Kaapana (from the hawaiian word kaʻāpana, meaning “distributor” or “part”) 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.
The Joint Imaging Platform ( JIP ) is a strategic initiative within the German Cancer Consortium (DKTK). The aim is to establish a technical infrastructure that enables modern and distributed imaging research within the consortium. The main focus is on the use of modern machine learning methods in medical image processing. It strengthens collaborations between the participating clinical sites and support multicenter trails.
The primary goal of the OP 4.1 project is to build a platform - in analogy to a smartphone operating system - that enables enterprises of all sizes to bring innovative software solutions via apps to the operating theater of the future in an efficient manner. The service-based platform is intended to enable the implementation of smart assistance functions that intuitively convey the relevant information to the various actuators in the operating environment at the right time.
sciDB - Scientific Database
The scientific database (sciDB) is a medical imaging data management platform of research program “Imaging and radiooncology” in the DKFZ. The aim is to provide a solution for easy data access and data processing while reducing emerging hassles of data transfer, data protection and data storage. The integration of kaapana is currently ongoing to provide several services like visual meta data exploration, interactive cohort definition or automatic image processing pipelines.
In the National Center For Tumor Diseases Heidelberg (NCT) kaapana is used to establish image analysis workflows to facilitate translational research.
HiGHmed is a highly innovative consortial project in the context of the “Medical Informatics Initiative Germany” that develops novel, interoperable solutions in medical informatics with the aim to make medical patient data accessible for clinical research in order to improve both, clinical research and patient care. kaapana is part of the Omics Data Integration Center (OmicsDIC) that offers sophisticated technologies to process data and to access information contained in data - from genomics to radiomics. In HiGHmed we also improve the interoperability of image based information by working on the mapping between different important standards like DICOM, HL7 FHIR or OpenEHR.
Unispital Basel cooperation
In cooperation with the unispital Basel, the DKFZ is supporting imaging studies (e.g. the detection of lung nodes and fully automatic analysis of heart MRIs). Our developed methods and “ready-to-use”-workflows are deployed via kaapana to seamlessly integrate it into the radiological research in Basel. This thrives both sides, radiological research and computer science. The unispital profits from sophisticated machine learning workflows and the robust software environment delivered by the DKFZ and in return enables us to proof and improve our methods under real world conditions.
The Helmholtz Analytics Framework is a data science pilot project funded by the Helmholtz Initiative and Networking Fund. The DKFZ is contributing in the Use Case “High-Throughput Image-Based Cohort Phenotyping” and providing pipelines powered by kaapana. The domain overlapping topics, like time-efficient parallel processing on High-Performance Computing (HPC) clusters, efficient data mining techniques, uncertainty management, sophisticated machine learning and inference approaches that are addressed by HAF, are used to improve the kaapana “ecosystem”.