U-net and its variants for medical image segmentation: A review of theory and applications
U-net is an image segmentation technique developed primarily for image segmentation
tasks. These traits provide U-net with a high utility within the medical imaging community …
tasks. These traits provide U-net with a high utility within the medical imaging community …
[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 …
Cross-modality LGE-CMR segmentation using image-to-image translation based data augmentation
Accurate segmentation of ventricle and myocardium from the late gadolinium enhancement
(LGE) cardiac magnetic resonance (CMR) is an important tool for myocardial infarction (MI) …
(LGE) cardiac magnetic resonance (CMR) is an important tool for myocardial infarction (MI) …
Cardiac segmentation on late gadolinium enhancement MRI: a benchmark study from multi-sequence cardiac MR segmentation challenge
Accurate computing, analysis and modeling of the ventricles and myocardium from medical
images are important, especially in the diagnosis and treatment management for patients …
images are important, especially in the diagnosis and treatment management for patients …
Disentangle, align and fuse for multimodal and semi-supervised image segmentation
Magnetic resonance (MR) protocols rely on several sequences to assess pathology and
organ status properly. Despite advances in image analysis, we tend to treat each sequence …
organ status properly. Despite advances in image analysis, we tend to treat each sequence …
Adapt everywhere: unsupervised adaptation of point-clouds and entropy minimization for multi-modal cardiac image segmentation
Deep learning models are sensitive to domain shift phenomena. A model trained on images
from one domain cannot generalise well when tested on images from a different domain …
from one domain cannot generalise well when tested on images from a different domain …
Artificial Intelligence framework with traditional computer vision and deep learning approaches for optimal automatic segmentation of left ventricle with scar
Automatic segmentation of the cardiac left ventricle with scars remains a challenging and
clinically significant task, as it is essential for patient diagnosis and treatment pathways. This …
clinically significant task, as it is essential for patient diagnosis and treatment pathways. This …
Sk-unet: An improved u-net model with selective kernel for the segmentation of lge cardiac mr images
In the clinical environment, myocardial infarction (MI) as one common cardiovascular
disease is mainly evaluated using late gadolinium enhancement (LGE) cardiac magnetic …
disease is mainly evaluated using late gadolinium enhancement (LGE) cardiac magnetic …
[HTML][HTML] GOMPS: global attention-based ophthalmic image measurement and postoperative appearance prediction system
Accurate measurements of ophthalmic parameters and postoperative appearance prediction
are essential for the diagnosis and treatment of many ophthalmic diseases. Nevertheless, it …
are essential for the diagnosis and treatment of many ophthalmic diseases. Nevertheless, it …
Attention gate based dual-pathway network for vertebra segmentation of X-ray spine images
W Shi, T Xu, H Yang, Y Xi, Y Du, J Li… - IEEE Journal of …, 2022 - ieeexplore.ieee.org
Automatic spine and vertebra segmentation from X-ray spine images is a critical and
challenging problem in many computer-aid spinal image analysis and disease diagnosis …
challenging problem in many computer-aid spinal image analysis and disease diagnosis …