Medical Image Computing Research

Transformer based architectures have been introduced to computer vision with the Vision Transformer (ViT). Since then, research efforts have sought to replicate the breakthroughs in natural language processing in vision based tasks such as classification, detection and segmentation using transformers. Medical image segmentation has seen its share, with some research incorporating ViT backbones while others using hierarchical feature search alongside transformer layers promising high performance. This project seeks to explore the effectiveness of transformer based architectures, in comparison to well-understood convolutional nets, in the face of realistic dataset sizes in medical image analysis.

Saikat Roy

Chronic Obstructive Pulmonary Disease (COPD) is a common lung disease characterized by persistent or recurrent respiratory symptoms. Different patterns ("phenotypes") of lung damage are observed with different consequences for individual therapy, e.g. the destruction of the alveolar sac (emphysema) or bronchial wall thickening and obstruction with mucus (airway disease). Medical air-flow lung function tests typically fail to detect subtle changes within the lungs or even sub-regions of the lung. Beyond visual inspection of computed tomography (CT), quantitative CT analysis using computer-aided detection and deep learning techniques promises more insight into the lung and its reactions to the disease or medication. We aim to further analyze the CT images by exploring unseen patterns and clusters by deep learning in order to find new methods for the classification and monitoring of COPD.

Silvia Dias Almeida

HIFIS offers free-of-charge consulting as a service to research groups under the Helmholtz umbrella. We help you to deal with specific licensing issues, pointing out solutions on how to improve your software or setting up a new projects. We are also very happy to discuss other software engineering topics such as software engineering process in general. We are a small team that tries to help as many researchers and research groups as possible across all Helmholtz institutes.

Ashis Ravindran

Within the German Cancer Consortium (DKTK) the Joint Funding Project “Joint Imaging Platform” will establish a distributed IT infrastructure for image analysis and machine learning in the member institutions. It will facilitate pooling of analysis methods that can be applied in an automated and standardized manner to patient data in the different centers, allowing for unprecedented cohort sizes. The biggest research challenge is the combination, aggregation and distribution of training data, processes and models for non-shareable sensitive data as well as the validation of quantitative imaging biomarkers across a multi-institutional consortium. On the implementation side we investigate distributed learning methods as well as latest private cloud technologies for a robust deployment of data management and processing. More information

Jonas Scherer
Klaus Kades

Model fitting plays a central role in the quantitative analysis of medical images. One prominent example is dynamic contrast-enhanced (DCE) MRI, where perfusion-related parameters are estimated using pharmacokinetic modelling. Other applications include mapping the apparent diffusion coefficient (ADC) in diffusion weighted MRI, and the analysis of Z-spectra in chemical exchange saturation transfer (CEST) imaging.
The ready-to-use model fitting toolbox is embedded into MITK and provides tools for model-fitting (ROI-based or voxel-by-voxel), fit evaluation and visualization. Any fitting task can be performed given a user-defined model. Being part of MITK, MITK-ModelFit applications can be easily and flexibly incorporated into pre-and post-processing workflows and offer a large set of interoperability options and supported data formats.
A special emphasis is put on pharmacokinetic analysis of DCE MRI data. Here, a variety of pharmacokinetic models is available. In addition tools are offered for arterial input function estimation and conversion from signal to concentration.

Ina Kompan
Ralf Floca

Additionally to the interactive processing focused in MITK, today's research questions often require the standardized and automated processing and easy data access while reducing emerging hassles of data transfer, data protection and data storage. We provide scientific cloud and platform solutions and evaluate new exploration and processing capabilities to medical imaging researchers.

Hanno Gao

Extensive research has been conducted in the field of image-based computational analysis of major clinical diseases. By contrast, little is known about the potential variety of interrelations between typical co-occuring pathologies. During clinical routine, diagnosis and treatment are normally targeted at a primary disease, while co-occuring pathologies often remain undetected and underdiagnosed, despite their expected substantial effect on overall prognoses and treatment outcomes. This project focuses on a more holistic analysis of imaging data from clinical routine, as a means to automatically detect and quantify an expected variety of co-occuring diseases. Therefor, a well-defined subset of pathologies and datasets at University Hospital Heidelberg serves as a foundation for the development of learning based image analysis algorithms, with the goal to promote a deeper understanding of comorbidities encountered in clinical practice. For seamless translation of research methods into the clinical workflow and for future scalability of the project, an integrated system for automated algorithm deployment is developed based on the joint imaging platform (JIP).

