Volker J Schmid

Spatial Distribution and Birth Prevalence of Congenital Heart Disease in Iran: A Systematic Review and Hierarchical Bayesian Meta-analysis

Fri, 12 Jan 2024 00:00:00 +0000

Bayesian Spatial analysis for breast and prostate cancer incidence in Sudan based on 2009 - 2013 national registry data

Fri, 01 Dec 2023 00:00:00 +0000

Maximum Entropy Technique and Regularization Functional for Determining the Pharmacokinetic Parameters in DCE-MRI

Thu, 26 May 2022 00:00:00 +0000

Modified Maximum Entropy Method and Estimating the AIF via DCE-MRI Data Analysis

Thu, 20 Jan 2022 00:00:00 +0000

Distinctive nuclear zone for RAD51-mediated homologous recombinational DNA repair.

Tue, 30 Nov 2021 00:00:00 +0000

Zusammenhang zwischen der regionalen Siedlungsstruktur und der Krankenhausinzidenz, Therapieform und Mortalität von nicht-rupturierten abdominalen Aortenaneurysmen. Sekundärdatenanalyse der deutschen DRG-Statistik von 2005–2014

Tue, 01 Jun 2021 00:00:00 +0000

Co-localization analysis in fluorescence microscopy via maximum entropy copula

Sat, 01 May 2021 00:00:00 +0000

Wed, 10 Feb 2021 00:00:00 +0000

Impressum

Prof. Dr. Volker Schmid
Ludwigstrasse 33
80539 München

Freistellungserklärung

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Auch eine automatische kurzzeitige Zwischenspeicherung dieser “fremden Informationen” erfolgt wegen der gewählten Aufruf- und Verlinkungsmethodik durch uns nicht, so dass sich auch dadurch keine Verantwortlichkeit für diese fremden Inhalte ergibt.

Bei der erstmaligen Verknüpfung mit diesen Internetangeboten habe ich den fremden Inhalt jedoch daraufhin überprüft, ob durch ihn eine mögliche zivilrechtliche oder strafrechtliche Verantwortung ausgelöst wird. Wir erhalten aber keine automatischen Informationen über Veränderungen der fremden Internetangebote und können deren Inhalte auch nicht ständig auf Veränderungen überprüfen. Deshalb können wir auch keine Verantwortung für diese übernehmen. Für illegale, fehlerhafte oder unvollständige Inhalte und insbesondere für Schäden, die aus der Nutzung oder Nichtnutzung von Informationen Dritter entstehen, haftet allein der jeweilige Anbieter des fremden Internetangebotes. Ich bemühe uns jedoch, die eingebundenen Verlinkungen regelmäßig bezüglich der genannten Kriterien zu prüfen.

Relationship between regional settlement structure and hospital incidence, type of therapy and mortality of non-ruptured abdominal aortic aneurysms

Fri, 01 Jan 2021 00:00:00 +0000

Geographic risk of general and abdominal obesity and related determinants in Iranian children and adolescents: CASPIAN-IV Study

Tue, 01 Dec 2020 00:00:00 +0000

Mindestmengeneffekte bei der Therapie des abdominalen Aortenaneurysmas

Fri, 27 Nov 2020 00:00:00 +0000

Cohesin depleted cells pass through mitosis and reconstitute a functional nuclear architecture.

Sun, 01 Nov 2020 00:00:00 +0000

Regional variation in endovascular treatment rate and in-hospital mortality of abdominal aortic aneurysms in Germany. Secondary data analysis of nationwide hospital DRG data from 2012 to 2014

Fri, 29 Nov 2019 00:00:00 +0000

Bayesian Age-Period-Cohort-Modelling and Prediction

Fri, 22 Feb 2019 00:00:00 +0000

bioimagetools

Fri, 22 Feb 2019 00:00:00 +0000

nucim

Fri, 22 Feb 2019 00:00:00 +0000

Software for Magnetic Resonance Imaging

Fri, 22 Feb 2019 00:00:00 +0000

R packages for working with DCE-MRI data:

dcemriS4: R package for the quantitative analysis of dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted imaging (DWI) for oncology applications. It provides methods for fitting voxel-wise kinetic models using Levenberg-Marquardt and using a Bayesian framework. Several kinetic models and arterial input functions are available.
oro.nifti: Functions for the input/output and visualization of medical imaging data that follow either the ANALYZE, NIfTI or AFNI formats.
cmR: cmR: cardiac magnetic resonance imaging in R
dcemri.SpatEnet: Analysing DCE-MRI using a spatially regularized Elastic Net.
dcemriboost: Voxelwise and spatially regularized boosting for DCE-MRI data.
adaptsmoFMRI: Adaptive smoothing of fMRI data.

