Search Results for author:
Found 16 papers, 6 papers with code
Rapid patient-specific neural networks for intraoperative X-ray to volume registration
The integration of artificial intelligence in image-guided interventions holds transformative potential, promising to extract 3D geometric and quantitative information from conventional 2D imaging modalities during complex procedures.
3D/2D Registration of Angiograms using Silhouette-based Differentiable Rendering
We present a method for 3D/2D registration of Digital Subtraction Angiography (DSA) images to provide valuable insight into brain hemodynamics and angioarchitecture.
Differentiable Voxel-based X-ray Rendering Improves Sparse-View 3D CBCT Reconstruction
We present DiffVox, a self-supervised framework for Cone-Beam Computed Tomography (CBCT) reconstruction by directly optimizing a voxelgrid representation using physics-based differentiable X-ray rendering.
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.
Learning Expected Appearances for Intraoperative Registration during Neurosurgery
In contrast to conventional methods, our approach transfers the processing tasks to the preoperative stage, reducing thereby the impact of low-resolution, distorted, and noisy intraoperative images, that often degrade the registration accuracy.
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.
Deep Learning for Detection and Localization of B-Lines in Lung Ultrasound
B-line artifacts in LUS videos are key findings associated with pulmonary congestion.
Double-Uncertainty Guided Spatial and Temporal Consistency Regularization Weighting for Learning-based Abdominal Registration
Such convention has two limitations: (i) Besides the laborious grid search for the optimal fixed weight, the regularization strength of a specific image pair should be associated with the content of the images, thus the "one value fits all" training scheme is not ideal; (ii) Only spatially regularizing the transformation may neglect some informative clues related to the ill-posedness.
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.
