17 citations · 23 across the 15 of their papers we have counts for
15 papers
Data-driven registration and modeling of brain deformation for image-guided neurosurgery
Tiago Assis, Colin P. Galvin, Joshua P. Castillo +12
Accurate compensation of brain deformation is critical for reliable image-guided neurosurgery. Surgical manipulation and tumor resection induce tissue motion, causing preoperative…
3D/2D Registration of Angiograms using Silhouette-based Differentiable Rendering
Taewoong Lee, Sarah Frisken, Nazim Haouchine
We present a method for 3D/2D registration of Digital Subtraction Angiography (DSA) images to provide valuable insight into brain hemodynamics and angioarchitecture. Our approach f…
Unified Cross-Modal Medical Image Synthesis with Hierarchical Mixture of Product-of-Experts
Reuben Dorent, Nazim Haouchine, Alexandra Golby +3
We propose a deep mixture of multimodal hierarchical variational auto-encoders called MMHVAE that synthesizes missing images from observed images in different modalities. MMHVAE's…
Intraoperative Registration by Cross-Modal Inverse Neural Rendering
Maximilian Fehrentz, Mohammad Farid Azampour, Reuben Dorent +7
We present in this paper a novel approach for 3D/2D intraoperative registration during neurosurgery via cross-modal inverse neural rendering. Our approach separates implicit neural…
Learning to Match 2D Keypoints Across Preoperative MR and Intraoperative Ultrasound
Hassan Rasheed, Reuben Dorent, Maximilian Fehrentz +6
We propose in this paper a texture-invariant 2D keypoints descriptor specifically designed for matching preoperative Magnetic Resonance (MR) images with intraoperative Ultrasound (…
Patient-Specific Real-Time Segmentation in Trackerless Brain Ultrasound
Reuben Dorent, Erickson Torio, Nazim Haouchine +5
Intraoperative ultrasound (iUS) imaging has the potential to improve surgical outcomes in brain surgery. However, its interpretation is challenging, even for expert neurosurgeons.…
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…
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…
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…
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…
Double-Uncertainty Guided Spatial and Temporal Consistency Regularization Weighting for Learning-based Abdominal Registration
Zhe Xu, Jie Luo, Donghuan Lu +7
In order to tackle the difficulty associated with the ill-posed nature of the image registration problem, regularization is often used to constrain the solution space. For most lea…
Do Public Datasets Assure Unbiased Comparisons for Registration Evaluation?
Jie Luo, Guangshen Ma, Sarah Frisken +8
With the increasing availability of new image registration approaches, an unbiased evaluation is becoming more needed so that clinicians can choose the most suitable approaches for…
Are Registration Uncertainty and Error Monotonically Associated
Jie Luo, Sarah Frisken, Duo Wang +3
In image-guided neurosurgery, current commercial systems usually provide only rigid registration, partly because it is harder to predict, validate and understand non-rigid registra…
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…