Center for Interventional Oncology, National Institutes of Health, Bethesda, MD, USA.
Philips Healthcare, Cambridge, MA, USA.
Sci Rep. 2024 Nov 25;14(1):29168. doi: 10.1038/s41598-024-80284-7.
To aid in the diagnosis, monitoring, and surveillance of bladder carcinoma, this study aimed to develop and test an algorithm that creates a referenceable bladder map rendered from standard cystoscopy videos without the need for specialized equipment. A vision-based algorithm was developed to generate 2D bladder maps from individual video frames, by sequentially stitching image frames based on matching surface features, and subsequently localize and track frames during reevaluation. The algorithm was developed and calibrated in a 2D model and 3D anthropomorphic bladder phantom. The performance was evaluated in vivo in swine and with retrospective clinical cystoscopy video. Results showed that the algorithm was capable of capturing and stitching intravesical images with different sweeping patterns. Between 93% and 99% of frames had sufficient features for bladder map generation. Upon reevaluation, the cystoscope accurately localized a frame within 4.5 s. In swine, a virtual mucosal surface map was generated that matched the explant anatomy. A surface map could be generated based on archived patient cystoscopy images. This tool could aid recording and referencing pathologic findings and biopsy or treatment locations for subsequent procedures and may have utility in patients with metachronous bladder cancer and in low-resource settings.
为了辅助膀胱癌的诊断、监测和随访，本研究旨在开发和测试一种算法，该算法可从标准膀胱镜视频中创建可参考的膀胱图谱，而无需专门设备。本研究开发了一种基于视觉的算法，可通过顺序拼接基于匹配表面特征的图像帧，以及随后在重新评估期间定位和跟踪帧，从而从单个视频帧生成 2D 膀胱图谱。该算法在二维模型和三维拟人膀胱模型中进行了开发和校准。该算法在猪体内和回顾性临床膀胱镜视频中进行了性能评估。结果表明，该算法能够捕获和拼接具有不同扫掠模式的膀胱内图像。有 93%至 99%的帧具有足够的特征用于生成膀胱图谱。在重新评估时，膀胱镜可在 4.5 秒内准确定位一帧。在猪体内，生成了与解剖标本相匹配的虚拟黏膜表面图谱。可基于存档的患者膀胱镜图像生成表面图谱。该工具可辅助记录和参考病理发现以及活检或治疗位置，以便后续操作，并且可能对患有膀胱癌和资源有限的患者具有实用价值。
