SoftwareApplication
identifier: 10.1200/CCI.20.00045
name: Kaapana
description: Kaapana is an open source toolkit for state of the art platform provisioning in the field of medical data analysis. The applications comprise AI-based workflows and federated learning scenarios with a focus on radiological and radiotherapeutic imaging.
description: [![](https://www.kaapana.ai/kaapana-downloads/kaapana-docs/stable/img/thumbnail_kaapana_vid.png)](https://www.kaapana.ai/kaapana-downloads/kaapana-docs/stable/gif/kaapana-v0.2.1-showcase.mp4) Kaapana is an open-source toolkit for state-of-the-art platform provisioning in the field of medical data analysis. The applications comprise AI-based workflows and federated learning scenarios with a focus on radiological and radiotherapeutic imaging. Obtaining large amounts of medical data necessary for developing and training modern machine learning methods is an extremely challenging effort that often fails in a multi-center setting, e.g. due to technical, organizational and legal hurdles. A federated approach where the data remains under the authority of the individual institutions and is only processed on-site is, in contrast, a promising approach ideally suited to overcome these difficulties. Following this federated concept, the goal of Kaapana is to provide a framework and a set of tools for sharing data processing algorithms, for standardized workflow design and execution as well as for performing distributed method development. This will facilitate data analysis in a compliant way enabling researchers and clinicians to perform large-scale multi-center studies. By adhering to established standards and by adopting widely used open technologies for private cloud development and containerized data processing, Kaapana integrates seamlessly with the existing clinical IT infrastructure, such as the Picture Archiving and Communication System (PACS), and ensures modularity and easy extensibility. Core components of Kaapana: * **Workflow management:** Large-scale image processing with SOTA deep learning algorithms, such as [nnU-Net](https://github.com/MIC-DKFZ/nnunet) image segmentation and [TotalSegmentator](https://github.com/wasserth/TotalSegmentator) * **Datasets:** Exploration, visualization and curation of medical images * **Extensions:** Simple integration of new, custo…
code repository: https://github.com/kaapana/kaapana
author: Person
  given name: Mikuláš
  family name: Bankovič
  name: Mikuláš Bankovič
  role name: Developer
  affiliation: Organization
    name: German Cancer Research Center
author: https://orcid.org/0009-0006-1399-8676
author: https://orcid.org/0000-0003-3218-3377
author: Person
  given name: Lorenz
  family name: Feineis
  name: Lorenz Feineis
  role name: Developer
  affiliation: Organization
    name: German Cancer Research Center
author: 0009-0008-2302-743X
author: https://orcid.org/0000-0002-6075-0757
keywords: Federated Learning
keywords: Image processing
keywords: Platform toolkit
keywords: Workflow Execution
keywords: data managment
programming language: Python
programming language: Vue
programming language: Shell
programming language: TypeScript
programming language: Dockerfile
programming language: Java
programming language: HTML
programming language: JavaScript
programming language: FreeMarker
programming language: CSS
programming language: Jupyter Notebook
programming language: Open Policy Agent
programming language: Go Template
programming language: SCSS
programming language: Jinja
programming language: Mako
programming language: Makefile
license:
  creative work: creativeWork
    name: AGPL-3.0-only

https://orcid.org/0009-0006-1399-8676
type: Person
given name: Ünal
family name: Akünal
name: Ünal Akünal
role name: Developer

https://orcid.org/0000-0003-3218-3377
type: Person
given name: Ralf
family name: Floca
name: Ralf Floca
affiliation: Organization
  name: German Cancer Research Center

0009-0008-2302-743X
type: Person
given name: Stefan
family name: Denner
name: Stefan Denner
role name: PhD Student
affiliation: Organization
  name: German Cancer Research Center

https://orcid.org/0000-0002-6075-0757
type: Person
given name: Philipp
family name: Schader
name: Philipp Schader
role name: PhD Student
affiliation: Organization
  name: German Cancer Research Center