A real-time classifier to predict the contribution of cystoscopy frames to 3D reconstructions

Audrey K. Bowden; Mayaank Pillai; Rachel Eimen; Kristen Scarpato

doi:10.1117/12.2650435

17 March 2023 A real-time classifier to predict the contribution of cystoscopy frames to 3D reconstructions

Audrey K. Bowden, Mayaank Pillai, Rachel Eimen, Kristen Scarpato

Proceedings Volume PC12353, Advanced Photonics in Urology 2023; PC1235309 (2023) https://doi.org/10.1117/12.2650435
Event: SPIE BiOS, 2023, San Francisco, California, United States

Abstract

White light cystoscopy is key to inform care of patients with suspected or confirmed bladder cancer. Although three-dimensional reconstructions of cystoscopy videos can facilitate rapid, comprehensive review, they are limited by the quality of the original video. Here we address a fundamental bottleneck to reconstruction quality: real-time assessment of frame quality for eventual clinician guidance. We implemented nine metrics and combined them with a random forest classifier that achieves a sensitivity and specificity of 90.6% and 93.7%, respectively. We will use this classifier to perform real-time clinician guidance to facilitate acquisition of high-quality cystoscopy videos that produce robust three-dimensional reconstructions.

Conference Presentation

Citation Download Citation

Audrey K. Bowden, Mayaank Pillai, Rachel Eimen, and Kristen Scarpato "A real-time classifier to predict the contribution of cystoscopy frames to 3D reconstructions", Proc. SPIE PC12353, Advanced Photonics in Urology 2023, PC1235309 (17 March 2023); https://doi.org/10.1117/12.2650435

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