Spatio-temporal pattern of two common cancers among Iranian woman: An adaptive smoothing model

Tue, 01 Jan 2019 00:00:00 +0000

Bayesian Age-Period-Cohort-Modelling and Prediction

Mon, 22 Oct 2018 09:00:00 +0000

Quantitative analysis of the nuclear landscape

Mon, 22 Oct 2018 09:00:00 +0000

The impact of model assumptions in scalar-on-image regression

Tue, 21 Aug 2018 00:00:00 +0000

Freie Mitarbeiterstelle an der Arbeitsgruppe Bioimaging

Wed, 25 Jul 2018 10:18:57 +0000

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:

Wissenschaftliche/r Mitarbeiter/in
(E13 TV-L, Vollbeschäftigung, befristet, Qualifizierungsstelle)
im Bereich Bayesianischer Bildverarbeitung
im Rahmen eines beantragten Kompetenzzentrum für Machine Learning

Die Ausschreibung richtet sich vorrangig an Doktorandinnen und Doktoranden. Sie kann aber auch mit einer Postdoktorandin oder einem Postdoktoranden besetzt werden.

Einstellungsvoraussetzungen

‐ abgeschlossenes wissenschaftliches Hochschulstudium (Staatsexamen, Master, Diplom oder vergleichbarer Abschluss) in Statistik oder einem eng verwandten Gebiet mit mindestens gutem Ergebnis

‐ Kenntnisse in Bayesianischer und/oder räumlicher Statistik

– Programmierkenntnisse, idealerweise in R und C++

‐ sichere Kenntnisse der deutschen und englischen Sprache

‐ Bereitschaft, ernsthaft und engagiert an einem Vorhaben der eigenen wissenschaftlichen Qualifizierung (Promotion) bzw. Weiterqualifizierung zu arbeiten

Bewerbungsadresse

Bewerbungen mit den üblichen Unterlagen (Lebenslauf, Zeugnisse, …) senden Sie bitte ausschliesslich in elektronischer Form als ein PDF-File an Prof. Volker Schmid, Mail: volker.schmid@lmu.de

Für weitere Informationen wenden Sie sich formlos an Volker Schmid.

Hinweis

Ihr Arbeitsplatz befindet sich in zentraler Lage in München und ist sehr gut mit öffentlichen Verkehrsmitteln zu erreichen. Wir bieten Ihnen eine interessante und verantwortungsvolle Tätigkeit mit guten Weiterbildungs‐ und Entwicklungsmöglichkeiten. Schwerbehinderte Bewerber/Bewerberinnen werden bei ansonsten im Wesentlichen gleicher Eignung bevorzugt. Die Bewerbung von Frauen wird begrüßt.

Regionale Haeufigkeit von revaskularisierenden Prozeduren bei Karotisstenose in Deutschland

Fri, 20 Jul 2018 00:00:00 +0000

Bivariate spatiotemporal disease mapping of cancer of the breast and cervix uteri among Iranian women

Mon, 01 Jan 2018 00:00:00 +0000

Editors Choice – High Annual Hospital Volume is Associated with Decreased in Hospital Mortality and Complication Rates Following Treatment of Abdominal Aortic Aneurysms: Secondary Data Analysis of the Nationwide German DRG Statistics from 2005 to 2013

Mon, 01 Jan 2018 00:00:00 +0000

Joint Spatio-Temporal Shared Component Model with an Application in Iran Cancer Data

Mon, 01 Jan 2018 00:00:00 +0000

Spatial Analysis of Hospital Incidence and in Hospital Mortality of Abdominal Aortic Aneurysms in Germany: Secondary Data Analysis of Nationwide Hospital Episode (DRG) Data

Mon, 01 Jan 2018 00:00:00 +0000

Fitting large-scale structured additive regression models

Sun, 01 Jan 2017 00:00:00 +0000

Initial high-resolution microscopic mapping of active and inactive regulatory sequences proves non-random 3D arrangements in chromatin domain clusters

Sun, 01 Jan 2017 00:00:00 +0000

Inzidenz, Therapie und Letalitat abdominaler Aortenaneurysmen

Sun, 01 Jan 2017 00:00:00 +0000

Maximum Entropy Approach in Dynamic Contrast-Enhanced Magnetic Resonance Imaging

Sun, 01 Jan 2017 00:00:00 +0000

Quantitative analyses of the 3D nuclear landscape recorded with super-resolved fluorescence microscopy

Sun, 01 Jan 2017 00:00:00 +0000

How to add dependencies on a bioconductor package

Mon, 17 Oct 2016 00:00:00 +0000

Our R package bioimagetools is on CRAN now. The package depends on EBImage, which is an Bioconductor package. The problem is, installing from CRAN via

install.packages("bioimagetools")

will only install dependencies from CRAN, not from Bioconductor (also Bioconductor is considered a „mainstream repository“). Some googling showed that I am not the only one with this issue. So here is the solution: In the DESCRIPTON file, add the line

Additional_repositories: https://bioconductor.org/packages/3.3/bioc/

The „Additional_repositories“ field was new to me. It will check the repositories with the given URL for the dependencies in the DESCRIPTION file. The more general „https://bioconductor.org/packages/bioc“ will also work, but throw a warning.

