35 citations · 48 across the 9 of their papers we have counts for
5 papers · 1 filter
Seeing Beyond the Lesion: Disease Recognition from Reactive CNS Tissue
Jan Schnorrenberg, Jan Ernsting, Enrico Küllenberg +3
Sampling error yields exclusively reactive, non-lesional brain parenchyma in a significant proportion of intracranial biopsies, leaving the underlying disease undiagnosed. We bench…
Population-Scale Segmentation of Penile Tissue in DIXON MRI using Deep Learning for Quantitative Phenotyping in Male Reproductive Health
Jan Ernsting, Gunnar Paul Kordes, Nils Johannaber +5
Penile measurement is clinically relevant across male reproductive and urogenital health, including conditions such as micropenis, congenital and endocrine disorders, and sexual or…
deepmriprep: Voxel-based Morphometry (VBM) Preprocessing via Deep Neural Networks
Lukas Fisch, Nils R. Winter, Janik Goltermann +25
Voxel-based Morphometry (VBM) has emerged as a powerful approach in neuroimaging research, utilized in over 7,000 studies since the year 2000. Using Magnetic Resonance Imaging (MRI…
Deepbet: Fast brain extraction of T1-weighted MRI using Convolutional Neural Networks
Lukas Fisch, Stefan Zumdick, Carlotta Barkhau +8
Brain extraction in magnetic resonance imaging (MRI) data is an important segmentation step in many neuroimaging preprocessing pipelines. Image segmentation is one of the research…
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…