There is a newer version of the record available.

Published May 13, 2025 | Version 0.4.2
Software Open

kaapana/kaapana: v0.4.2

Authors/Creators

  • 1. German Cancer Research Center

Description

Kaapana release v0.4.2

Files

kaapana/kaapana-0.4.2.zip

Files (6.9 MB)

Name Size Download all
md5:b76bd3348abdbe4213def25fcc198709
6.9 MB Preview Download

Additional details

Related works

Is documented by
Software documentation: https://kaapana.readthedocs.io/en/stable/ (URL)
Is new version of
10.5281/zenodo.5786648 (DOI)

Software

Repository URL
https://github.com/kaapana/kaapana

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.