Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Kaapana: A Comprehensive Open-Source Platform for Integrating AI in Medical Imaging Research Environments

Ünal Akünal, Markus Bujotzek, Stefan Denner, Benjamin Hamm, Klaus Kades, Philipp Schader, Jonas Scherer, Marco Nolden, Peter Neher, Ralf Floca, Klaus Maier-Hein

TL;DR

The paper addresses the challenge of building generalizable AI for medical imaging under data-sharing and regulatory constraints. It presents Kaapana, an open-source, modular platform that unifies data ingestion, curation, processing, and result interpretation, with federated processing to preserve data sovereignty. The system uses a Kubernetes-based microservice architecture with Helm deployments, an EDK for extensibility, and integrated viewers, supported by end-to-end pipelines and distributed computing. Real-world deployments across multi-center initiatives demonstrate privacy-preserving collaboration and scalable imaging research, while an active community and clear contribution pathways invite broader adoption. Overall, Kaapana aims to reduce technical overhead, improve reproducibility, and accelerate large-scale, cross-institutional imaging studies.

Abstract

Developing generalizable AI for medical imaging requires both access to large, multi-center datasets and standardized, reproducible tooling within research environments. However, leveraging real-world imaging data in clinical research environments is still hampered by strict regulatory constraints, fragmented software infrastructure, and the challenges inherent in conducting large-cohort multicentre studies. This leads to projects that rely on ad-hoc toolchains that are hard to reproduce, difficult to scale beyond single institutions and poorly suited for collaboration between clinicians and data scientists. We present Kaapana, a comprehensive open-source platform for medical imaging research that is designed to bridge this gap. Rather than building single-use, site-specific tooling, Kaapana provides a modular, extensible framework that unifies data ingestion, cohort curation, processing workflows and result inspection under a common user interface. By bringing the algorithm to the data, it enables institutions to keep control over their sensitive data while still participating in distributed experimentation and model development. By integrating flexible workflow orchestration with user-facing applications for researchers, Kaapana reduces technical overhead, improves reproducibility and enables conducting large-scale, collaborative, multi-centre imaging studies. We describe the core concepts of the platform and illustrate how they can support diverse use cases, from local prototyping to nation-wide research networks. The open-source codebase is available at https://github.com/kaapana/kaapana

Kaapana: A Comprehensive Open-Source Platform for Integrating AI in Medical Imaging Research Environments

TL;DR

The paper addresses the challenge of building generalizable AI for medical imaging under data-sharing and regulatory constraints. It presents Kaapana, an open-source, modular platform that unifies data ingestion, curation, processing, and result interpretation, with federated processing to preserve data sovereignty. The system uses a Kubernetes-based microservice architecture with Helm deployments, an EDK for extensibility, and integrated viewers, supported by end-to-end pipelines and distributed computing. Real-world deployments across multi-center initiatives demonstrate privacy-preserving collaboration and scalable imaging research, while an active community and clear contribution pathways invite broader adoption. Overall, Kaapana aims to reduce technical overhead, improve reproducibility, and accelerate large-scale, cross-institutional imaging studies.

Abstract

Developing generalizable AI for medical imaging requires both access to large, multi-center datasets and standardized, reproducible tooling within research environments. However, leveraging real-world imaging data in clinical research environments is still hampered by strict regulatory constraints, fragmented software infrastructure, and the challenges inherent in conducting large-cohort multicentre studies. This leads to projects that rely on ad-hoc toolchains that are hard to reproduce, difficult to scale beyond single institutions and poorly suited for collaboration between clinicians and data scientists. We present Kaapana, a comprehensive open-source platform for medical imaging research that is designed to bridge this gap. Rather than building single-use, site-specific tooling, Kaapana provides a modular, extensible framework that unifies data ingestion, cohort curation, processing workflows and result inspection under a common user interface. By bringing the algorithm to the data, it enables institutions to keep control over their sensitive data while still participating in distributed experimentation and model development. By integrating flexible workflow orchestration with user-facing applications for researchers, Kaapana reduces technical overhead, improves reproducibility and enables conducting large-scale, collaborative, multi-centre imaging studies. We describe the core concepts of the platform and illustrate how they can support diverse use cases, from local prototyping to nation-wide research networks. The open-source codebase is available at https://github.com/kaapana/kaapana

Paper Structure

This paper contains 8 sections, 1 figure.

Table of Contents

  1. Motivation
  2. Kaapana is the Solution.
  3. Technical Overview.
  4. Kaapana's End-to-End Processing Pipeline.
  5. Extensibility and Customizability.
  6. Real-World Applications and Impact.
  7. Call to Action.
  8. Acknowledgments

Figures (1)

  • Figure 1: To overcome challenges in multi-institutional AI research, including fragmented toolchains, regulatory constraints, and lack of standardized infrastructure; Kaapana offers a modular and open-source solution. (A) The platform unifies the medical image analysis workflow: Data Ingestion, Curation, Workflow Execution, Results Analysis, and Development Interface, thereby enabling clinical and data science collaboration. (B) This integration is enabled by a robust, open-source tech stack supporting interoperability, scalability, and reproducibility across environments. (C) Kaapana instances can operate collaboratively to support multi-center studies and federated learning, allowing AI models to be trained across institutions while preserving data sovereignty.