Papers Myocardium Segmentation
“Myocardium Segmentation” 태그가 달린 논문 23편 · 필터 해제
CineMA: A Foundation Model for Cine Cardiac MRI
Cardiac magnetic resonance (CMR) is a key investigation in clinical cardiovascular medicine and has been used extensively in population research. However, extracting clinically important measurements such as ejection fra…
Myocardium SegmentationRS-MOCO: A deep learning-based topology-preserving image registration method for cardiac T1 mapping
Cardiac T1 mapping can evaluate various clinical symptoms of myocardial tissue. However, there is currently a lack of effective, robust, and efficient methods for motion correction in cardiac T1 mapping. In this paper, w…
Image RegistrationMyocardium SegmentationAn Improved Approach for Cardiac MRI Segmentation based on 3D UNet Combined with Papillary Muscle Exclusion
Left ventricular ejection fraction (LVEF) is the most important clinical parameter of cardiovascular function. The accuracy in estimating this parameter is highly dependent upon the precise segmentation of the left ventr…
MRI segmentationMyocardium SegmentationSegmentationSegmenting Medical Images: From UNet to Res-UNet and nnUNet
This study provides a comparative analysis of deep learning models including UNet, Res-UNet, Attention Res-UNet, and nnUNet, and evaluates their performance in brain tumour, polyp, and multi-class heart segmentation task…
DiagnosticHeart SegmentationMyocardium SegmentationRight Ventricle Segmentation+1Simultaneous Deep Learning of Myocardium Segmentation and T2 Quantification for Acute Myocardial Infarction MRI
In cardiac Magnetic Resonance Imaging (MRI) analysis, simultaneous myocardial segmentation and T2 quantification are crucial for assessing myocardial pathologies. Existing methods often address these tasks separately, li…
DecoderMyocardium SegmentationSegmentationTemporal-spatial Adaptation of Promptable SAM Enhance Accuracy and Generalizability of cine CMR Segmentation
Accurate myocardium segmentation across all phases in one cardiac cycle in cine cardiac magnetic resonance (CMR) scans is crucial for comprehensively cardiac function analysis. Despite advancements in deep learning (DL) …
Myocardium SegmentationSegmentationZero-shot GeneralizationStructure Preserving Cycle-GAN for Unsupervised Medical Image Domain Adaptation
The presence of domain shift in medical imaging is a common issue, which can greatly impact the performance of segmentation models when dealing with unseen image domains. Adversarial-based deep learning models, such as C…
Domain AdaptationMyocardium SegmentationSegmentationUnsupervised Domain AdaptationJoint 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. …
Myocardium SegmentationSegmentationDeep Statistic Shape Model for Myocardium Segmentation
Accurate segmentation and motion estimation of myocardium have always been important in clinic field, which essentially contribute to the downstream diagnosis. However, existing methods cannot always guarantee the shape …
modelMotion EstimationMyocardium SegmentationSegmentationSynthetic 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 SegmentationSegmentationTEDS-Net: Enforcing Diffeomorphisms in Spatial Transformers to Guarantee Topology Preservation in Segmentations
Accurate topology is key when performing meaningful anatomical segmentations, however, it is often overlooked in traditional deep learning methods. In this work we propose TEDS-Net: a novel segmentation method that guara…
Myocardium SegmentationSegmentationEffects 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. Non-extra pixel and extra pixe…
Deep LearningMyocardium SegmentationSemantic SegmentationAnatomically-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 pathophysiolog…
Myocardium SegmentationSegmentationEnsembling Low Precision Models for Binary Biomedical Image Segmentation
Segmentation of anatomical regions of interest such as vessels or small lesions in medical images is still a difficult problem that is often tackled with manual input by an expert. One of the major challenges for this ta…
Image SegmentationLesion SegmentationMyocardium SegmentationSegmentation+1MvMM-RegNet: A new image registration framework based on multivariate mixture model and neural network estimation
Current deep-learning-based registration algorithms often exploit intensity-based similarity measures as the loss function, where dense correspondence between a pair of moving and fixed images is optimized through backpr…
Heart SegmentationImage RegistrationMyocardium SegmentationSegmentationCondenseUNet: 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 radiatio…
Cardiac SegmentationImage SegmentationMyocardium Segmentationparameter estimation+2Segmentation 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. The pathological myocardium in LGE images presents distinctive bright…
Myocardium SegmentationSegmentationA multi-level convolutional LSTM model for the segmentation of left ventricle myocardium in infarcted porcine cine MR images
Automatic segmentation of left ventricle (LV) myocardium in cardiac short-axis cine MR images acquired on subjects with myocardial infarction is a challenging task, mainly because of the various types of image inhomogene…
Myocardium SegmentationImproving Myocardium Segmentation in Cardiac CT Angiography using Spectral Information
Accurate segmentation of the left ventricle myocardium in cardiac CT angiography (CCTA) is essential for e.g. the assessment of myocardial perfusion. Automatic deep learning methods for segmentation in CCTA might suffer …
Data AugmentationMyocardium SegmentationSegmentationLeft 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+1