R. Opfer

发表

Tight frame expansions of multiscale reproducing kernels in Sobolev spaces

R. Opfer, 2006 .

MRI FLAIR lesion segmentation in multiple sclerosis: Does automated segmentation hold up with manual annotation?

Roland Opfer, Maria Pia Sormani, Timo Kepp, 2016, NeuroImage: Clinical.

Results on meshless collocation techniques

R. Schaback, R. Opfer, L. Ling, 2006 .

Atlas based brain volumetry: How to distinguish regional volume changes due to biological or physiological effects from inherent noise of the methodology.

H. Huppertz, R. Opfer, L. Spies, 2016, Magnetic resonance imaging.

Single-subject analysis of regional brain volumetric measures can be strongly influenced by the method for head size adjustment

R. Buchert, R. Opfer, L. Spies, 2022, Neuroradiology.

Fully automatic detection of deep white matter T1 hypointense lesions in multiple sclerosis

Roland Opfer, Ralph Buchert, Per Suppa, 2013, Physics in medicine and biology.

Estimates of age-dependent cutoffs for pathological brain volume loss using SIENA/FSL—a longitudinal brain volumetry study in healthy adults

M. Sormani, R. Opfer, N. Stefano, 2017, Neurobiology of Aging.

FDG PET/CT in cancer therapy monitoring

S Renisch, W Brenner, I. Apostolova, 2010, Nuklearmedizin.

MRI-Based Brain Volumetry at a Single Time Point Complements Clinical Evaluation of Patients With Multiple Sclerosis in an Outpatient Setting

R. Opfer, L. Spies, G. Winkler, 2018, Front. Neurol..

Computer Aided Detection and Quantification

Thomas Bülow, Rafael Wiemker, Patrik Rogalla, 2006 .

Single-subject analysis of regional brain volumetric measures can be strongly influenced by the method for head size adjustment

R. Buchert, R. Opfer, L. Spies, 2022, Neuroradiology.

Data-Efficient Vision Transformers for Multi-Label Disease Classification on Chest Radiographs

R. Opfer, A. Schlaefer, J. Krüger, 2022, Current Directions in Biomedical Engineering.

Automatic segmentation of the thalamus using a massively trained 3D convolutional neural network: higher sensitivity for the detection of reduced thalamus volume by improved inter-scanner stability

R. Buchert, R. Opfer, L. Spies, 2022, European Radiology.

T-Cell Specificity Influences Disease Heterogeneity in Multiple Sclerosis

R. Opfer, I. Jelcic, R. Planas, 2021, Neurology: Neuroimmunology & Neuroinflammation.

MS optic neuritis-induced long-term structural changes within the visual pathway

M. Hoffmann, R. Opfer, J. Kaufmann, 2020, Neurology: Neuroimmunology & Neuroinflammation.

Global and regional annual brain volume loss rates in physiological aging

Roland Opfer, Ann-Christin Ostwaldt, Sven Schippling, 2017, Journal of Neurology.

Comparative performance analysis for computer aided lung nodule detection and segmentation on ultra-low-dose vs. standard-dose CT

Thomas Bülow, Rafael Wiemker, Patrik Rogalla, 2006, SPIE Medical Imaging.

Unsupervised anomaly detection in 3D brain MRI using deep learning with multi-task brain age prediction

R. Opfer, A. Schlaefer, M. Laves, 2022, Medical Imaging.

Toward computer-aided emphysema quantification on ultralow-dose CT: reproducibility of ventrodorsal gravity effect measurement and correction

Thomas Bülow, Rafael Wiemker, Roland Opfer, 2007, SPIE Medical Imaging.

Infratentorial lesions in multiple sclerosis patients: intra- and inter-rater variability in comparison to a fully automated segmentation using 3D convolutional neural networks

R. Opfer, L. Spies, S. Schippling, 2021, European Radiology.

Fully Automatic MRI-Based Hippocampus Volumetry Using FSL-FIRST: Intra-Scanner Test-Retest Stability, Inter-Field Strength Variability, and Performance as Enrichment Biomarker for Clinical Trials Using Prodromal Target Populations at Risk for Alzheimer's Disease.

Roland Opfer, Simone Lista, Enrica Cavedo, 2017, Journal of Alzheimer's disease : JAD.

Three-dimensional deep learning with spatial erasing for unsupervised anomaly segmentation in brain MRI

Marcel Bengs, Alexander Schlaefer, Roland Opfer, 2021, International Journal of Computer Assisted Radiology and Surgery.

Multiple Sclerosis Lesion Activity Segmentation with Attention-Guided Two-Path CNNs

Nils Gessert, Alexander Schlaefer, Roland Opfer, 2020, Comput. Medical Imaging Graph..

Fully automated longitudinal segmentation of new or enlarged multiple sclerosis lesions using 3D convolutional neural networks

Nils Gessert, Alexander Schlaefer, Roland Opfer, 2020, NeuroImage: Clinical.

4D Deep Learning for Multiple Sclerosis Lesion Activity Segmentation pdf

Marcel Bengs, Nils Gessert, Alexander Schlaefer, 2020, ArXiv.

Within-patient fluctuation of brain volume estimates from short-term repeated MRI measurements using SIENA/FSL

Roland Opfer, Maria Pia Sormani, Ann-Christin Ostwaldt, 2018, Journal of Neurology.

Age-dependent cut-offs for pathological deep gray matter and thalamic volume loss using Jacobian integration

R. Opfer, L. Spies, S. Schippling, 2020, NeuroImage: Clinical.

Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI

R. Opfer, A. Schlaefer, Finn Behrendt, 2023, ArXiv.

A systematic approach to deep learning-based nodule detection in chest radiographs

R. Opfer, A. Schlaefer, J. Krüger, 2023, Scientific reports.

Automatic lesion tracking for a PET/CT based computer aided cancer therapy monitoring system

Rafael Wiemker, Roland Opfer, Ingwer Carlsen, 2008, SPIE Medical Imaging.

Guided Reconstruction with Conditioned Diffusion Models for Unsupervised Anomaly Detection in Brain MRIs

R. Opfer, J. Krüger, Finn Behrendt, 2023, ArXiv.

Estimates of age-dependent cutoffs for pathological brain volume loss using SIENA/FSL—a longitudinal brain volumetry study in healthy adults

M. Sormani, R. Opfer, N. Stefano, 2017, Neurobiology of Aging.

Unsupervised Anomaly Detection in 3D Brain MRI Using Deep Learning with Impured Training Data

R. Opfer, A. Schlaefer, M. Bengs, 2022, 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI).

Fully automated longitudinal segmentation of new or enlarged multiple sclerosis lesions using 3D convolutional neural networks

R. Opfer, S. Schippling, A. Schlaefer, 2020, NeuroImage: Clinical.

AI-based detection of contrast-enhancing MRI lesions in patients with multiple sclerosis

M. Mühlau, R. Opfer, D. Hedderich, 2023, Insights into Imaging.