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Found 11 papers, 2 papers with code
Unified Cross-Modal Image Synthesis with Hierarchical Mixture of Product-of-Experts
We propose a deep mixture of multimodal hierarchical variational auto-encoders called MMHVAE that synthesizes missing images from observed images in different modalities.
Intraoperative Registration by Cross-Modal Inverse Neural Rendering
We present in this paper a novel approach for 3D/2D intraoperative registration during neurosurgery via cross-modal inverse neural rendering.
Learning to Match 2D Keypoints Across Preoperative MR and Intraoperative Ultrasound
We propose in this paper a texture-invariant 2D keypoints descriptor specifically designed for matching preoperative Magnetic Resonance (MR) images with intraoperative Ultrasound (US) images.
Patient-Specific Real-Time Segmentation in Trackerless Brain Ultrasound
To disambiguate ultrasound imaging and adapt to the neurosurgeon's surgical objective, a patient-specific real-time network is trained using synthetic ultrasound data generated by simulating virtual iUS sweep acquisitions in pre-operative MR data.
Spatiotemporal Disentanglement of Arteriovenous Malformations in Digital Subtraction Angiography
Although Digital Subtraction Angiography (DSA) is the most important imaging for visualizing cerebrovascular anatomy, its interpretation by clinicians remains difficult.
Unified Brain MR-Ultrasound Synthesis using Multi-Modal Hierarchical Representations
We introduce MHVAE, a deep hierarchical variational auto-encoder (VAE) that synthesizes missing images from various modalities.
Do Public Datasets Assure Unbiased Comparisons for Registration Evaluation?
In this study, we use the variogram to screen the manually annotated landmarks in two datasets used to benchmark registration in image-guided neurosurgeries.
Are Registration Uncertainty and Error Monotonically Associated
Probabilistic image registration (PIR) methods provide measures of registration uncertainty, which could be a surrogate for assessing the registration error.
A Feature-Driven Active Framework for Ultrasound-Based Brain Shift Compensation
Kernels of the GP are estimated by using variograms and a discrete grid search method.
On the Applicability of Registration Uncertainty
For probabilistic image registration (PIR), the predominant way to quantify the registration uncertainty is using summary statistics of the distribution of transformation parameters.
Functional Geometry Alignment and Localization of Brain Areas
This advantage is pronounced for subjects with tumors that affect the language areas and thus cause spatial reorganization of the functional regions.
