most citedUnified Brain MR-Ultrasound Synthesis using Multi-Modal Hierarchical Representations

17 citations · 18 across the 5 of their papers we have counts for

collaborators

5 papers

eess.IV2024

Spatiotemporal Disentanglement of Arteriovenous Malformations in Digital Subtraction Angiography

Kathleen Baur, Xin Xiong, Erickson Torio +6

Although Digital Subtraction Angiography (DSA) is the most important imaging for visualizing cerebrovascular anatomy, its interpretation by clinicians remains difficult. This is pa…

cs.GR2024

A High-Performance SurfaceNets Discrete Isocontouring Algorithm

Will Schroeder, Spiros Tsalikis, Michael Halle +1

Isocontouring is one of the most widely used visualization techniques. However, many popular contouring algorithms were created prior to the advent of ubiquitous parallel approache…

cs.CV2023

Learning Expected Appearances for Intraoperative Registration during Neurosurgery

Nazim Haouchine, Reuben Dorent, Parikshit Juvekar +5

We present a novel method for intraoperative patient-to-image registration by learning Expected Appearances. Our method uses preoperative imaging to synthesize patient-specific exp…

cs.CV202317 cited

Unified Brain MR-Ultrasound Synthesis using Multi-Modal Hierarchical Representations

Reuben Dorent, Nazim Haouchine, Fryderyk Kögl +9

We introduce MHVAE, a deep hierarchical variational auto-encoder (VAE) that synthesizes missing images from various modalities. Extending multi-modal VAEs with a hierarchical laten…

eess.IV20231 cited

Deep Learning for Detection and Localization of B-Lines in Lung Ultrasound

Ruben T. Lucassen, Mohammad H. Jafari, Nicole M. Duggan +18

Lung ultrasound (LUS) is an important imaging modality used by emergency physicians to assess pulmonary congestion at the patient bedside. B-line artifacts in LUS videos are key fi…