Myocardium Segmentation
3 papers with code • 1 benchmarks • 0 datasets
Latest papers with no code
Cardiac Magnetic Resonance 2D+T Short- and Long-axis Segmentation via Spatio-temporal SAM Adaptation
Accurate 2D+T myocardium segmentation in cine cardiac magnetic resonance (CMR) scans is essential to analyze LV motion throughout the cardiac cycle comprehensively.
Structure Preserving Cycle-GAN for Unsupervised Medical Image Domain Adaptation
SP Cycle-GAN achieved a state of the art Myocardium segmentation Dice score (DSC) of 0. 7435 for the MR to CT MM-WHS domain adaptation problem, and excelled in nearly all categories for the MM-WHS dataset.
Joint Deep Learning for Improved Myocardial Scar Detection from Cardiac MRI
Automated identification of myocardial scar from late gadolinium enhancement cardiac magnetic resonance images (LGE-CMR) is limited by image noise and artifacts such as those related to motion and partial volume effect.
Deep Statistic Shape Model for Myocardium Segmentation
Additionally, the predicted point cloud guarantees boundary correspondence for sequential images, which contributes to the downstream tasks, such as the motion estimation of myocardium.
Synthetic Velocity Mapping Cardiac MRI Coupled with Automated Left Ventricle Segmentation
Temporal patterns of cardiac motion provide important information for cardiac disease diagnosis.
Effects of Image Size on Deep Learning
In this work, the best size for late gadolinium enhancement (LGE) magnetic resonance imaging (MRI) images in the training dataset was determined to optimize deep learning training outcomes.
Anatomically-Informed Deep Learning on Contrast-Enhanced Cardiac MRI for Scar Segmentation and Clinical Feature Extraction
Visualizing disease-induced scarring and fibrosis in the heart on cardiac magnetic resonance (CMR) imaging with contrast enhancement (LGE) is paramount in characterizing disease progression and quantifying pathophysiological substrates of arrhythmias.
Ensembling Low Precision Models for Binary Biomedical Image Segmentation
Our core idea is straightforward: A diverse ensemble of low precision and high recall models are likely to make different false positive errors (classifying background as foreground in different parts of the image), but the true positives will tend to be consistent.
CondenseUNet: A Memory-Efficient Condensely-Connected Architecture for Bi-ventricular Blood Pool and Myocardium Segmentation
With the advent of Cardiac Cine Magnetic Resonance (CMR) Imaging, there has been a paradigm shift in medical technology, thanks to its capability of imaging different structures within the heart without ionizing radiation.
Segmentation of Multimodal Myocardial Images Using Shape-Transfer GAN
Myocardium segmentation of late gadolinium enhancement (LGE) Cardiac MR images is important for evaluation of infarction regions in clinical practice.