Fitting large-scale structured additive regression models using Krylov subspace methods

Sat, 23 Jul 2016 19:11:16 +0000

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.

Abstract:

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. A Markov chain Monte Carlo (MCMC) framework for the applied statistician is presented that allows to fit models with millions of parameters with only low to moderate computational requirements. The method combines a modified sampling scheme with novel accomplishments in iterative methods for sparse linear systems. This way a solution is given that eliminates potential computational burdens such as calculating the log-determinant of massive precision matrices and sampling from high-dimensional Gaussian distributions. In an extensive simulation study with models of moderate size it is shown that this approach gives results that are in perfect agreement with state-of-the-art methods for fitting structured additive regression models. Furthermore, the method is applied to two real world examples from the field of medical imaging.

Boosting in Nonlinear Regression Models with an Application to DCE-MRI Data

Fri, 01 Jan 2016 00:00:00 +0000

Stage-dependent remodeling of the nuclear envelope and lamina during rabbit early embryonic development

Fri, 01 Jan 2016 00:00:00 +0000

Bayesian mixed-effects model for the analysis of a series of FRAP images

Thu, 26 Mar 2015 16:26:34 +0000

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. We apply our method to a series of FRAP experiments of DNA methyltransferase 1 tagged to green fluorescent protein expressed in a somatic mouse cell line and compare the results to the application of three different fixed-effects models to the same series of FRAP experiments. With the proposed model, we get estimates of the off-rates of the interactions of the molecules under study together with credible intervals, and additionally gain information about the variability between nuclei. The proposed model is superior to and more robust than the tested fixed-effects models. Therefore, it can be used for the joint analysis of data from FRAP experiments on various similar nuclei.

Published in Statistical Applications in Genetics and Molecular Biology

Bayesian mixed-effects model for the analysis of a series of FRAP images

Thu, 01 Jan 2015 00:00:00 +0000

Remodeling of the Nuclear Envelope and Lamina during Bovine Preimplantation Development and Its Functional Implications

Thu, 01 Jan 2015 00:00:00 +0000

Pattern Recognition and Signal Analysis in Medical Imaging

Mon, 05 May 2014 08:21:45 +0000

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.

Link

Table of Content:

Introduction
Feature Selection and Extraction
Subband Coding and Wavelet Transform
The Wavelet Transform in Medical Imaging
Genetic Algorithms
Statistical and Syntactic Pattern Recognition
Foundations of Neural Networks
Transformation and Signal-Separation Neural Networks
Neuro-Fuzzy Classification
Specialized Neural Networks Relevant to Bioimaging
Spatio-Temporal Models in Functional and Perfusion Imaging
Analysis of Dynamic Susceptibility Contrast MRI Time-Series Based on Unsupervised Clustering Methods
Computer-Aided Diagnosis for Diagnostically Challenging Breast Lesions in DCE-MRI

Neue Veröffentlichung: Spatially regularized estimation for the analysis of DCE-MRI data

Sat, 15 Mar 2014 00:00:00 +0000

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. The form of the basis functions is derived from a kinetic model and thus describes the contribution of a specific compartment. Using a spatial elastic net estimator, we chose a sparse set of basis functions per voxel, and hence, rate constants of compartments. The spatial penalty takes into account the voxel structure of an image and performs better than a penalty treating voxels independently. The proposed estimation method is evaluated for simulated images and applied to an in vivo dataset.

Spatially regularized estimation for the analysis of dynamic contrast-enhanced magnetic resonance imaging data

Sat, 15 Mar 2014 00:00:00 +0000

More-compartment models for DCE-MRI

Tue, 11 Mar 2014 09:26:03 +0000

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.

Spatial two-tissue compartment model for dynamic contrast-enhanced magnetic resonance imaging. By Julia Sommer, and Volker J Schmid. Journal of the Royal Statistical Society: Series C. (LINK)

In the quantitative analysis of dynamic contrast-enhanced magnetic resonance imaging compartment models allow the uptake of contrast medium to be described with biologically meaningful kinetic parameters. As simple models often fail to describe adequately the observed uptake behaviour, more complex compartment models have been proposed. However, the non-linear regression problem arising from more complex compartment models often suffers from parameter redundancy. We incorporate spatial smoothness on the kinetic parameters of a two-tissue compartment model by imposing Gaussian Markov random-field priors on them. We analyse to what extent this spatial regularization helps to avoid parameter redundancy and to obtain stable parameter point estimates per voxel. Choosing a full Bayesian approach, we obtain posteriors and point estimates by running Markov chain Monte Carlo simulations. The approach proposed is evaluated for simulated concentration time curves as well as for in vivo data from a breast cancer study.

