Medical Image Computing Research
End-to-end Deep Learning Architecture
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Machine Learning-based dMRI Processing
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Validation of Fiber Tractography
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Learning from Noisy Annotations
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Learning from Weak Annotations
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The Black Swan Project
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Knowledge-based Large-scale Image Segmentation
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This project aims at the development of flexible and accurate methods for an automatic semantic image segmentation on large clinical datasets. We focus on a combination of model-based methods with techniques from Active Learning, Online Learning, Transfer Learning and Deep Learning. They allow an optimized training on sparse data, a continuous learning at runtime and an assessment of segmentation quality, as a prerequisite for the successful annotation of large and heterogeneous datasets.
Image-based Stratification of Glioblastoma Patients
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Radiomics
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In Radiomics, high-throughput computing techniques are systematically employed for the conversion of images to higher-dimensional data, i.e. predictive features. The aim is to improve decision support by the subsequent analysis of these features. We study comprehensive MRI imaging phenotypes for linkage of imaging with clinical, biological and genomic parameters in several entities including prostate cancer, breast cancer and brain tumors.
Longitudinal Modeling for Glioblastoma Assessment
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"Deep Probabilistic Modeling of Glioma Growth", Petersen et al., MICCAI 2019 (https://arxiv.org/abs/1907.04064)
Joint Imaging Platform
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
Unsupervised Deep Learning
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Medical Imaging Interaction Toolkit (MITK)
Cancer Research Center since 2002 with contributions and users from an
international community in research and industry.
MITK Workbench
The MITK Workbench is a free medical image viewer, which supports all kinds of DICOM modalities like CT, MRT, US and RT as well as a number of research file formats. In addition to 2D, 3D and 3D+t visualization capabilities, there are numerous plugins for segmentation, registration and other processing steps available.
Research data management and automated processing
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Internal support and infrastructure
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Intraoperative assistance system for mobile C-Arm devices
Intraoperative imaging can help to improve the quality of reduction results in trauma surgery. However, the mobility of the device comes with lack of information about its orientation to the patient. Screws, plates and pathologies further increase the challenge of image understanding. We aim to assist the surgeon in upper ankle surgery by incorporating prior knowledge and information of the contralateral site using 2D/3D registration.
Intraoperative assistance for mobile C-arm positioning
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Intraoperative imaging guides the surgeon through interventions and leads to higher precision and reduced surgical revisions. For evaluation purposes the surgeon needs to acquire anatomy-specific standardized projections. We aim to replace the current manual positioning procedure of the C-arm involving continuous fluoroscopy by an automatic procedure, thereby reducing the dose and time requirement. We tackle this problem employing data simulation techniques and deep learning based methods.
RTToolbox
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AVID
AVID is especially used in the context of radiotherapy. Therefore, many of the integrated components are provided by RT-Toolbox. However, it was also used successfully in other areas like radiological image analysis and image registration error effect studies
CSI-HD
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)
MITK-ModelFit and Perfusion
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 selection and conversion from signal to concentration.
MITK-MRUtilities
Magnetic Resonance imaging is moving towards ultra-high field strengths (7T and higher) to achieve higher resolutions, more enhanced image contrasts and an increased signal-to-noise-ratio. This provides the means for improved diagnostic methods and facilitates non-proton MRI such as sodium imaging. However, at ultra-high-field, physical and hardware limitations, such as RF field inhomogeneity, show as image distortions and artifacts. To overcome these limitations, novel pulse sequences, post-processing procedures and hardware extensions such parallel transmit coils are required.
The “MRUtilities”-toolbox is an extension of MITK which aims to support physicists and physicians conducting research at ultra-high field MRI. Provided applications include B1-mapping and optimized RF-shimming for parallel transmit coils.
Hyppopy
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Hyppopy is a python toolbox for blackbox-function optimization providing an easy to use interface for a variety of solver frameworks. Hyppopy gives access to gridsearch, random and quasirandom, particle and bayes solvers. The aim of Hyppopy is making hyperparameter optimization as simple as possible (in our cases e.g. for the optimization of image processing pipelines or machine learning tasks). It can be easily integrated in existing code bases and provides real-time visualization of the parameter space and the optimization process via a visdom server. The internal design is focused on extensibility, ensuring that custom solver and future approaches can be integrated.