1 citations · 5 across the 19 of their papers we have counts for
20 papers
CrossFeat: Bridging Imaging Modalities in Feature Descriptor Space
Paul Schneider, Nazim Haouchine
Most advances in keypoint descriptions address monomodal settings, where image variations arise from viewpoint, illumination, or contrast changes. Multimodal scenarios involve imag…
Towards Patient-Specific Deformable Registration in Laparoscopic Surgery
Alberto Neri, Veronica Penza, Nazim Haouchine +1
Unsafe surgical care is a critical health concern, often linked to limitations in surgeon experience, skills, and situational awareness. Integrating patient-specific 3D models into…
NOIR: Neural Operator mapping for Implicit Representations
Sidaty El Hadramy, Nazim Haouchine, Michael Wehrli +1
This paper presents NOIR, a framework that reframes core medical imaging tasks as operator learning between continuous function spaces, challenging the prevailing paradigm of discr…
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…
BridgeSplat: Bidirectionally Coupled CT and Non-Rigid Gaussian Splatting for Deformable Intraoperative Surgical Navigation
Maximilian Fehrentz, Alexander Winkler, Thomas Heiliger +3
We introduce BridgeSplat, a novel approach for deformable surgical navigation that couples intraoperative 3D reconstruction with preoperative CT data to bridge the gap between surg…
Learn2Reg 2024: New Benchmark Datasets Driving Progress on New Challenges
Lasse Hansen, Wiebke Heyer, Christoph Großbröhmer +51
Medical image registration is critical for clinical applications, and fair benchmarking of different methods is essential for monitoring ongoing progress in the field. To date, the…
When Experts Disagree: Characterizing Annotator Variability for Vessel Segmentation in DSA Images
M. Geshvadi, G. So, D. D. Chlorogiannis +12
We analyze the variability among segmentations of cranial blood vessels in 2D DSA performed by multiple annotators in order to characterize and quantify segmentation uncertainty. W…
A 3D Cross-modal Keypoint Descriptor for MR-US Matching and Registration
Daniil Morozov, Reuben Dorent, Nazim Haouchine
Intraoperative registration of real-time ultrasound (iUS) to preoperative Magnetic Resonance Imaging (MRI) remains an unsolved problem due to severe modality-specific differences i…
Surgical Neural Radiance Fields from One Image
Alberto Neri, Maximilan Fehrentz, Veronica Penza +2
Purpose: Neural Radiance Fields (NeRF) offer exceptional capabilities for 3D reconstruction and view synthesis, yet their reliance on extensive multi-view data limits their applica…
Backward Stochastic Differential Equations-guided Generative Model for Structural-to-functional Neuroimage Translator
Zengjing Chen, Lu Wang, Yongkang Lin +7
A Method for structural-to-functional neuroimage translator
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…
Registration of Longitudinal Spine CTs for Monitoring Lesion Growth
Malika Sanhinova, Nazim Haouchine, Steve D. Pieper +11
Accurate and reliable registration of longitudinal spine images is essential for assessment of disease progression and surgical outcome. Implementing a fully automatic and robust r…
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…
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…
Calipso: Physics-based Image and Video Editing through CAD Model Proxies
Nazim Haouchine, Frederick Roy, Hadrien Courtecuisse +2
We present Calipso, an interactive method for editing images and videos in a physically-coherent manner. Our main idea is to realize physics-based manipulations by running a full p…