Aktualisierung: Die Stelle ist vergeben!
An der Ludwig-Maximilians-Universität München (LMU) ist vorbehaltlich der endgültigen Finanzierung am Institut für Statistik, Arbeitsgruppe Bioimaging und räumliche Statistik ab sofort für die Dauer von zunächst 3 Jahren die folgende Stelle zu besetzen:
(E13 TV-L, Vollbeschäftigung, befristet, Qualifizierungsstelle)
im Bereich Bayesianischer Bildverarbeitung
im Rahmen eines beantragten Kompetenzzentrum für Machine Learning
Just accepted for publication at Computational Statistics & Data Analysis: Fitting large-scale structured additive regression models using Krylov subspace methods by Paul Schmidt, Mark Mühlau and Volker Schmid.
Fitting regression models can be challenging when regression coefficients are high-dimensional. Especially when large spatial or temporal effects need to be taken into account the limits of computational capacities of normal working stations are reached quickly. The analysis of images with several million pixels, where each pixel value can be seen as an observation on a new spatial location, represent such a situation.
The binding behavior of molecules in nuclei of living cells can be studied through the analysis of images from fluorescence recovery after photobleaching experiments. However, there is still a lack of methodology for the statistical evaluation of FRAP data, especially for the joint analysis of multiple dynamic images. We propose a hierarchical Bayesian nonlinear model with mixed-effect priors based on local compartment models in order to obtain joint parameter estimates for all nuclei as well as to account for the heterogeneity of the nuclei population.
Medical imaging is one of the heaviest funded biomedical engineering research areas. The second edition of Pattern Recognition and Signal Analysis in Medical Imaging brings sharp focus to the development of integrated systems for use in the clinical sector, enabling both imaging and the automatic assessment of the resultant data.
Since the first edition, there has been tremendous development of new, powerful technologies for detecting, storing, transmitting, analyzing, and displaying medical images. Computer-aided analytical techniques, coupled with a continuing need to derive more information from medical images, has led to a growing application of digital processing techniques in cancer detection as well as elsewhere in medicine.
This book is an essential tool for students and professionals, compiling and explaining proven and cutting-edge methods in pattern recognition for medical imaging.
Abstract Competing compartment models of different complexities have been used for the quantitative analysis of dynamic contrast-enhanced magnetic resonance imaging data. We present a spatial elastic net approach that allows to estimate the number of compartments for each voxel such that the model complexity is not fixed a priori. A multi-compartment approach is considered, which is translated into a restricted least square model selection problem. This is done by using a set of basis functions for a given set of candidate rate constants.
Standard compartment models for the analysis of Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) are based on one or two compartments, parts of the tissue, which exchange the contrast agent. However, these models are often to simplistic, especially when analysing DCE-MR images of cancerous tissue. On the other hand, models with more than two compartments suffer from redundancy issues, that is, the model parameters cannot be identified. Julia Sommer has worked on ways to make more-compartment-models identifiable by using spatial regularization methods. In two recent publications, she also shows how the number of compartments can be found.