Spatially regularized estimation for the analysis of dynamic contrast-enhanced magnetic resonance imaging data. By Julia Sommer, Jan Gertheiss, and Volker J Schmid. Statistics in Medicine. (LINK)

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. The form of the basis functions is derived from a kinetic model and thus describes the contribution of a specific compartment. Using a spatial elastic net estimator, we chose a sparse set of basis functions per voxel, and hence, rate constants of compartments. The spatial penalty takes into account the voxel structure of an image and performs better than a penalty treating voxels independently. The proposed estimation method is evaluated for simulated images and applied to an in vivo dataset.

Pattern Recognition and Signal Analysis in Medical Imaging

Wed, 05 Mar 2014 00:00:00 +0000

Verfügbar als Hardcover, Paperback und eBook

Description

This book is an essential tool for students and professionals, compiling and explaining proven and cutting-edge methods in pattern recognition for medical imaging. View less >

Key Features

New edition has been expanded to cover signal analysis, which was only superficially covered in the first edition
New chapters cover Cluster Validity Techniques, Computer-Aided Diagnosis Systems in Breast MRI, Spatio-Temporal Models in Functional, Contrast-Enhanced and Perfusion Cardiovascular MRI
Gives readers an unparalleled insight into the latest pattern recognition and signal analysis technologies, modeling, and applications

Readership

Biomedical engineers; Electrical and electronics engineers; medical physicists; clinical and medical professionals; biomathematicians

Foundations of Medical Imaging; Feature Selection and Extraction; Theory of Subband Decomposition and Wavelets; The Wavelet Transform in Medical Imaging; Genetic Algorithms; Statistical Pattern Recognition; Syntactic Pattern Recognition; Neural Networks; Theory; Neural Networks: Applications; Fuzzy Logic: Theory and Clustering Algorithms; Computer Aided Diagnosis Systems

Future Declines of Coronary Heart Disease Mortality in England and Wales Could Counter the Burden of Population Ageing

Wed, 01 Jan 2014 00:00:00 +0000

Reprogramming of fibroblast nuclei in cloned bovine embryos involves major structural remodeling with both striking similarities and differences to nuclear phenotypes of in vitro fertilized embryos

Wed, 01 Jan 2014 00:00:00 +0000

Spatial two-tissue compartment model for dynamic contrast-enhanced magnetic resonance imaging

Wed, 01 Jan 2014 00:00:00 +0000

Three-dimensional super-resolution microscopy of the inactive X chromosome territory reveals a collapse of its active nuclear compartment harboring distinct Xist RNA foci.

Wed, 01 Jan 2014 00:00:00 +0000

Trends of breast cancer incidence in Iran during 2004-2008: A bayesian space-time model

Wed, 01 Jan 2014 00:00:00 +0000

Automatic post-picking using MAPPOS improves particle image detection from cryo-EM micrographs

Tue, 01 Jan 2013 00:00:00 +0000

Dissection of cell cycle dependent dynamics of Dnmt1 by FRAP and diffusion-coupled modeling

Tue, 01 Jan 2013 00:00:00 +0000

Fully Bayesian Inference for Structural MRI: Application to Segmentation and Statistical Analysis of T2-Hypointensities

Tue, 01 Jan 2013 00:00:00 +0000

Sensitivity to Prior Specification in Bayesian Genome-based Prediction Models

Tue, 01 Jan 2013 00:00:00 +0000

An automated tool for detection of FLAIR-hyperintense white-matter lesions in Multiple Sclerosis

Sun, 01 Jan 2012 00:00:00 +0000

Effect of Aging on Lung Structure In Vivo: Assessment With Densitometric and Fractal Analysis of High-resolution Computed Tomography Data.

Sun, 01 Jan 2012 00:00:00 +0000

Stepwise heterogeneity analysis of breast tumors in perfusion DCE-MRI datasets

Sun, 01 Jan 2012 00:00:00 +0000

The potential of 3D-FISH and super-resolution structured illumination microscopy for studies of 3D nuclear architecture: 3D structured illumination microscopy of defined chromosomal structures visualized by 3D (immuno)-FISH opens new perspectives for studies of nuclear architecture

Sun, 01 Jan 2012 00:00:00 +0000

A Bayesian hierarchical model for DCE-MRI to evaluate treatment response in a phase II study in advanced squamous cell carcinoma of the head and neck