Staging of liver fibrosis is important in the diagnosis and treatment
pl...
Due to the cross-domain distribution shift aroused from diverse medical
...
Myocardial pathology segmentation (MyoPS) is critical for the risk
strat...
Cardiac segmentation is in great demand for clinical practice. Due to th...
Medical images are generally acquired with limited field-of-view (FOV), ...
Curating a large scale fully-annotated dataset can be both labour-intens...
Myocardial pathology segmentation (MyoPS) can be a prerequisite for the
...
This paper presents a generic probabilistic framework for estimating the...
Multi-modality cardiac imaging plays a key role in the management of pat...
Partially-supervised learning can be challenging for segmentation due to...
Distributed learning has shown great potential in medical image analysis...
Although supervised deep-learning has achieved promising performance in
...
Previous methods on multimodal groupwise registration typically require
...
Cardiac segmentation is an essential step for the diagnosis of cardiovas...
Modeling statistics of image priors is useful for image super-resolution...
Curating a large set of fully annotated training data can be costly,
esp...
Multi-sequence cardiac magnetic resonance (CMR) provides essential patho...
Assessment of myocardial viability is essential in diagnosis and treatme...
Accurate cardiac computing, analysis and modeling from multi-modality im...
Right ventricular (RV) segmentation from magnetic resonance imaging (MRI...
Late gadolinium enhancement magnetic resonance imaging (LGE MRI) is comm...
Unsupervised domain adaptation is useful in medical image segmentation.
...
Left atrial (LA) segmentation from late gadolinium enhanced magnetic
res...
Registration networks have shown great application potentials in medical...
Multi-modality medical images can provide relevant and complementary
ana...
Super-resolution (SR) is an ill-posed problem, which means that infinite...
Developing efficient vessel-tracking algorithms is crucial for imaging-b...
The principal rank-one (RO) components of an image represent the
self-si...
Pathological area segmentation in cardiac magnetic resonance (MR) images...
In this paper, we study the problem of imaging orientation in cardiac MR...
Delineating the brain tumor from magnetic resonance (MR) images is criti...
Deep learning (DL)-based models have demonstrated good performance in me...
Both image registration and label fusion in the multi-atlas segmentation...
Left atrial (LA) and atrial scar segmentation from late gadolinium enhan...
Current deep-learning-based registration algorithms often exploit
intens...
We propose an end-to-end deep neural network (DNN) which can simultaneou...
Image registration is one of the most underlined processes in medical im...
Cardiac segmentation from late gadolinium enhancement MRI is an importan...
Single image super-resolution (SR) is extremely difficult if the upscali...
Background: Parkinson's disease (PD) is a prevalent long-term
neurodegen...
Alzheimer's Disease (AD) is one of the most concerned neurodegenerative
...
Knowledge of whole heart anatomy is a prerequisite for many clinical
app...
Late gadolinium enhancement magnetic resonance imaging (LGE MRI) appears...
Deep convolutional networks have demonstrated the state-of-the-art
perfo...
Late Gadolinium Enhancement Magnetic Resonance Imaging (LGE MRI) emerged...
We present a fully-automated segmentation and quantification of the left...
Purpose: Atrial fibrillation (AF) is the most common cardiac arrhythmia ...
This paper proposes a method for simultaneous segmentation of multi-sour...