1. Bladder image stitching algorithm for navigation and referencing using a standard cystoscope.
- Author
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Li M, Varble NA, Gurram S, Long D, Valera V, Gopal N, Bakhutashvili I, Reed S, Pritchard WF, Karanian JW, Xu S, and Wood BJ
- Subjects
- Animals, Swine, Urinary Bladder Neoplasms diagnostic imaging, Urinary Bladder Neoplasms pathology, Cystoscopes, Image Processing, Computer-Assisted methods, Humans, Algorithms, Urinary Bladder diagnostic imaging, Cystoscopy methods, Phantoms, Imaging
- 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., Competing Interests: Declarations. Competing interests: NV is an employee of Philips. BJW is the Principal Investigator in Cooperative Research and Development Agreements between NIH and the following: BTG Biocompatibles/Boston Scientific, Siemens, NVIDIA, Celsion Corp, Canon Medical, XAct Robotics, and Philips. BJW and NIH are party to Material Transfer or Collaboration Agreements with: Angiodynamics, 3T Technologies, Profound Medical, Exact Imaging, Johnson and Johnson, Endocare/Healthtronics, and Medtronic. Outside the submitted work, BJW is primary inventor on 47 issued patents owned by the NIH (list available upon request), a portion of which have been licensed by NIH to Philips. BJW and NIH report a licensing agreement with Canon Medical on algorithm software with no patent. BJW is joint inventor (assigned to HHS NIH US Government (for patents and pending patents related to drug eluting bead technology, some of which may have joint inventorships with BTG Biocompatibles/Boston Scientific. BJW is primary inventor on patents owned by NIH in the space of drug eluting embolic beads. No other authors have conflicts of interest to declare., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)
- Published
- 2024
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