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Found 5 papers, 1 papers with code
Predicting 4D Liver MRI for MR-guided Interventions
Our method can be used in two ways: First, it can reconstruct near real-time 4D MRI with high quality and high resolution (209x128x128 matrix size with isotropic 1. 8mm voxel size and 0. 6s/volume) given a dynamic interventional 2D navigator slice for guidance during an intervention.
Uncertainty-Aware Temporal Self-Learning (UATS): Semi-Supervised Learning for Segmentation of Prostate Zones and Beyond
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 peripheral zone (PZ).
Learning Multi-Modal Volumetric Prostate Registration with Weak Inter-Subject Spatial Correspondence
We show that the combination of mDSC and SDM similarity measures results in a more accurate and natural transformation pattern together with a stronger gradient coverage.
Anisotropic 3D Multi-Stream CNN for Accurate Prostate Segmentation from Multi-Planar MRI
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 monitoring of focal and loco-regional therapeutic interventions.
4D MRI: Robust sorting of free breathing MRI slices for use in interventional settings
Interventional radiologists perceive the reconstruction quality of our method as higher compared to the baseline (262. 5 points vs. 217. 5 points, p=0. 02).
