Plenoptic Modeling of 3D Scenes with a Sensor-augmented Multi-Camera Rig

We propose a system for robust modeling and visualisation of complex outdoor scenes from multi-camera image sequences and additional sensor information. A camera rig with one or more fire-wire cameras is used in conjunction with a 3-axis rotation sensor to robustly obtain a calibration of the scene with an uncalibrated structure from motion approach. Dense depth maps are estimated with multi-viewpoint stereo and the scene is visualized from a plenoptic representation of the scene

Distributed Realtime Interaction and Visualization System

A distributed realtime system for immersive visualization is presented which uses distributed interaction for control. We will focus on user tracking with fixed and pan-tilt-zoom cameras, synchronization of multiple interaction devices and distributed synchronized visualization. The system uses only standard hardware and standard network protocols. Furthermore for the realtime visualization we only use consumer graphics hardware

Using AI and Gd-EOB-DTPA-enhanced MR imaging to assess liver function, comparing the MELIF score with the ALBI score

Abstract Monitoring disease progression is particularly important for determining the optimal treatment strategy in patients with liver disease. Especially for patients with diseases that have a reversible course, there is a lack of suitable tools for monitoring liver function. The development and establishment of such tools is very important, especially in view of the expected increase in such diseases in the future. Image-based liver function parameters, such as the T1 relaxometry-based MELIF score, are ideally suited for this purpose. The determination of this new liver function score is fully automated by software developed with AI technology. In this study, the MELIF score is compared with the widely used ALBI score. The ALBI score was used as a benchmark, as it has been shown to better capture the progression of less severe liver disease than the MELD and Child‒Pugh scores. In this study, we retrospectively determined the ALBI and MELIF scores for 150 patients, compared these scores with the corresponding MELD and Child‒Pugh scores (Pearson correlation), and examined the ability of these scores to discriminate between good and impaired liver function (AUC: MELIF 0.8; ALBI 0.77) and to distinguish between patients with and without cirrhosis (AUC: MELIF 0.83, ALBI 0.79). The MELIF score performed more favourably than the ALBI score and may also be suitable for monitoring mild disease progression. Thus, the MELIF score is promising for closing the gap in the available early-stage liver disease monitoring tools (i.e., identification of liver disease at a potentially reversible stage before chronic liver disease develops)

MELIF, a Fully Automated Liver Function Score Calculated from Gd-EOB-DTPA-Enhanced MR Images: Diagnostic Performance vs. the MELD Score

In the management of patients with chronic liver disease, the assessment of liver function is essential for treatment planning. Gd-EOB-DTPA-enhanced MRI allows for both the acquisition of anatomical information and regional liver function quantification. The objective of this study was to demonstrate and evaluate the diagnostic performance of two fully automatically generated imaging-based liver function scores that take the whole liver into account. T1 images from the native and hepatobiliary phases and the corresponding T1 maps from 195 patients were analyzed. A novel artificial-intelligence-based software prototype performed image segmentation and registration, calculated the reduction rate of the T1 relaxation time for the whole liver (rrT1(liver)) and used it to calculate a personalized liver function score, then generated a unified score-the MELIF score-by combining the liver function score with a patient-specific factor that included weight, height and liver volume. Both scores correlated strongly with the MELD score, which is used as a reference for global liver function. However, MELIF showed a stronger correlation than the rrT1(liver) score. This study demonstrated that the fully automated determination of total liver function, regionally resolved, using MR liver imaging is feasible, providing the opportunity to use the MELIF score as a diagnostic marker in future prospective studies