Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives
Transformer, one of the latest technological advances of deep learning, has gained
prevalence in natural language processing or computer vision. Since medical imaging bear …
prevalence in natural language processing or computer vision. Since medical imaging bear …
[HTML][HTML] Medical image registration in the era of Transformers: a recent review
Motivated by the rapid and current progress to develop intelligent image-guided intervention
tools, we aim in this paper to present, a recent review of a specific family of deep learning …
tools, we aim in this paper to present, a recent review of a specific family of deep learning …
Geometric visual similarity learning in 3d medical image self-supervised pre-training
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 …
due to their sharing of numerous same semantic regions. However, the lack of the semantic …
MedShapeNet – a large-scale dataset of 3D medical shapes for computer vision
Objectives The shape is commonly used to describe the objects. State-of-the-art algorithms
in medical imaging are predominantly diverging from computer vision, where voxel grids …
in medical imaging are predominantly diverging from computer vision, where voxel grids …
[HTML][HTML] Towards automated coronary artery segmentation: A systematic review
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 …
medical images for both clinical and research use. Current segmentation methods are …
[HTML][HTML] An automated and time-efficient framework for simulation of coronary blood flow under steady and pulsatile conditions
Background and objective Invasive fractional flow reserve (FFR) measurement is the gold
standard method for coronary artery disease (CAD) diagnosis. FFR-CT exploits …
standard method for coronary artery disease (CAD) diagnosis. FFR-CT exploits …
Annotated computed tomography coronary angiogram images and associated data of normal and diseased arteries
R Gharleghi, D Adikari, K Ellenberger, M Webster… - Scientific Data, 2023 - nature.com
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 …
evaluate coronary artery anatomy and disease. CTCA is ideal for geometry reconstruction to …
Learning better registration to learn better few-shot medical image segmentation: Authenticity, diversity, and robustness
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) …
proposed framework based on the learning registration to learn segmentation (LRLS) …
Mining multi-center heterogeneous medical data with distributed synthetic learning
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 …
to privacy protection and data heterogeneity in the healthcare system. In this study, we …
An Anatomy-and Topology-Preserving Framework for Coronary Artery Segmentation
Coronary artery segmentation is critical for coronary artery disease diagnosis but
challenging due to its tortuous course with numerous small branches and inter-subject …
challenging due to its tortuous course with numerous small branches and inter-subject …