ArticleBiomedical optics express2017
3D reconstruction of cystoscopy videos for comprehensive bladder records.
Article in Biomedical optics express, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
26 citing papers in PubMed, 56 citations in OpenAlex.
- A multi-modal approach for decision making in bladder cancer.Nature reviews. Urology · 2026Review
- A Novel Radiomics-based Interpretable Model for Bladder Cancer Grade Prediction Using White-Light Cystoscopy Images.European urology open science · 2026Article
- 3D Rendering of Cystoscopy Video Footage: A Novel Method Utilizing Neural Radiance Field Processing.Cureus · 2025Article
- High-Fidelity 3D Reconstruction for Accurate Anatomical Measurements in Endoscopic Sinus Surgery.Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
- Optimizing cystoscopy and TURBT: enhanced imaging and artificial intelligence.Nature reviews. Urology · 2025Review
- Bladder image stitching algorithm for navigation and referencing using a standard cystoscope.Scientific reports · 2024Article
- Virtual simulator for cystoscopy containing motion blur and bladder debris to aid the development of clinical tools.Biomedical optics express · 2024Article
- Three-Dimensional Dense Reconstruction: A Review of Algorithms and Datasets.Sensors (Basel, Switzerland) · 2024Review
- Automated Upper Tract Urothelial Carcinoma Tumor Segmentation During Ureteroscopy Using Computer Vision Techniques.Journal of endourology · 2024Article
- Autonomous Magnetic Navigation in Endoscopic Image Mosaics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Towards improved 3D reconstruction of cystoscopies through real-time feedback for frame reacquisition.Biomedical optics express · 2024Article
- Fiberscopic pattern removal for optimal coverage in 3D bladder reconstructions of fiberscope cystoscopy videos.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Optimizing Submillimeter 3D Modeling with Auxiliary Lighting and Artificial Textures: An SfM-Based Approach.F1000Research · 2024Article
- Pose estimation via structure-depth information from monocular endoscopy images sequence.Biomedical optics express · 2024Article
- Adaptive feature extraction method for capsule endoscopy images.Visual computing for industry, biomedicine, and art · 2023Article
- 3-D and 2-D reconstruction of bladders for the assessment of inter-session detection of tissue changes: a proof of concept.International journal of computer assisted radiology and surgery · 2023Article
- Deep causal learning for robotic intelligence.Frontiers in neurorobotics · 2023Review
- Joint estimation of depth and motion from a monocular endoscopy image sequence using a multi-loss rebalancing network.Biomedical optics express · 2022Article
- Feature matching for texture-less endoscopy images via superpixel vector field consistency.Biomedical optics express · 2022Article
- Intensity-Mosaic: automatic panorama mosaicking of disordered images with insufficient features.Journal of medical imaging (Bellingham, Wash.) · 2021Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
White light endoscopy is widely used for diagnostic imaging of the interior of organs and body cavities, but the inability to correlate individual 2D images with 3D organ morphology limits its utility for quantitative or longitudinal studies of disease physiology or cancer surveillance. As a result, most endoscopy videos, which carry enormous data potential, are used only for real-time guidance and are discarded after collection. We present a computational method to reconstruct and visualize a 3D model of organs from an endoscopic video that captures the shape and surface appearance of the organ. A key aspect of our strategy is the use of advanced computer vision techniques and unmodified, clinical-grade endoscopy hardware with few constraints on the image acquisition protocol, which presents a low barrier to clinical translation. We validate the accuracy and robustness of our reconstruction and co-registration method using cystoscopy videos from tissue-mimicking bladder phantoms and show clinical utility during cystoscopy in the operating room for bladder cancer evaluation. As our method can powerfully augment the visual medical record of the appearance of internal organs, it is broadly applicable to endoscopy and represents a significant advance in cancer surveillance opportunities for big-data cancer research.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.