Image registration is a fundamental technique in medical imaging. It has a wide range of applications, such as image-guided surgery, radiation therapy, disease progression monitoring, image fusion, brain mapping, and computer-aided diagnosis. The goal of image registration is to align a moving (source) image with a fixed (target) image by estimating a geometric transformation that matches corresponding features or structures in the two images.
RAPHIA pipeline automates prostate segmentation using deep learning approaches to refine labor-intensive and time-consuming protocols we currently employ.
RetinaRegNet, a versatile model that achieves state-of-the-art performance on various retinal image registration tasks by innovation.
First deep-learning-based semi-automated approach for registering in-vivo micro-US and ex-vivo pseudo-whole mount histopathology images.
Novel in-phase/out-of-phase ventilation (IOV) function plot to visualize and measure the amount of high-function IOV That occurs during the breathing cycle.
Geodesic Density Regression for Artifact Correction
The GDR algorithm uses binary artifact masks to exclude artifact regions from the regression and accommodates image intensity change associated with breathing by using a tissue density deformation action.
First application of deep learning for MRI-histopathology image registration. Approximately 20-times faster than conventional approaches.
