3D Rendering of Cystoscopy Video Footage: A Novel Method Utilizing Neural Radiance Field Processing

Recent developments in neural radiance field (NeRF) processing have leveraged the power of neural networks to quickly reconstruct 3D spaces from 2D images. Our objective was to utilize this technology to 3D render video recordings of diagnostic cystoscopies and test their fidelity. With institutional review board (IRB) approval, we recorded two diagnostic cystoscopies, one with an Ambu single-use flexible cystoscope and the other with a Richard Wolf digital cystoscope. We converted the videos to images and manually curated approximately 100 representative images, which minimized blur and spanned a large segment of the bladder. We then utilized the NVIDIA Instant Neural Graphics Primitives (iNGP), a NeRF algorithm that uses multiresolution hash encoding with a compact neural network for significantly faster convergence, to reconstruct the bladder and render novel, unseen views within seconds. We computed the structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) to assess the quality and fidelity of the 3D rendering. Both videos were able to be utilized for 3D rendering using iNGP. The rendering derived from the Richard Wolf cystoscopy had a PSNR = 29.8 (min = 27.2, max = 32.6) and SSIM = 0.89. Similarly, the rendering derived from the Ambu cystoscopy had a PSNR = 31.3 (min = 27.1, max = 35.1) and SSIM = 0.90. Independent of cystoscopy equipment, both 3D renderings achieved reasonable fidelity. Major limitations to widespread adoption of this technology include the need for a curator to select representative and high-quality images from the initial cystoscopy video recording and the relatively small segments of bladder successfully rendered. Nonetheless, we feel that with further refinement, this technology can be scaled to create 3D renderings of cystoscopies that will enable evaluation of both completeness and quality of the cystoscopy. Furthermore, this technology would be able to facilitate the comparison of cystoscopies performed in the same patient over time.

Authors

Institutions

Publication Details

Journal
Cureus
Published
2025-08-11
DOI
https://doi.org/10.7759/cureus.89825
Primary Topic
Pelvic floor disorders treatments
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

3D Rendering of Cystoscopy Video Footage: A Novel Method Utilizing Neural Radiance Field Processing

Jonathan E. Katz, Michael C. Yip, Roger L. Sur, Jingpei Lu et al.
Cureus
Pelvic floor disorders treatments
article

3D Rendering of Cystoscopy Video Footage: A Novel Method Utilizing Neural Radiance Field Processing

Jonathan E. Katz, Michael C. Yip, Roger L. Sur, Jingpei Lu, Lin Shan, Jamie L. Finegan, Pablo F Beutelspacher
article en

Abstract

Recent developments in neural radiance field (NeRF) processing have leveraged the power of neural networks to quickly reconstruct 3D spaces from 2D images. Our objective was to utilize this technology to 3D render video recordings of diagnostic cystoscopies and test their fidelity. With institutional review board (IRB) approval, we recorded two diagnostic cystoscopies, one with an Ambu single-use flexible cystoscope and the other with a Richard Wolf digital cystoscope. We converted the videos to images and manually curated approximately 100 representative images, which minimized blur and spanned a large segment of the bladder. We then utilized the NVIDIA Instant Neural Graphics Primitives (iNGP), a NeRF algorithm that uses multiresolution hash encoding with a compact neural network for significantly faster convergence, to reconstruct the bladder and render novel, unseen views within seconds. We computed the structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) to assess the quality and fidelity of the 3D rendering. Both videos were able to be utilized for 3D rendering using iNGP. The rendering derived from the Richard Wolf cystoscopy had a PSNR = 29.8 (min = 27.2, max = 32.6) and SSIM = 0.89. Similarly, the rendering derived from the Ambu cystoscopy had a PSNR = 31.3 (min = 27.1, max = 35.1) and SSIM = 0.90. Independent of cystoscopy equipment, both 3D renderings achieved reasonable fidelity. Major limitations to widespread adoption of this technology include the need for a curator to select representative and high-quality images from the initial cystoscopy video recording and the relatively small segments of bladder successfully rendered. Nonetheless, we feel that with further refinement, this technology can be scaled to create 3D renderings of cystoscopies that will enable evaluation of both completeness and quality of the cystoscopy. Furthermore, this technology would be able to facilitate the comparison of cystoscopies performed in the same patient over time.

CureusVol. 17(8)
University of San Diego (US), University of California San Diego (US)
Openalex Percentile: Top 68%
Pelvic floor disorders treatments
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.