6 papers
Learning Representations of Endoscopic Videos to Detect Tool Presence Without Supervision
David Z. Li, Masaru Ishii, Russell H. Taylor +2
In this work, we explore whether it is possible to learn representations of endoscopic video frames to perform tasks such as identifying surgical tool presence without supervision.…
Self-supervised Dense 3D Reconstruction from Monocular Endoscopic Video
Xingtong Liu, Ayushi Sinha, Masaru Ishii +3
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. Our m…
Dense Depth Estimation in Monocular Endoscopy with Self-supervised Learning Methods
Xingtong Liu, Ayushi Sinha, Masaru Ishii +4
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 sha…
Self-supervised Learning for Dense Depth Estimation in Monocular Endoscopy
Xingtong Liu, Ayushi Sinha, Mathias Unberath +4
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 sha…
Towards automatic initialization of registration algorithms using simulated endoscopy images
Ayushi Sinha, Masaru Ishii, Russell H. Taylor +2
Registering images from different modalities is an active area of research in computer aided medical interventions. Several registration algorithms have been developed, many of whi…
Endoscopic navigation in the absence of CT imaging
Ayushi Sinha, Xingtong Liu, Austin Reiter +3
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. In thi…