58 citations · 101 across the 22 of their papers we have counts for
6 papers · 1 filter
Deep Information Theoretic Registration
Alireza Sedghi, Jie Luo, Alireza Mehrtash +5
This paper establishes an information theoretic framework for deep metric based image registration techniques. We show an exact equivalence between maximum profile likelihood and m…
Deep learning based automatic segmentation of lumbosacral nerves on non-contrast CT for radiographic evaluation: a pilot study
Guoxin Fan, Huaqing Liu, Zhenhua Wu +7
Background and objective: Combined evaluation of lumbosacral structures (e.g. nerves, bone) on multimodal radiographic images is routinely conducted prior to spinal surgery and int…
Semi-Supervised Deep Metrics for Image Registration
Alireza Sedghi, Jie Luo, Alireza Mehrtash +5
Deep metrics have been shown effective as similarity measures in multi-modal image registration; however, the metrics are currently constructed from aligned image pairs in the trai…
A Feature-Driven Active Framework for Ultrasound-Based Brain Shift Compensation
Jie Luo, Matt Toews, Ines Machado +10
A reliable Ultrasound (US)-to-US registration method to compensate for brain shift would substantially improve Image-Guided Neurological Surgery. Developing such a registration met…
On the Applicability of Registration Uncertainty
Jie Luo, Alireza Sedghi, Karteek Popuri +8
Estimating the uncertainty in (probabilistic) image registration enables, e.g., surgeons to assess the operative risk based on the trustworthiness of the registered image data. If…
mvn2vec: Preservation and Collaboration in Multi-View Network Embedding
Yu Shi, Fangqiu Han, Xinwei He +4
Multi-view networks are broadly present in real-world applications. In the meantime, network embedding has emerged as an effective representation learning approach for networked da…