name": "Jasmin Metzger"
},
{
"affiliation": "German Cancer Research Center",
"name": "Peter Neher"
},
{
"affiliation": "German Cancer Research Center",
"name": "Marco Nolden"
},
{
"affiliation": "German Cancer Research Center",
"name": "Santhosh Parampottupadam"
},
{
"affiliation": "German Cancer Research Center",
"name": "Philipp Schader"
}
],
"keywords": [
"medical",
"imaging",
"platform",
"ai",
"processing",
"cloud-computing",
"kubernetes",
"federated"
],
"access_right": "open",
"references": [
"Denner, S., Scherer, J., Kades, K., Bounias, D., Schader, P., Kausch, L., ... & Maier-Hein, K. (2023, October). Efficient Large Scale Medical Image Dataset Preparation for Machine Learning Applications. In MICCAI Workshop on Data Engineering in Medical Imaging (pp. 46-55). Cham: Springer Nature Switzerland.",
"Fischer, M., Schader, P., Braren, R., Götz, M., Muckenhuber, A., Weichert, W., ... & Nolden, M. (2022, April). DICOM Whole Slide Imaging for Computational Pathology Research in Kaapana and the Joint Imaging Platform. In Bildverarbeitung für die Medizin 2022: Proceedings, German Workshop on Medical Image Computing, Heidelberg, June 26-28, 2022 (pp. 273-278). Wiesbaden: Springer Fachmedien Wiesbaden.",
"Herz, C., Fillion-Robin, J. C., Onken, M., Riesmeier, J., Lasso, A., Pinter, C., ... & Fedorov, A. (2017). DCMQI: an open source library for standardized communication of quantitative image analysis results using DICOM. Cancer research, 77(21), e87-e90.",
"Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2), 203-211.",
"Kades, K., Scherer, J., Zenk, M., Kempf, M., & Maier-Hein, K. (2022, September). Towards Real-World Federated Learning in Medical Image Analysis Using Kaapana. In International Workshop on Distributed, Collaborative, and Federated Learning (pp. 130-140). Cham: Springer Nature Switzerland.",
"Michael Götz, Marco Nolden, Klaus Maier-Hein. MITK Phenotyping: An open-source toolchain for image-based personalized medicine with radiomics. Radiotherapy and Oncology, Volume 131, 2019, Pages 108-111, ISSN 0167-8140, https://doi.org/10.1016/j.radonc.2018.11.021.",
"MITK Team. (2023). MITK (Version v2023.04) [Computer software]. https://github.com/MITK/MITK",
"Norajitra T, Maier-Hein KH. 3D Statistical Shape Models Incorporating Landmark-Wise Random Regression Forests for Omni-Directional Landmark Detection. IEEE Trans Med Imaging. 2017 Jan;36(1):155-168. doi: 10.1109/TMI.2016.2600502. Epub 2016 Aug 16. PMID: 27541630.",
"Sarah Schuhegger. (2021). MIC-DKFZ/BodyPartRegression: (v1.0). Zenodo. https://doi.org/10.5281/zenodo.5195341",
"Thomas Phil, Thomas Albrecht, Skylar Gay, & Mathis Ersted Rasmussen. (2023). Sikerdebaard/dcmrtstruct2nii: dcmrtstruct2nii v5 (Version v5). Zenodo. https://doi.org/10.5281/zenodo.4037864",
"Van Griethuysen, J. J., Fedorov, A., Parmar, C., Hosny, A., Aucoin, N., Narayan, V., ... & Aerts, H. J. (2017). Computational radiomics system to decode the radiographic phenotype. Cancer research, 77(21), e104-e107.",
"Wasserthal, J., Breit, H.-C., Meyer, M.T., Pradella, M., Hinck, D., Sauter, A.W., Heye, T., Boll, D., Cyriac, J., Yang, S., Bach, M., Segeroth, M., 2023. TotalSegmentator: Robust Segmentation of 104 Anatomic Structures in CT Images. Radiology: Artificial Intelligence. https://doi.org/10.1148/ryai.230024",
"Ziegler, E., Urban, T., Brown, D., Petts, J., Pieper, S. D., Lewis, R., ... & Harris, G. J. (2020). Open health imaging foundation viewer: an extensible open-source framework for building web-based imaging applications to support cancer research. JCO Clinical Cancer Informatics, 4, 336-345."
],
"related_identifiers": [
{
"scheme": "url",
"identifier": "https://kaapana.readthedocs.io/en/stable/",
"relation": "isDocumentedBy",
"resource_type": "publication-softwaredocumentation"
},
{
"scheme": "doi",
"identifier": "10.5281/zenodo.5786648",
"relation": "isNewVersionOf"
}
]
}