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d:cipher - Medical Image Computing at Scale

Mission


d:cipher is a technology platform for Distributed Computational Image-based PHEnotyping and Radiomics. It is designed to establish a better link between clinical (imaging) data, computational power and methodical tools.

D:cipher supports single-institutional use, where it improves direct workflow integration of computing tools or analysis of meta data. However, d:cipher also scales to multi-institutional settings, where it scales with the computational resources, the sizes of cohorts and with the number of methods available. The federated computing capabilities are readily built in - so no centralization (neither for data nor for methods) is needed. Leveraging state of the art open source technologies we aim at a high interoperability with existing standards and solutions.

We are currently working on the first open source release to the community, so if you are interested please get in touch with us! You might as well be interested in other projects from us, e.g. the Medical Imaging Interaction Toolkit.

Projects


DKTK Joint Imaging Platform

Link to homepage: JIP

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.

OP 4.1

Link to homepage: OP 4.1

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 d:cipher is currently ongoing to provide several services like visual meta data exploration, interactive cohort definition or automatic image processing pipelines.

NCT cooperation

In the National Center For Tumor Diseases Heidelberg (NCT) d:cipher is used to establish image analysis workflows to facilitate translational research.

HiGHmed

Link to homepage: HiGHmed

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. D:cipher 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 d:cipher 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.

Helmholtz Analytics Framework (HAF)

Link to homepage: Helmholtz Analytics

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 d:cipher. 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 d:cipher “ecosystem”.

Team


Contact


  Heidelberg, Germany