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 …
A novel deep network with triangular-star spatial–spectral fusion encoding and entropy-aware double decoding for coronary artery segmentation
Coronary artery segmentation is a crucial prerequisite for computer-aided diagnosis of
coronary artery disease (CAD). However, this task remains challenging due to the intricate …
coronary artery disease (CAD). However, this task remains challenging due to the intricate …
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 …
SIRE: scale-invariant, rotation-equivariant estimation of artery orientations using graph neural networks
Blood vessel orientation as visualized in 3D medical images is an important descriptor of its
geometry that can be used for centerline extraction and subsequent segmentation and …
geometry that can be used for centerline extraction and subsequent segmentation and …
A new understanding of coronary curvature and haemodynamic impact on the course of plaque onset and progression
M Zhang, R Gharleghi, C Shen… - Royal Society Open …, 2024 - royalsocietypublishing.org
The strong link between atherosclerosis and luminal biomechanical stresses is well
established. Yet, this understanding has not translated into preventative coronary diagnostic …
established. Yet, this understanding has not translated into preventative coronary diagnostic …
World of Forms: Deformable Geometric Templates for One-Shot Surface Meshing in Coronary CT Angiography
Deep learning-based medical image segmentation and surface mesh generation typically
involve a sequential pipeline from image to segmentation to meshes, often requiring large …
involve a sequential pipeline from image to segmentation to meshes, often requiring large …
Integrated deep learning approach for automatic coronary artery segmentation and classification on computed tomographic coronary angiography
CD Muthusamy, R Murugesh - Network Modeling Analysis in Health …, 2024 - Springer
The field of coronary artery disease (CAD) has seen a rapid development in coronary
computed tomography angiography (CCTA). However, manual coronary artery tree …
computed tomography angiography (CCTA). However, manual coronary artery tree …
XA-Sim2Real: Adaptive Representation Learning for Vessel Segmentation in X-Ray Angiography
B Zhang, Z Zhang, S Liu, S Faghihroohi… - … Conference on Medical …, 2024 - Springer
Accurate vessel segmentation from X-ray Angiography (XA) is essential for various medical
applications, including diagnosis, treatment planning, and image-guided interventions …
applications, including diagnosis, treatment planning, and image-guided interventions …
Assessing Encoder-Decoder Architectures for Robust Coronary Artery Segmentation
Coronary artery diseases are among the leading causes of mortality worldwide. Timely and
accurate diagnosis, facilitated by precise coronary artery segmentation, is pivotal in …
accurate diagnosis, facilitated by precise coronary artery segmentation, is pivotal in …
Exploiting Scale Invariance and Rotation Equivariance for Sparse and Dense Artery Orientation Estimation
We present SIRE, a modular estimator of local artery orientations that is Scale Invariant and
Rotation Equivariant. These symmetry preservations are obtained by operating on …
Rotation Equivariant. These symmetry preservations are obtained by operating on …