61 citations · 112 across the 9 of their papers we have counts for
9 papers
VIBESegmentator: Full Body MRI Segmentation for the NAKO and UK Biobank
Robert Graf, Paul-Sören Platzek, Evamaria Olga Riedel +17
Objectives: To present a publicly available deep learning-based torso segmentation model that provides comprehensive voxel-wise coverage, including delineations that extend to the…
MRSegmentator: Multi-Modality Segmentation of 40 Classes in MRI and CT
Hartmut Häntze, Lina Xu, Christian J. Mertens +29
Purpose: To develop and evaluate a deep learning model for multi-organ segmentation of MRI scans. Materials and Methods: The model was trained on 1,200 manually annotated 3D axial…
FedNorm: Modality-Based Normalization in Federated Learning for Multi-Modal Liver Segmentation
Tobias Bernecker, Annette Peters, Christopher L. Schlett +5
Given the high incidence and effective treatment options for liver diseases, they are of great socioeconomic importance. One of the most common methods for analyzing CT and MRI ima…
An Uncertainty-Aware, Shareable and Transparent Neural Network Architecture for Brain-Age Modeling
Tim Hahn, Jan Ernsting, Nils R. Winter +31
The deviation between chronological age and age predicted from neuroimaging data has been identified as a sensitive risk-marker of cross-disorder brain changes, growing into a corn…
Predicting brain-age from raw T 1 -weighted Magnetic Resonance Imaging data using 3D Convolutional Neural Networks
Lukas Fisch, Jan Ernsting, Nils R. Winter +33
Age prediction based on Magnetic Resonance Imaging (MRI) data of the brain is a biomarker to quantify the progress of brain diseases and aging. Current approaches rely on preparing…
Bayesian Neural Networks for Uncertainty Estimation of Imaging Biomarkers
J. Senapati, A. Guha Roy, S. Pölsterl +6
Image segmentation enables to extract quantitative measures from scans that can serve as imaging biomarkers for diseases. However, segmentation quality can vary substantially acros…
Fully Automated and Standardized Segmentation of Adipose Tissue Compartments by Deep Learning in Three-dimensional Whole-body MRI of Epidemiological Cohort Studies
Thomas Küstner, Tobias Hepp, Marc Fischer +9
Purpose: To enable fast and reliable assessment of subcutaneous and visceral adipose tissue compartments derived from whole-body MRI. Methods: Quantification and localization of di…
Deep Shape Analysis on Abdominal Organs for Diabetes Prediction
Benjamin Gutierrez-Becker, Sergios Gatidis, Daniel Gutmann +3
Morphological analysis of organs based on images is a key task in medical imaging computing. Several approaches have been proposed for the quantitative assessment of morphological…
A Machine-learning framework for automatic reference-free quality assessment in MRI
Thomas Küstner, Sergios Gatidis, Annika Liebgott +9
Magnetic resonance (MR) imaging offers a wide variety of imaging techniques. A large amount of data is created per examination which needs to be checked for sufficient quality in o…