Papers Cardiac Segmentation
“Cardiac Segmentation” 태그가 달린 논문 88편 · 필터 해제
Innovative Integration of 4D Cardiovascular Reconstruction and Hologram: A New Visualization Tool for Coronary Artery Bypass Grafting Planning
Background: Coronary artery bypass grafting (CABG) planning requires advanced spatial visualization and consideration of coronary artery depth, calcification, and pericardial adhesions. Objective: To develop and evaluate…
Cardiac SegmentationDAM-Seg: Anatomically accurate cardiac segmentation using Dense Associative Networks
Deep learning-based cardiac segmentation has seen significant advancements over the years. Many studies have tackled the challenge of anatomically incorrect segmentation predictions by introducing auxiliary modules. Thes…
Cardiac SegmentationSegmentationGLoG-CSUnet: Enhancing Vision Transformers with Adaptable Radiomic Features for Medical Image Segmentation
Vision Transformers (ViTs) have shown promise in medical image semantic segmentation (MISS) by capturing long-range correlations. However, ViTs often struggle to model local spatial information effectively, which is esse…
Cardiac SegmentationImage SegmentationMedical Image AnalysisMedical Image Segmentation+2Rate-My-LoRA: Efficient and Adaptive Federated Model Tuning for Cardiac MRI Segmentation
Cardiovascular disease (CVD) and cardiac dyssynchrony are major public health problems in the United States. Precise cardiac image segmentation is crucial for extracting quantitative measures that help categorize cardiac…
Cardiac SegmentationFederated LearningImage SegmentationMRI segmentation+1SFB-net for cardiac segmentation: Bridging the semantic gap with attention
In the past few years, deep learning algorithms have been widely used for cardiac image segmentation. However, most of these architectures rely on convolutions that hardly model long-range dependencies, limiting their ab…
Cardiac SegmentationDecoderImage SegmentationSemantic SegmentationRotCAtt-TransUNet++: Novel Deep Neural Network for Sophisticated Cardiac Segmentation
Cardiovascular disease remains a predominant global health concern, responsible for a significant portion of mortality worldwide. Accurate segmentation of cardiac medical imaging data is pivotal in mitigating fatality ra…
Cardiac SegmentationSegmentationHow good nnU-Net for Segmenting Cardiac MRI: A Comprehensive Evaluation
Cardiac segmentation is a critical task in medical imaging, essential for detailed analysis of heart structures, which is crucial for diagnosing and treating various cardiovascular diseases. With the advent of deep learn…
Cardiac SegmentationImage SegmentationMedical Image SegmentationSegmentation+1ModelMix: A New Model-Mixup Strategy to Minimize Vicinal Risk across Tasks for Few-scribble based Cardiac Segmentation
Pixel-level dense labeling is both resource-intensive and time-consuming, whereas weak labels such as scribble present a more feasible alternative to full annotations. However, training segmentation networks with weak su…
Cardiac SegmentationSegmentationCAMS: Convolution and Attention-Free Mamba-based Cardiac Image Segmentation
Convolutional Neural Networks (CNNs) and Transformer-based self-attention models have become the standard for medical image segmentation. This paper demonstrates that convolution and self-attention, while widely used, ar…
Cardiac SegmentationDecoderImage SegmentationMamba+3Direct Cardiac Segmentation from Undersampled K-space Using Transformers
The prevailing deep learning-based methods of predicting cardiac segmentation involve reconstructed magnetic resonance (MR) images. The heavy dependency of segmentation approaches on image quality significantly limits th…
Cardiac SegmentationSegmentationLUCF-Net: Lightweight U-shaped Cascade Fusion Network for Medical Image Segmentation
In this study, the performance of existing U-shaped neural network architectures was enhanced for medical image segmentation by adding Transformer. Although Transformer architectures are powerful at extracting global inf…
Cardiac SegmentationImage SegmentationMedical Image SegmentationOrgan Segmentation+2Deep Separable Spatiotemporal Learning for Fast Dynamic Cardiac MRI
Dynamic magnetic resonance imaging (MRI) plays an indispensable role in cardiac diagnosis. To enable fast imaging, the k-space data can be undersampled but the image reconstruction poses a great challenge of high-dimensi…
Cardiac SegmentationImage ReconstructionWeak-Mamba-UNet: Visual Mamba Makes CNN and ViT Work Better for Scribble-based Medical Image Segmentation
Medical image segmentation is increasingly reliant on deep learning techniques, yet the promising performance often come with high annotation costs. This paper introduces Weak-Mamba-UNet, an innovative weakly-supervised …
Cardiac SegmentationDecoderImage SegmentationMamba+4TAI-GAN: A Temporally and Anatomically Informed Generative Adversarial Network for early-to-late frame conversion in dynamic cardiac PET inter-frame motion correction
Inter-frame motion in dynamic cardiac positron emission tomography (PET) using rubidium-82 (82-Rb) myocardial perfusion imaging impacts myocardial blood flow (MBF) quantification and the diagnosis accuracy of coronary ar…
Cardiac SegmentationGenerative Adversarial NetworkImage RegistrationMotion EstimationSemi-Mamba-UNet: Pixel-Level Contrastive and Pixel-Level Cross-Supervised Visual Mamba-based UNet for Semi-Supervised Medical Image Segmentation
Medical image segmentation is essential in diagnostics, treatment planning, and healthcare, with deep learning offering promising advancements. Notably, the convolutional neural network (CNN) excels in capturing local im…
Cardiac SegmentationContrastive LearningImage SegmentationMamba+4Mamba-UNet: UNet-Like Pure Visual Mamba for Medical Image Segmentation
In recent advancements in medical image analysis, Convolutional Neural Networks (CNN) and Vision Transformers (ViT) have set significant benchmarks. While the former excels in capturing local features through its convolu…
Cardiac SegmentationComputational EfficiencyDecoderImage Segmentation+5Harmonized Spatial and Spectral Learning for Robust and Generalized Medical Image Segmentation
Deep learning has demonstrated remarkable achievements in medical image segmentation. However, prevailing deep learning models struggle with poor generalization due to (i) intra-class variations, where the same class app…
Cardiac SegmentationImage SegmentationMedical Image SegmentationSegmentation+1CaRe-CNN: Cascading Refinement CNN for Myocardial Infarct Segmentation with Microvascular Obstructions
Late gadolinium enhanced (LGE) magnetic resonance (MR) imaging is widely established to assess the viability of myocardial tissue of patients after acute myocardial infarction (MI). We propose the Cascading Refinement CN…
Cardiac SegmentationSDF4CHD: Generative Modeling of Cardiac Anatomies with Congenital Heart Defects
Congenital heart disease (CHD) encompasses a spectrum of cardiovascular structural abnormalities, often requiring customized treatment plans for individual patients. Computational modeling and analysis of these unique ca…
Cardiac SegmentationImage SegmentationMORPHSegmentation+1Vicinal Feature Statistics Augmentation for Federated 3D Medical Volume Segmentation
Federated learning (FL) enables multiple client medical institutes collaboratively train a deep learning (DL) model with privacy protection. However, the performance of FL can be constrained by the limited availability o…
Cardiac SegmentationData AugmentationFederated LearningSegmentation