Search Results for author:
Found 5 papers, 2 papers with code
Image To Tree with Recursive Prompting
Extracting complex structures from grid-based data is a common key step in automated medical image analysis.
Morphology-based non-rigid registration of coronary computed tomography and intravascular images through virtual catheter path optimization
We formulate the problem in terms of finding the optimal \emph{virtual catheter path} that samples the CCTA data to recapitulate the coronary artery morphology found in the intravascular image.
Atlas-ISTN: Joint Segmentation, Registration and Atlas Construction with Image-and-Spatial Transformer Networks
We propose Atlas-ISTN, a framework that jointly learns segmentation and registration on 2D and 3D image data, and constructs a population-derived atlas in the process.
Image-and-Spatial Transformer Networks for Structure-Guided Image Registration
The goal is to learn a complex function that maps the appearance of input image pairs to parameters of a spatial transformation in order to align corresponding anatomical structures.
Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images
AGs can be easily integrated into standard CNN models such as VGG or U-Net architectures with minimal computational overhead while increasing the model sensitivity and prediction accuracy.
