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 \revision{expand} 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.

翻译：目的：肾脏输尿管镜导航具有挑战性且学习曲线陡峭。然而，当前的临床培训存在重大缺陷，因为它需要专家的一对一反馈，并且发生在手术室（OR）内。因此，需要一种具有自动化反馈的模型训练系统，以极大地扩展培训机会。方法：我们提出了一种新颖的、纯粹基于输尿管镜视频的镜体定位框架，该框架能自动识别学员在肾脏模型探查中遗漏的肾盏。我们使用一段缓慢、彻底的、预先录制的肾脏探查视频来生成参考重建模型。然后，该参考重建模型可用于定位同一模型的任何探查视频。结果：在15段探查视频中，总共74个肾盏中有69个被正确分类。我们实现了小于4毫米的相机姿态定位误差。在给定参考重建模型的情况下，系统需要10分钟来生成一次典型探查（时长1-2分钟）的结果。结论：我们展示了一种新颖的相机定位框架，能够为肾脏模型探查提供准确、自动的反馈。我们证明了其作为一种有效工具的能力，可在无需专家监督的情况下实现手术室外培训。