4 papers
Uncertainty-Aware Temporal Self-Learning (UATS): Semi-Supervised Learning for Segmentation of Prostate Zones and Beyond
Anneke Meyer, Suhita Ghosh, Daniel Schindele +4
Various convolutional neural network (CNN) based concepts have been introduced for the prostate's automatic segmentation and its coarse subdivision into transition zone (TZ) and pe…
Learning Multi-Modal Volumetric Prostate Registration with Weak Inter-Subject Spatial Correspondence
Oleksii Bashkanov, Anneke Meyer, Daniel Schindele +4
Recent studies demonstrated the eligibility of convolutional neural networks (CNNs) for solving the image registration problem. CNNs enable faster transformation estimation and gre…
Anisotropic 3D Multi-Stream CNN for Accurate Prostate Segmentation from Multi-Planar MRI
Anneke Meyer, Grzegorz Chlebus, Marko Rak +8
Background and Objective: Accurate and reliable segmentation of the prostate gland in MR images can support the clinical assessment of prostate cancer, as well as the planning and…
4D MRI: Robust sorting of free breathing MRI slices for use in interventional settings
Gino Gulamhussene, Fabian Joeres, Marko Rak +2
Purpose: We aim to develop a robust 4D MRI method for large FOVs enabling the extraction of irregular respiratory motion that is readily usable with all MRI machines and thus appli…