3 papers
eess.IV2021
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…
eess.IV2021
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…
eess.IV2020
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…