Papers Heart Segmentation
“Heart Segmentation” 태그가 달린 논문 30편 · 필터 해제
Foundation Model for Whole-Heart Segmentation: Leveraging Student-Teacher Learning in Multi-Modal Medical Imaging
Whole-heart segmentation from CT and MRI scans is crucial for cardiovascular disease analysis, yet existing methods struggle with modality-specific biases and the need for extensive labeled datasets. To address these cha…
Heart SegmentationSegmentationSelf-Supervised LearningUnsupervised detection and classification of heartbeats using the dissimilarity matrix in PCG signals
The proposed system consists of a two-stage cascade. The first stage performs a rough heartbeat detection while the second stage refines the previous one, improving the temporal localization and also classifying the hear…
Heart SegmentationSound ClassificationTemporal LocalizationPreserving Cardiac Integrity: A Topology-Infused Approach to Whole Heart Segmentation
Whole heart segmentation (WHS) supports cardiovascular disease (CVD) diagnosis, disease monitoring, treatment planning, and prognosis. Deep learning has become the most widely used method for WHS applications in recent y…
Heart SegmentationPrognosisSegmentationSegmenting 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+1Transforming Heart Chamber Imaging: Self-Supervised Learning for Whole Heart Reconstruction and Segmentation
Automated segmentation of Cardiac Magnetic Resonance (CMR) plays a pivotal role in efficiently assessing cardiac function, offering rapid clinical evaluations that benefit both healthcare practitioners and patients. Whil…
Heart SegmentationSegmentationSelf-Supervised LearningSemantic SegmentationMultimodal Information Interaction for Medical Image Segmentation
The use of multimodal data in assisted diagnosis and segmentation has emerged as a prominent area of interest in current research. However, one of the primary challenges is how to effectively fuse multimodal features. Mo…
Heart SegmentationImage SegmentationMedical Image SegmentationSegmentation+1FPL+: Filtered Pseudo Label-based Unsupervised Cross-Modality Adaptation for 3D Medical Image Segmentation
Adapting a medical image segmentation model to a new domain is important for improving its cross-domain transferability, and due to the expensive annotation process, Unsupervised Domain Adaptation (UDA) is appealing wher…
Data AugmentationDomain AdaptationHeart SegmentationImage Segmentation+4Leveraging point annotations in segmentation learning with boundary loss
This paper investigates the combination of intensity-based distance maps with boundary loss for point-supervised semantic segmentation. By design the boundary loss imposes a stronger penalty on the false positives the fa…
Computational EfficiencyHeart SegmentationOrgan SegmentationSegmentation+1A Simple and Robust Framework for Cross-Modality Medical Image Segmentation applied to Vision Transformers
When it comes to clinical images, automatic segmentation has a wide variety of applications and a considerable diversity of input domains, such as different types of Magnetic Resonance Images (MRIs) and Computerized Tomo…
Heart SegmentationImage GenerationImage SegmentationMedical Image Segmentation+3nnSAM: Plug-and-play Segment Anything Model Improves nnUNet Performance
Automatic segmentation of medical images is crucial in modern clinical workflows. The Segment Anything Model (SAM) has emerged as a versatile tool for image segmentation without specific domain training, but it requires …
Few-Shot LearningHeart SegmentationImage SegmentationMedical Image Segmentation+2Prior Knowledge-Guided Attention in Self-Supervised Vision Transformers
Recent trends in self-supervised representation learning have focused on removing inductive biases from training pipelines. However, inductive biases can be useful in settings when limited data are available or provide a…
Heart SegmentationMedical Image AnalysisRepresentation LearningUnsupervised Domain Adaptation with Variational Approximation for Cardiac Segmentation
Unsupervised domain adaptation is useful in medical image segmentation. Particularly, when ground truths of the target images are not available, domain adaptation can train a target-specific model by utilizing the existi…
Cardiac SegmentationDomain AdaptationHeart SegmentationImage Segmentation+4Optimal Latent Vector Alignment for Unsupervised Domain Adaptation in Medical Image Segmentation
This paper addresses the domain shift problem for segmentation. As a solution, we propose OLVA, a novel and lightweight unsupervised domain adaptation method based on a Variational Auto-Encoder (VAE) and Optimal Transpor…
Domain AdaptationHeart SegmentationImage SegmentationMedical Image Segmentation+4Cardiac Segmentation on CT Images through Shape-Aware Contour Attentions
Cardiac segmentation of atriums, ventricles, and myocardium in computed tomography (CT) images is an important first-line task for presymptomatic cardiovascular disease diagnosis. In several recent studies, deep learning…
Cardiac SegmentationComputed Tomography (CT)Distance regressionHeart Segmentation+4Robust Classification from Noisy Labels: Integrating Additional Knowledge for Chest Radiography Abnormality Assessment
Chest radiography is the most common radiographic examination performed in daily clinical practice for the detection of various heart and lung abnormalities. The large amount of data to be read and reported, with more th…
General ClassificationHeart SegmentationRobust classificationEfficient Model Monitoring for Quality Control in Cardiac Image Segmentation
Deep learning methods have reached state-of-the-art performance in cardiac image segmentation. Currently, the main bottleneck towards their effective translation into clinics requires assuring continuous high model perfo…
Anomaly DetectionCardiac SegmentationHeart SegmentationImage Segmentation+3Multi-class probabilistic atlas-based whole heart segmentation method in cardiac CT and MRI
Accurate and robust whole heart substructure segmentation is crucial in developing clinical applications, such as computer-aided diagnosis and computer-aided surgery. However, segmentation of different heart substructure…
DecoderDiagnosticHeart SegmentationSegmentationChest X-ray lung and heart segmentation based on minimal training sets
As the COVID-19 pandemic aggravated the excessive workload of doctors globally, the demand for computer aided methods in medical imaging analysis increased even further. Such tools can result in more robust diagnostic pi…
DiagnosticHeart SegmentationDeep Learning from Dual-Energy Information for Whole-Heart Segmentation in Dual-Energy and Single-Energy Non-Contrast-Enhanced Cardiac CT
Deep learning-based whole-heart segmentation in coronary CT angiography (CCTA) allows the extraction of quantitative imaging measures for cardiovascular risk prediction. Automatic extraction of these measures in patients…
Heart SegmentationSegmentationMvMM-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 SegmentationSegmentation