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Myocardium Segmentation

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Showing 1–23 of 23 papers

TitleStatusHype
CineMA: A Foundation Model for Cine Cardiac MRICode2
TEDS-Net: Enforcing Diffeomorphisms in Spatial Transformers to Guarantee Topology Preservation in SegmentationsCode1
MvMM-RegNet: A new image registration framework based on multivariate mixture model and neural network estimationCode1
RS-MOCO: A deep learning-based topology-preserving image registration method for cardiac T1 mapping—0
An Improved Approach for Cardiac MRI Segmentation based on 3D UNet Combined with Papillary Muscle Exclusion—0
Segmenting Medical Images: From UNet to Res-UNet and nnUNet—0
Simultaneous Deep Learning of Myocardium Segmentation and T2 Quantification for Acute Myocardial Infarction MRI—0
Temporal-spatial Adaptation of Promptable SAM Enhance Accuracy and Generalizability of cine CMR Segmentation—0
Structure Preserving Cycle-GAN for Unsupervised Medical Image Domain Adaptation—0
Joint Deep Learning for Improved Myocardial Scar Detection from Cardiac MRI—0
Deep Statistic Shape Model for Myocardium Segmentation—0
Synthetic Velocity Mapping Cardiac MRI Coupled with Automated Left Ventricle Segmentation—0
Effects of Image Size on Deep Learning—0
Anatomically-Informed Deep Learning on Contrast-Enhanced Cardiac MRI for Scar Segmentation and Clinical Feature Extraction—0
Ensembling Low Precision Models for Binary Biomedical Image Segmentation—0
CondenseUNet: A Memory-Efficient Condensely-Connected Architecture for Bi-ventricular Blood Pool and Myocardium Segmentation—0
Segmentation of Multimodal Myocardial Images Using Shape-Transfer GAN—0
A multi-level convolutional LSTM model for the segmentation of left ventricle myocardium in infarcted porcine cine MR images—0
Improving Myocardium Segmentation in Cardiac CT Angiography using Spectral Information—0
Left Ventricle Segmentation and Quantification from Cardiac Cine MR Images via Multi-task Learning—0
Factorised spatial representation learning: application in semi-supervised myocardial segmentationCode0
Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis—0
Multivariate mixture model for myocardium segmentation combining multi-source images—0
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