Search Results for author:
Found 31 papers, 18 papers with code
Anatomically Constrained Video-CT Registration via the V-IMLOP Algorithm
Functional endoscopic sinus surgery (FESS) is a surgical procedure used to treat acute cases of sinusitis and other sinus diseases.
Plan in 2D, execute in 3D: An augmented reality solution for cup placement in total hip arthroplasty
Reproducibly achieving proper implant alignment is a critical step in total hip arthroplasty (THA) procedures that has been shown to substantially affect patient outcome.
Exploiting Partial Structural Symmetry For Patient-Specific Image Augmentation in Trauma Interventions
The main challenge is to automatically estimate the desired plane of symmetry within the patient's pre-operative CT. We propose to estimate this plane using a non-linear optimization strategy, by minimizing Tukey's biweight robust estimator, relying on the partial symmetry of the anatomy.
Endoscopic navigation in the absence of CT imaging
Clinical examinations that involve endoscopic exploration of the nasal cavity and sinuses often do not have a reference image to provide structural context to the clinician.
Self-supervised Learning for Dense Depth Estimation in Monocular Endoscopy
We present a self-supervised approach to training convolutional neural networks for dense depth estimation from monocular endoscopy data without a priori modeling of anatomy or shading.
Towards automatic initialization of registration algorithms using simulated endoscopy images
Several registration algorithms have been developed, many of which achieve high accuracy.
Dense Depth Estimation in Monocular Endoscopy with Self-supervised Learning Methods
We present a self-supervised approach to training convolutional neural networks for dense depth estimation from monocular endoscopy data without a priori modeling of anatomy or shading.
Pose Estimation of Periacetabular Osteotomy Fragments with Intraoperative X-Ray Navigation
Results: In simulation, average fragment pose errors were 1. 3{\deg}/1. 7 mm when the planned fragment matched the intraoperative fragment, 2. 2{\deg}/2. 1 mm when the plan was not updated to match the true shape, and 1. 9{\deg}/2. 0 mm when the fragment shape was intraoperatively estimated.
Learning to Detect Collisions for Continuum Manipulators without a Prior Model
Due to their flexibility, dexterity, and compact size, Continuum Manipulators (CMs) can enhance minimally invasive interventions.
Self-supervised Dense 3D Reconstruction from Monocular Endoscopic Video
We present a self-supervised learning-based pipeline for dense 3D reconstruction from full-length monocular endoscopic videos without a priori modeling of anatomy or shading.
Extremely Dense Point Correspondences using a Learned Feature Descriptor
In direct comparison to recent local and dense descriptors on an in-house sinus endoscopy dataset, we demonstrate that our proposed dense descriptor can generalize to unseen patients and scopes, thereby largely improving the performance of Structure from Motion (SfM) in terms of model density and completeness.
Reconstructing Sinus Anatomy from Endoscopic Video -- Towards a Radiation-free Approach for Quantitative Longitudinal Assessment
Reconstructing accurate 3D surface models of sinus anatomy directly from an endoscopic video is a promising avenue for cross-sectional and longitudinal analysis to better understand the relationship between sinus anatomy and surgical outcomes.
Anatomical Mesh-Based Virtual Fixtures for Surgical Robots
This paper presents a dynamic constraint formulation to provide protective virtual fixtures of 3D anatomical structures from polygon mesh representations.
Robotics Systems and Control Systems and Control
Learning Representations of Endoscopic Videos to Detect Tool Presence Without Supervision
We use three different methods to manipulate these latent representations in order to predict tool presence in each frame.
Surgical Data Science -- from Concepts toward Clinical Translation
We further complement this technical perspective with (4) a review of currently available SDS products and the translational progress from academia and (5) a roadmap for faster clinical translation and exploitation of the full potential of SDS, based on an international multi-round Delphi process.
Revisiting Stereo Depth Estimation From a Sequence-to-Sequence Perspective with Transformers
Stereo depth estimation relies on optimal correspondence matching between pixels on epipolar lines in the left and right images to infer depth.
Medical Robots for Infectious Diseases: Lessons and Challenges from the COVID-19 Pandemic
Medical robots can play an important role in mitigating the spread of infectious diseases and delivering quality care to patients during the COVID-19 pandemic.
Robotics
Neighborhood Normalization for Robust Geometric Feature Learning
Extracting geometric features from 3D models is a common first step in applications such as 3D registration, tracking, and scene flow estimation.
E-DSSR: Efficient Dynamic Surgical Scene Reconstruction with Transformer-based Stereoscopic Depth Perception
After that, a dynamic reconstruction algorithm which can estimate the tissue deformation and camera movement, and aggregate the information over time is proposed for surgical scene reconstruction.
On the Sins of Image Synthesis Loss for Self-supervised Depth Estimation
It is based on the idea that observed frames can be synthesized from neighboring frames if accurate depth of the scene is known - or in this case, estimated.
Temporally Consistent Online Depth Estimation in Dynamic Scenes
We present a framework named Consistent Online Dynamic Depth (CODD) to produce temporally consistent depth estimates in dynamic scenes in an online setting.
Integrating Artificial Intelligence and Augmented Reality in Robotic Surgery: An Initial dVRK Study Using a Surgical Education Scenario
In this paper, we develop a novel system by seamlessly merging artificial intelligence module and augmented reality visualization to automatically generate the surgical guidance for robotic surgery education.
SAGE: SLAM with Appearance and Geometry Prior for Endoscopy
In endoscopy, many applications (e. g., surgical navigation) would benefit from a real-time method that can simultaneously track the endoscope and reconstruct the dense 3D geometry of the observed anatomy from a monocular endoscopic video.
SyntheX: Scaling Up Learning-based X-ray Image Analysis Through In Silico Experiments
Here, we demonstrate that creating realistic simulated images from human models is a viable alternative and complement to large-scale in situ data collection.
Context-Enhanced Stereo Transformer
We construct our stereo depth estimation model, Context Enhanced Stereo Transformer (CSTR), by plugging CEP into the state-of-the-art stereo depth estimation method Stereo Transformer.
Twin-S: A Digital Twin for Skull-base Surgery
Twin-S tracks and updates the virtual model in real-time given measurements from modern tracking technologies.
TAToo: Vision-based Joint Tracking of Anatomy and Tool for Skull-base Surgery
TAToo jointly tracks the rigid 3D motion of patient skull and surgical drill from stereo microscopic videos.
Neuralangelo: High-Fidelity Neural Surface Reconstruction
Neural surface reconstruction has been shown to be powerful for recovering dense 3D surfaces via image-based neural rendering.
A Quantitative Evaluation of Dense 3D Reconstruction of Sinus Anatomy from Monocular Endoscopic Video
In this work, we perform a quantitative analysis of a self-supervised approach for sinus reconstruction using endoscopic sequences paired with optical tracking and high-resolution computed tomography acquired from nine ex-vivo specimens.
An Endoscopic Chisel: Intraoperative Imaging Carves 3D Anatomical Models
Purpose: Preoperative imaging plays a pivotal role in sinus surgery where CTs offer patient-specific insights of complex anatomy, enabling real-time intraoperative navigation to complement endoscopy imaging.
FluoroSAM: A Language-aligned Foundation Model for X-ray Image Segmentation
Recently, foundation models (FMs) -- machine learning models trained on large amounts of highly variable data thus enabling broad applicability -- have emerged as promising tools for automated image analysis.
