28 citations · 47 across the 8 of their papers we have counts for
6 papers · 1 filter
Signal to Noise and b-value Analysis for Optimal Intra-Voxel Incoherent Motion Imaging in the Brain
Harri Merisaari, Christian Federau
Intravoxel incoherent motion (IVIM) is a method that can provide quantitative information about perfusion in the human body, in vivo, and without contrast agent. Unfortunately, the…
Improved Segmentation and Detection Sensitivity of Diffusion-Weighted Brain Infarct Lesions with Synthetically Enhanced Deep Learning
Christian Federau, Soren Christensen, Nino Scherrer +7
Purpose: To compare the segmentation and detection performance of a deep learning model trained on a database of human-labelled clinical diffusion-weighted (DW) stroke lesions to a…
Simulation of Intravoxel Incoherent Perfusion Signal Using a Realistic Capillary Network of a Mouse Brain
Valerie Phi van, Franca Schmid, Georg Spinner +2
Purpose: To simulate the intravoxel incoherent perfusion magnetic resonance magnitude signal from the motion of blood particles in three realistic vascular network graphs from a mo…
Image Translation for Medical Image Generation -- Ischemic Stroke Lesions
Moritz Platscher, Jonathan Zopes, Christian Federau
Deep learning based disease detection and segmentation algorithms promise to improve many clinical processes. However, such algorithms require vast amounts of annotated training da…
Multi-modal segmentation of 3D brain scans using neural networks
Jonathan Zopes, Moritz Platscher, Silvio Paganucci +1
Purpose: To implement a brain segmentation pipeline based on convolutional neural networks, which rapidly segments 3D volumes into 27 anatomical structures. To provide an extensive…
Diffusion-Weighted Magnetic Resonance Brain Images Generation with Generative Adversarial Networks and Variational Autoencoders: A Comparison Study
Alejandro Ungría Hirte, Moritz Platscher, Thomas Joyce +3
We show that high quality, diverse and realistic-looking diffusion-weighted magnetic resonance images can be synthesized using deep generative models. Based on professional neurora…