Learning with limited annotations: a survey on deep semi-supervised learning for medical image segmentation
Medical image segmentation is a fundamental and critical step in many image-guided
clinical approaches. Recent success of deep learning-based segmentation methods usually …
clinical approaches. Recent success of deep learning-based segmentation methods usually …
[HTML][HTML] Multi-modality cardiac image computing: A survey
Multi-modality cardiac imaging plays a key role in the management of patients with
cardiovascular diseases. It allows a combination of complementary anatomical …
cardiovascular diseases. It allows a combination of complementary anatomical …
Sa-med2d-20m dataset: Segment anything in 2d medical imaging with 20 million masks
Segment Anything Model (SAM) has achieved impressive results for natural image
segmentation with input prompts such as points and bounding boxes. Its success largely …
segmentation with input prompts such as points and bounding boxes. Its success largely …
[HTML][HTML] Unsupervised unpaired multiple fusion adaptation aided with self-attention generative adversarial network for scar tissues segmentation framework
Late gadolinium enhancement (LGE) is a specialized imaging technique used in
cardiovascular magnetic resonance (CMR) imaging to detect and characterize areas of scar …
cardiovascular magnetic resonance (CMR) imaging to detect and characterize areas of scar …
Towards enabling cardiac digital twins of myocardial infarction using deep computational models for inverse inference
Cardiac digital twins (CDTs) have the potential to offer individualized evaluation of cardiac
function in a non-invasive manner, making them a promising approach for personalized …
function in a non-invasive manner, making them a promising approach for personalized …
Segmentation of cardiac infarction in delayed-enhancement MRI using probability map and transformers-based neural networks
E Lecesne, A Simon, M Garreau… - Computer Methods and …, 2023 - Elsevier
Abstract Background and Objective: Automatic segmentation of myocardial infarction is of
great clinical interest for the quantitative evaluation of myocardial infarction (MI). Late …
great clinical interest for the quantitative evaluation of myocardial infarction (MI). Late …
Artificial Intelligence in Image-based Cardiovascular Disease Analysis: A Comprehensive Survey and Future Outlook
Recent advancements in Artificial Intelligence (AI) have significantly influenced the field of
Cardiovascular Disease (CVD) analysis, particularly in image-based diagnostics. Our paper …
Cardiovascular Disease (CVD) analysis, particularly in image-based diagnostics. Our paper …
Towards Accurate Cardiac MRI Segmentation with Variational Autoencoder-Based Unsupervised Domain Adaptation
Accurate myocardial segmentation is crucial in the diagnosis and treatment of myocardial
infarction (MI), especially in Late Gadolinium Enhancement (LGE) cardiac magnetic …
infarction (MI), especially in Late Gadolinium Enhancement (LGE) cardiac magnetic …
ST-GAN: A Swin Transformer-Based Generative Adversarial Network for Unsupervised Domain Adaptation of Cross-Modality Cardiac Segmentation
Unsupervised domain adaptation (UDA) methods have shown great potential in cross-
modality medical image segmentation tasks, where target domain labels are unavailable …
modality medical image segmentation tasks, where target domain labels are unavailable …
Multiscale Kernel Atrous Convolution Network for Segmentation of Myocardium
Cardiovascular diseases are a global leading cause of death. The automatic segmentation
of the left ventricle (LV) from magnetic resonance (MR) images is essential for quantitative …
of the left ventricle (LV) from magnetic resonance (MR) images is essential for quantitative …