Deep Information Theoretic Registration

no code implementations • 31 Dec 2018 • Alireza Sedghi, Jie Luo, Alireza Mehrtash, Steve Pieper, Clare M. Tempany, Tina Kapur, Parvin Mousavi, William M. Wells III

This paper establishes an information theoretic framework for deep metric based image registration techniques.

Image Registration

Semi-Supervised Deep Metrics for Image Registration

no code implementations • 4 Apr 2018 • Alireza Sedghi, Jie Luo, Alireza Mehrtash, Steve Pieper, Clare M. Tempany, Tina Kapur, Parvin Mousavi, William M. Wells III

In this paper, we propose a strategy for learning such metrics from roughly aligned training data.

Data Augmentation Image Registration

A Feature-Driven Active Framework for Ultrasound-Based Brain Shift Compensation

no code implementations • 20 Mar 2018 • Jie Luo, Matt Toews, Ines Machado, Sarah Frisken, Miaomiao Zhang, Frank Preiswerk, Alireza Sedghi, Hongyi Ding, Steve Pieper, Polina Golland, Alexandra Golby, Masashi Sugiyama, William M. Wells III

Kernels of the GP are estimated by using variograms and a discrete grid search method.

On the Applicability of Registration Uncertainty

no code implementations • 14 Mar 2018 • Jie Luo, Alireza Sedghi, Karteek Popuri, Dana Cobzas, Miaomiao Zhang, Frank Preiswerk, Matthew Toews, Alexandra Golby, Masashi Sugiyama, William M. Wells III, Sarah Frisken

For probabilistic image registration (PIR), the predominant way to quantify the registration uncertainty is using summary statistics of the distribution of transformation parameters.

Image Registration