Abstract
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.