Brain tumor is one of the leading causes of cancer death. The high-grade brain tumors are
easier to recurrent even after standard treatment. Therefore, developing a method to predict …

Radiologists are tasked with interpreting a large number of images in a daily base, with the
responsibility of generating corresponding reports. This demanding workload elevates the …

Atrial fibrillation (AFib) is the prominent cardiac arrhythmia in the world. It affects mostly the
elderly population, with potential consequences such as stroke and heart failure in the …

Recently, deep learning-based brain segmentation methods have achieved great success.
However, most approaches focus on supervised segmentation, which requires many high …

Left atrial (LA) segmentation and quantification of atrial scars have opened a path to
automating Atrial Fibrillation (AF) diagnosis. This paper proposes a two-stage approach for …

Quantification of carotid atherosclerosis is important in monitoring patients at risk of
cardiovascular events and in evaluating therapies. High-resolution 3D carotid magnetic …

MRI brain structure segmentation plays an important role in neuroimaging studies. Existing
methods either spend much CPU time, require considerable annotated data, or fail in …

Purpose To develop deep learning models based on four-dimensional computed
tomography angiography (4D-CTA) images for automatic detection of large vessel occlusion …

Brain cancer is so deadly that diagnosis accuracy will be required before brain surgery.
Segmentation technology is important for medical imaging. CNN model is capable of …

Segmenting the whole brain into a large number (for example,≥ 100) of regions is
challenging due to the complexity of the brain and the lack of annotated data. Deep neural …