Taisiya Kopytova, Silvia Almeida, Tobias Norajitra

State-of-the-art segmentation frameworks show impressive performance with dice scores comparable to human inter- and intra-rater variability. However, these models frequently fail with severe and unexpected errors when being brought into the clinical environment. In this project a model will be developed which will incorporate the insights obtained from the analysis of the performance of current methods, as well as temporal and global information of samples in order to improve and stabilize the generated segmentations.

Yannick Kirchhoff

Breast cancer is the most invasive cancer for women throughout the world, in both developed and developing countries. This project aims to create robust models for breast lesion detection and classification using diffusion-weighted MR images and to produce a software platform, up to the high standards of market medical products, that can interactively assist clinical decision making by providing valuable feedback and diagnostic suggestions. The aspiration is that the deep learning model will be able correctly identify and classify malignant lesions with high sensitivity, while minimizing false positive results which are common in standard clinical screening practice.

Dimitrios Bounias
Michael Baumgartner

VISSART is an open-source framework (based on challengeR) for analyzing and visualizing challenge/benchmarking/algorithm results. It offers a set of tools for comprehensive analysis and visualization of method results. It applies a number of simulated and real-life computations such as visualizing assessment data, ranking robustness, and ranking stability, to demonstrate specific strengths and weaknesses of various algorithms. Furthermore, it also supports single-task and multi-task challenges. Thanks to this online version, there is no need of installing interpreters, necessary packages, libraries, etc. We facilitate the use of the challengeR framework to developers not familiar with the R language.

Ali Emre Kavur

The analyzation and identification of particle compositions plays a central role in increasing the effectiveness of ore processing and recycling techniques. These types of analyses are typically performed by crushing ores of unknown composition into particles, which are then placed in a synthetic resin embedding and CT-scanned in micron resolution. The resulting CT-scan is then manually analyzed in a time intensive and error-prone manner to identify the individual composition of multiple hundreds of particles.
The objective of this project is to automatically generate hierarchical instance segmentations of every particle in order to reduce the current time intensive approach and increase the correctness of the analysis.

Karol Gotkowski

Perfusion-related quantities derived from dynamic contrast-enhanced (DCE) and dynamic susceptibility contrast (DSC) magnetic resonance imaging (MRI) are useful biomarkers of vascular function. To generate perfusion-related quantities, the acquired data is typically analyzed using a sequence of processes which define an image analysis pipeline. Currently, there is a lack of clear reporting guidelines and standardized nomenclature for applied perfusion analyses pipelines. This means that analysis steps are often not accurately captured, leading to user-variability in reporting, which fundamentally limits reproducibility of perfusion-based research.The Open Source Initiative for Perfusion Imaging (OSIPI) is an initiative of the International Society for Magnetic Resonance in Medicine (ISMRM) perfusion study group with the mission is to promote the sharing of perfusion imaging software, improve the reproducibility of perfusion imaging research. As part of OSIPI a consensus-based DCE/DSC lexicon and a reporting framework are developed with the aim to improve reproducibility and in perfusion image analysis.

Ina Kompan

To develop a research-supporting risk prediction platform for the National Cancer Prevention Center, we are currently assembling an interdisciplinary digital cancer prevention team. The focus of the working group is on the development of a specific and evidence-based portal for the individual calculation of personal cancer risk. In doing so, existing prediction models will be validated, curated and merged according to a standardized procedure. Interested citizens should be able to use the portal to assess their individual cancer risk and receive information adapted to their personal cancer risk. For example, demographic data, information on lifestyle, family history, and results of previous tests can be included in the calculation. At the same time, these data are used to further optimize the prediction models and maintain a continuously high level of performance. In the long term, the aim is to develop a research-capable platform for sustainable data collection and access to research data in modern prevention research.

Angela Goncalves, Klaus Maier-Hein, Elias Müller

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. Our image analysis technology (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.

More information: HiGHmed

Ralf Floca

To make forensic radiology feasible, we develop workflows and processes that combine local and remote radiological infrastructure as well as latest technologies of medical image processing. To minimize required resources at image acquisition sites, we design automated image processing pipelines and secure data transfers to minimally occupy human resources and minimally interfere with previously existing on-site routine workflows.

This project is a cooperation between DKFZ, Institute for Legal and Traffic Medicine (University Clinic HD) and Institute for Anatomy (University HD).

Ignaz Reicht