Search Results for author:
Found 7 papers, 3 papers with code
From Generalization to Precision: Exploring SAM for Tool Segmentation in Surgical Environments
We argue that SAM drastically over-segment images with high corruption levels, resulting in degraded performance when only a single segmentation mask is considered, while the combination of the masks overlapping the object of interest generates an accurate prediction.
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.
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 Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation
In this context, we proposed a segmentation refinement method based on uncertainty analysis and graph convolutional networks.
Polyp-artifact relationship analysis using graph inductive learned representations
In inference, we use this classifier to analyze a second graph, generated from artifact and polyp predictions given by region proposal networks.
Understanding the effects of artifacts on automated polyp detection and incorporating that knowledge via learning without forgetting
We then investigated different strategies, such as a learning without forgetting framework, to leverage artifact knowledge to improve automated polyp detection.
Uncertainty-based graph convolutional networks for organ segmentation refinement
In this context, we proposed a segmentation refinement method based on uncertainty analysis and graph convolutional networks.
