Self-Supervised Feature Detection and 3D Reconstruction for Real-Time Neuroendoscopic Guidance.

ABSTRACT:

Objective

Transventricular approach to deep-brain targets offers direct visualization but also imparts deformation that challenges accurate neuronavigation. 3D reconstruction and registration of the endoscopic view could provide up-to-date, real-time guidance. We develop and evaluate a self-supervised feature detection method for 3D reconstruction and navigation in neuroendoscopy.

Methods

Unlabeled neuroendoscopic video data from 15 clinical cases yielding 11,527 video frames yielding 11,527 video frames were used to train a self-supervised learning method (R2D2-E) with 5-fold cross validation integrated into a simultaneous localization and mapping (SLAM) pipeline for 3D reconstruction. A series of experiments guided nominal hyperparameters selection and evaluated performanc

SUBMITTER: Vagdargi P 

PROVIDER: S-EPMC12910575 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature