Various approaches for movement analysis have been proposed in literature. Up to date, it is not clear which solution is outperforming the others. The provision of benchmark datasets are mandatory for research groups. This could enhance and speed up the process of getting the best performing and mo...

The segmentation of the carotid bifurcation region in non contrast-enhanced MRA TOF datasets is challenging due to its susceptibility to irregular blood flow patterns. These lead to intensity deviations especially close to the bifurcation and even to signal voids. We provide here standardiz...

Obtaining quantitative ground truth for color constancy under multiple light sources is difficult. We provide two datasets of 58 and 20 images that are exposed to two illuminants. The ground truth is obtained from an algorithm that operates on multiple aligned input images. Read more & down...

Epipolar Consistency in Transmission Imaging Two X-ray projection images of a rigid object may have different points of view, yet redundant information can be identified in such images. Not unlike a checksum, these occur naturally in the data and are known as consistency conditions. Real acquisi...

Single photon emission computed tomography (SPECT) is a medical imaging modality used to visualize the distribution of radioactive tracers in a patient's body. While SPECT's utility as a diagnostic modality has long been established, its use for planning and managing nuclear medicine therapies has ...

Sulaiman Vesal, PhD student at the Pattern Recognition Lab, was awarded the second best work in the STACOM Multi-sequence Cardiac MR Segmentation Challenge at MICCAI 2019. The award was given for the paper: "Automated Multi-sequence Cardiac MRI Segmentation Using Supervised Domain Adaptation", whic...

Non-invasive imaging methods are capable of revealing content that is not visible with the naked eye. In this thesis, we present solutions for three common areas of application: digitization of cultural heritage, medicine, and quality assessment. The success story of those approaches is further...

Congratulations to Maniraman Periyasamy, Meike Biendl and Alexander Richter, Jonas Utz, and Henrik Willer for their outstanding achievements in the Deep Learning Challenge 2019! A great success. Please enjoy the backpacks that were donated by Nvidia!

In a cooperation with Marc Kachelriess (DKFZ), Michael Lell (Klinikum Nürnberg Nord) and our lab, we successfully applied for another DFG Project. The aim of the project is to estimate patient-individualised dose for a CT scan using deep learning techniques. In the project, we will unite deep learn...

Real-time Respiratory Motion Analysis Using GPU-accelerated Range Imaging Abstract Respiratory motion analysis and management are crucial issues for a plurality of medical applications. Of particular scientific concern are methods that allow to analyze the patient’s breathing in a non-invasi...