34 citations · 87 across the 11 of their papers we have counts for
12 papers
SAGE: SLAM with Appearance and Geometry Prior for Endoscopy
Xingtong Liu, Zhaoshuo Li, Masaru Ishii +3
In endoscopy, many applications (e.g., surgical navigation) would benefit from a real-time method that can simultaneously track the endoscope and reconstruct the dense 3D geometry…
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.…
Extremely Dense Point Correspondences using a Learned Feature Descriptor
Xingtong Liu, Yiping Zheng, Benjamin Killeen +4
High-quality 3D reconstructions from endoscopy video play an important role in many clinical applications, including surgical navigation where they enable direct video-CT registrat…
Reconstructing Sinus Anatomy from Endoscopic Video -- Towards a Radiation-free Approach for Quantitative Longitudinal Assessment
Xingtong Liu, Maia Stiber, Jindan Huang +4
Reconstructing accurate 3D surface models of sinus anatomy directly from an endoscopic video is a promising avenue for cross-sectional and longitudinal analysis to better understan…
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…
Anatomically Constrained Video-CT Registration via the V-IMLOP Algorithm
Seth D. Billings, Ayushi Sinha, Austin Reiter +4
Functional endoscopic sinus surgery (FESS) is a surgical procedure used to treat acute cases of sinusitis and other sinus diseases. FESS is fast becoming the preferred choice of tr…
Perspectives on Surgical Data Science
S. Swaroop Vedula, Masaru Ishii, Gregory D. Hager
The availability of large amounts of data together with advances in analytical techniques afford an opportunity to address difficult challenges in ensuring that healthcare is safe,…
Automated Objective Surgical Skill Assessment in the Operating Room Using Unstructured Tool Motion
Piyush Poddar, Narges Ahmidi, S. Swaroop Vedula +3
Previous work on surgical skill assessment using intraoperative tool motion in the operating room (OR) has focused on highly-structured surgical tasks such as cholecystectomy. Furt…