Papers Left Ventricle Segmentation
“Left Ventricle Segmentation” 태그가 달린 논문 25편 · 필터 해제
EchoNet-Quality: Denoising Echocardiograms via Deep Generative Modeling of Ultrasound Noise
Echocardiography (echo), or cardiac ultrasound, is the most widely used imaging modality for cardiac form and function due to its relatively low cost, rapid acquisition time, and non-invasive nature. However, ultrasound …
DenoisingDiagnosticImage DenoisingLeft Ventricle SegmentationFedDA-TSformer: Federated Domain Adaptation with Vision TimeSformer for Left Ventricle Segmentation on Gated Myocardial Perfusion SPECT Image
Background and Purpose: Functional assessment of the left ventricle using gated myocardial perfusion (MPS) single-photon emission computed tomography relies on the precise extraction of the left ventricular contours whil…
Domain AdaptationFederated LearningLeft Ventricle SegmentationLV SegmentationTwo-Phase Segmentation Approach for Accurate Left Ventricle Segmentation in Cardiac MRI using Machine Learning
Accurate segmentation of the Left Ventricle (LV) holds substantial importance due to its implications in disease detection, regional analysis, and the development of complex models for cardiac surgical planning. CMR is a…
Left Ventricle SegmentationLV SegmentationSegmentationSimLVSeg: Simplifying Left Ventricular Segmentation in 2D+Time Echocardiograms with Self- and Weakly-Supervised Learning
Echocardiography has become an indispensable clinical imaging modality for general heart health assessment. From calculating biomarkers such as ejection fraction to the probability of a patient's heart failure, accurate …
Left Ventricle SegmentationLV SegmentationSegmentationSelf-Supervised Learning+3HDL: Hybrid Deep Learning for the Synthesis of Myocardial Velocity Maps in Digital Twins for Cardiac Analysis
Synthetic digital twins based on medical data accelerate the acquisition, labelling and decision making procedure in digital healthcare. A core part of digital healthcare twins is model-based data synthesis, which permit…
Decision MakingGenerative Adversarial NetworkLeft Ventricle SegmentationContrastive 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 problematic in medical image segmentation as it is difficult to have clinic…
Contrastive LearningImage SegmentationLeft Ventricle SegmentationMedical Image Segmentation+2Synthetic Velocity Mapping Cardiac MRI Coupled with Automated Left Ventricle Segmentation
Temporal patterns of cardiac motion provide important information for cardiac disease diagnosis. This pattern could be obtained by three-directional CINE multi-slice left ventricular myocardial velocity mapping (3Dir MVM…
Left Ventricle SegmentationMyocardium SegmentationSegmentationThe Impact of Domain Shift on Left and Right Ventricle Segmentation in Short Axis Cardiac MR Images
Domain shift refers to the difference in the data distribution of two datasets, normally between the training set and the test set for machine learning algorithms. Domain shift is a serious problem for generalization of …
Left Ventricle SegmentationRight Ventricle SegmentationSegmentationAutomated Multi-sequence Cardiac MRI Segmentation Using Supervised Domain Adaptation
Left ventricle segmentation and morphological assessment are essential for improving diagnosis and our understanding of cardiomyopathy, which in turn is imperative for reducing risk of myocardial infarctions in patients.…
DecoderDomain AdaptationImage SegmentationLeft Ventricle Segmentation+4A Novel Deep Learning Based Approach for Left Ventricle Segmentation in Echocardiography: MFP-Unet
Segmentation of the Left ventricle (LV) is a crucial step for quantitative measurements such as area, volume, and ejection fraction. However, the automatic LV segmentation in 2D echocardiographic images is a challenging …
DecoderImage SegmentationLeft Ventricle SegmentationLV Segmentation+3Curriculum semi-supervised segmentation
This study investigates a curriculum-style strategy for semi-supervised CNN segmentation, which devises a regression network to learn image-level information such as the size of a target region. These regressions are use…
Left Ventricle SegmentationregressionSegmentationSemantic Segmentation+1Spatio-Temporal Convolutional LSTMs for Tumor Growth Prediction by Learning 4D Longitudinal Patient Data
Prognostic tumor growth modeling via volumetric medical imaging observations can potentially lead to better outcomes of tumor treatment and surgical planning. Recent advances of convolutional networks have demonstrated h…
Generative Adversarial NetworkImage SegmentationLeft Ventricle SegmentationMedical Image Segmentation+1Explicit topological priors for deep-learning based image segmentation using persistent homology
We present a novel method to explicitly incorporate topological prior knowledge into deep learning based segmentation, which is, to our knowledge, the first work to do so. Our method uses the concept of persistent homolo…
Image SegmentationLeft Ventricle SegmentationSegmentationSemantic Segmentation+1End-to-end Learning of Convolutional Neural Net and Dynamic Programming for Left Ventricle Segmentation
Differentiable programming is able to combine different functions or programs in a processing pipeline with the goal of applying end-to-end learning or optimization. A significant impediment is the non-differentiable nat…
Left Ventricle SegmentationLeft Ventricle Segmentation via Optical-Flow-Net from Short-axis Cine MRI: Preserving the Temporal Coherence of Cardiac Motion
Quantitative assessment of left ventricle (LV) function from cine MRI has significant diagnostic and prognostic value for cardiovascular disease patients. The temporal movement of LV provides essential information on the…
DiagnosticLeft Ventricle SegmentationLV SegmentationOptical Flow Estimation+1Left Ventricle Segmentation and Quantification from Cardiac Cine MR Images via Multi-task Learning
Segmentation of the left ventricle and quantification of various cardiac contractile functions is crucial for the timely diagnosis and treatment of cardiovascular diseases. Traditionally, the two tasks have been tackled …
Left Ventricle SegmentationLV SegmentationMulti-Task LearningMyocardium Segmentation+1Multi-Scale Fully Convolutional Network for Cardiac Left Ventricle Segmentation
The morphological structure of left ventricle segmented from cardiac magnetic resonance images can be used to calculate key clinical parameters, and it is of great significance to the accurate and efficient diagnosis of …
DecoderLeft Ventricle SegmentationSegmentationSemantic SegmentationLeft Ventricle Segmentation and Volume Estimation on Cardiac MRI using Deep Learning
In the United States, heart disease is the leading cause of death for both men and women, accounting for 610,000 deaths each year [1]. Physicians use Magnetic Resonance Imaging (MRI) scans to take images of the heart in …
Distributed ComputingGPULeft Ventricle SegmentationLV Segmentation+1VoxelAtlasGAN: 3D Left Ventricle Segmentation on Echocardiography with Atlas Guided Generation and Voxel-to-voxel Discrimination
3D left ventricle (LV) segmentation on echocardiography is very important for diagnosis and treatment of cardiac disease. It is not only because of that echocardiography is a real-time imaging technology and widespread i…
Left Ventricle SegmentationLV SegmentationSegmentationSelf-LearningHybrid Forests for Left Ventricle Segmentation using only the first slice label
Machine learning models produce state-of-the-art results in many MRI images segmentation. However, most of these models are trained on very large datasets which come from experts manual labeling. This labeling process is…
Left Ventricle SegmentationSegmentation