Leveraging Near-Field Lighting for Monocular Depth Estimation from Endoscopy Videos

no code implementations • 26 Mar 2024 • Akshay Paruchuri, Samuel Ehrenstein, Shuxian Wang, Inbar Fried, Stephen M. Pizer, Marc Niethammer, Roni Sengupta

Monocular depth estimation in endoscopy videos can enable assistive and robotic surgery to obtain better coverage of the organ and detection of various health issues.

Monocular Depth Estimation Transfer Learning

A Surface-normal Based Neural Framework for Colonoscopy Reconstruction

no code implementations • 13 Mar 2023 • Shuxian Wang, Yubo Zhang, Sarah K. McGill, Julian G. Rosenman, Jan-Michael Frahm, Soumyadip Sengupta, Stephen M. Pizer

Reconstructing a 3D surface from colonoscopy video is challenging due to illumination and reflectivity variation in the video frame that can cause defective shape predictions.

ColDE: A Depth Estimation Framework for Colonoscopy Reconstruction

no code implementations • 19 Nov 2021 • Yubo Zhang, Jan-Michael Frahm, Samuel Ehrenstein, Sarah K. McGill, Julian G. Rosenman, Shuxian Wang, Stephen M. Pizer

Aiming to fundamentally improve the depth estimation quality for colonoscopy 3D reconstruction, in this work we have designed a set of training losses to deal with the special challenges of colonoscopy data.

3D Reconstruction Depth Estimation +1

Lighting Enhancement Aids Reconstruction of Colonoscopic Surfaces

no code implementations • 18 Mar 2021 • Yubo Zhang, Shuxian Wang, Ruibin Ma, Sarah K. McGill, Julian G. Rosenman, Stephen M. Pizer

In this work we focus on the lighting problem in colonoscopy videos.

Surface Reconstruction