Automated Assessment of Kidney Ureteroscopy Exploration for Training
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Abstract (arXiv)
Purpose: Kidney ureteroscopic navigation is challenging with a steep learning curve. However, current clinical training has major deficiencies, as it requires one-on-one feedback from experts and occurs in the operating room (OR). Therefore, there is a need for a phantom training system with automated feedback to greatly revisionexpand training opportunities. Methods: We propose a novel, purely ureteroscope video-based scope localization framework that automatically identifies calyces missed by the trainee in a phantom kidney exploration. We use a slow, thorough, prior exploration video of the kidney to generate a reference reconstruction. Then, this reference reconstruction can be used to localize any exploration video of the same phantom. Results: In 15 exploration videos, a total of 69 out of 74 calyces were correctly classified. We achieve < 4mm camera pose localization error. Given the reference reconstruction, the system takes 10 minutes to generate the results for a typical exploration (1-2 minute long). Conclusion: We demonstrate a novel camera localization framework that can provide accurate and automatic feedback for kidney phantom explorations. We show its ability as a valid tool that enables out-of-OR training without requiring supervision from an expert.
Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 8 of its 22 references that are in the map. First posted on arXiv 17 Feb 2026.
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References · 8
- 2015NetVLAD: CNN architecture for weakly supervised place recognitioncited by 935
- 2018From Coarse to Fine: Robust Hierarchical Localization at Large Scalecited by 557
- 2023ALIKED: A Lighter Keypoint and Descriptor Extraction Network via Deformable Transformationcited by 120
- 2023Tracking and Mapping in Medical Computer Vision: A Reviewcited by 36
- 2024BronchoTrack: Airway Lumen Tracking for Branch-Level Bronchoscopic Localizationcited by 11
- 20193D Reconstruction of Whole Stomach from Endoscope Video Using Structure-from-Motioncited by 10
- 2025From Monocular Vision to Autonomous Action: Guiding Tumor Resection via 3D Reconstructioncited by 10
- 2025NAVIUS: Navigated Augmented Reality Visualization for Ureteroscopic Surgerycited by 3
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