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Found 9 papers, 3 papers with code
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.
A force-sensing surgical drill for real-time force feedback in robotic mastoidectomy
Results: The force measurements on the tip of the surgical drill are validated with raw-egg drilling experiments, where a force sensor mounted below the egg serves as ground truth.
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.
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.
Simulation-to-Real domain adaptation with teacher-student learning for endoscopic instrument segmentation
Conclusion: We show that our proposed approach can successfully exploit the unlabeled real endoscopic video frames and improve generalization performance over pure simulation-based training and the previous state-of-the-art.
Endo-Sim2Real: Consistency learning-based domain adaptation for instrument segmentation
Surgical tool segmentation in endoscopic videos is an important component of computer assisted interventions systems.
VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images
Two datasets containing a total of 374 multi-detector CT scans from 355 patients were prepared and 4505 vertebrae have individually been annotated at voxel-level by a human-machine hybrid algorithm (https://osf. io/nqjyw/, https://osf. io/t98fz/).
Tool and Phase recognition using contextual CNN features
A transfer learning method for generating features suitable for surgical tools and phase recognition from the ImageNet classification features [1] is proposed here.
