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Found 5 papers, 1 papers with code
Pathology Synthesis of 3D-Consistent Cardiac MR Images using 2D VAEs and GANs
We devise three approaches for label manipulation in the latent space of the trained VAE model; i) \textbf{intra-subject synthesis} aiming to interpolate the intermediate slices of a subject to increase the through-plane resolution, ii) \textbf{inter-subject synthesis} aiming to interpolate the geometry and appearance of intermediate images between two dissimilar subjects acquired with different scanner vendors, and iii) \textbf{pathology synthesis} aiming to synthesize a series of pseudo-pathological synthetic subjects with characteristics of a desired heart disease.
sim2real: Cardiac MR Image Simulation-to-Real Translation via Unsupervised GANs
There has been considerable interest in the MR physics-based simulation of a database of virtual cardiac MR images for the development of deep-learning analysis networks.
XCAT-GAN for Synthesizing 3D Consistent Labeled Cardiac MR Images on Anatomically Variable XCAT Phantoms
Moreover, the improvement in utilizing synthetic images for augmenting the real data is evident through the reduction of Hausdorff distance up to 28% and an increase in the Dice score up to 5%, indicating a higher similarity to the ground truth in all dimensions.
4D Semantic Cardiac Magnetic Resonance Image Synthesis on XCAT Anatomical Model
Using the parameterized motion model of the XCAT heart, we generate labels for 25 time frames of the heart for one cardiac cycle at 18 locations for the short axis view.
Deep Learning Based Rib Centerline Extraction and Labeling
In this paper, we combine a deep learning-based rib detection with a dedicated centerline extraction algorithm applied to the detection result for the purpose of fast, robust and accurate rib centerline extraction and labeling from CT volumes.
