Transformer, one of the latest technological advances of deep learning, has gained
prevalence in natural language processing or computer vision. Since medical imaging bear …

Abstract Background and Objective: Vessel segmentation is the first processing stage of 3D
medical images for both clinical and research use. Current segmentation methods are …

Learning inter-image similarity is crucial for 3D medical images self-supervised pre-training,
due to their sharing of numerous same semantic regions. However, the lack of the semantic …

Prior to the deep learning era, shape was commonly used to describe the objects.
Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly …

Abstract Computed Tomography Coronary Angiography (CTCA) is a non-invasive method to
evaluate coronary artery anatomy and disease. CTCA is ideal for geometry reconstruction to …

In this work, we address the task of few-shot medical image segmentation (MIS) with a novel
proposed framework based on the learning registration to learn segmentation (LRLS) …

Overcoming barriers on the use of multi-center data for medical analytics is challenging due
to privacy protection and data heterogeneity in the healthcare system. In this study, we …

Coronary artery segmentation is critical for coronary artery disease diagnosis but
challenging due to its tortuous course with numerous small branches and inter-subject …

Accurate extraction of coronary arteries from coronary computed tomography angiography
(CCTA) is a prerequisite for the computer-aided diagnosis of coronary artery disease (CAD) …

Automatic segmentation of the intracranial artery (IA) in digital subtraction angiography
(DSA) sequence is an essential step in diagnosing IA-related diseases and guiding neuro …