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Boundary Attentive Spatial Multi-scale Network for Cardiac MRI Image Segmentation
Accurate automatic segmentation of cardiac MRI images can be used for clinical parameter calculation and provide visual guidance for surgery, which...
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Right Ventricle Segmentation via Registration and Multi-input Modalities in Cardiac Magnetic Resonance Imaging from Multi-disease, Multi-view and Multi-center
Quantitative assessment of cardiac function requires accurate segmentation of cardiac structures. Convolutional Neural Networks (CNNs) have achieved...
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Weakly Supervised Semantic Segmentation of Echocardiography Videos via Multi-level Features Selection
Echocardiogram illustrates what the capacity it owns of detecting the global and regional functions of the heart. With obvious benefits of...
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Reconstruction of 5D cardiac MRI through the blood flow registration: simulation of the fifth dimension and assessment of the left ventricular ejection fraction
In this paper, a medical decision approach for cardiac MRI by the registration algorithm and the reconstruction of 5D stacks for cine MRI sequences...
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Effect of Varying Pericardial Boundary Conditions on Whole Heart Function: A Computational Study
Pericardiectomy is recommended therapy for pericarditis, an inflammation of the pericardial layers that surround the heart and play a central role in...
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Effect of Spatial and Temporal Resolution on the Accuracy of Motion Tracking Using 2D and 3D Cine Cardiac Magnetic Resonance Imaging Data
In this paper, we investigate the effect of spatial and temporal resolution of cardiac MRI cine images on the extracted left ventricle motion. A...
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Semi-supervised Learning Based Right Ventricle Segmentation Using Deep Convolutional Boltzmann Machine Shape Model
Automated Right Ventricle (RV) segmentation is a challenge due to the RV’s variable shape and the lack of labelled data. This paper proposes a...
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Cardiac MRI Left Ventricular Segmentation and Function Quantification Using Pre-trained Neural Networks
Deep learning has demonstrated promise for cardiac magnetic resonance image (MRI) segmentation. However, the performance is degraded when a trained...
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Contrastive Pretraining for Echocardiography Segmentation with Limited Data
Contrastive learning has proven useful in many applications where access to labelled data is limited. The lack of annotated data is particularly...
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Implementing Machine Vision Process to Analyze Echocardiography for Heart Health Monitoring
Machine vision analysis of echocardiography images (echo) has vital recent advances. Echocardiography images are ultrasound scans that present the...
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Forward Uncertainty Quantification and Sensitivity Analysis of the Holzapfel-Ogden Model for the Left Ventricular Passive Mechanics
Cardiovascular diseases are still responsible for many deaths worldwide, and computational models are essential tools for a better understanding of...
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A self-optimized software tool for quantifying the degree of left ventricle hyper-trabeculation
Left ventricular non-compaction is characterized by the presence of multiple trabecules in the left ventricle myocardium, associated with multiple...
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Long Axis Cardiac MRI Segmentation Using Anatomically-Guided UNets and Transfer Learning
In this work we present a machine learning model to segment long axis magnetic resonance images of the left ventricle (LV) and address the challenges...
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Deep Conditional Shape Models for 3D Cardiac Image Segmentation
Delineation of anatomical structures is often the first step of many medical image analysis workflows. While convolutional neural networks achieve...
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Left Ventricle Full Quantification Using Deep Layer Aggregation Based Multitask Relationship Learning
Left ventricle full quantification is important in the assessment of cardiac functionality and diagnosis of cardiac diseases, but is also challenging...
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Left Ventricle Full Quantification via Hierarchical Quantification Network
Automatic quantitative analysis of cardiac left ventricle (LV) function is one of challenging task for heart disease diagnosis. Four different...
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Mesh Based Approximation of the Left Ventricle Using a Controlled Shrinkwrap Algorithm
This research paper introduces an adaptive algorithm to reconstruct the left ventricle from computer tomographic images (CT). Often, manual image...
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Automatic Image Quality Assessment and Cardiac Segmentation Based on CMR Images
This paper describes our methods for two tasks: automatic image quality assessment and cardiac segmentation based on cardiovascular magnetic...
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Robust Cardiac MRI Segmentation with Data-Centric Models to Improve Performance via Intensive Pre-training and Augmentation
Segmentation of anatomical structures from Cardiac Magnetic Resonance (CMR) is central to the non-invasive quantitative assessment of cardiac...
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Biomechanical Model to Aid Surgical Planning in Complex Congenital Heart Diseases
In the patients with congenitally corrected transposition of great arteries (ccTGA) it is important to evaluate the function of the left ventricle...