PURPOSE: The purpose of the study is to develop an algorithm for the segmentation of renal calculi on ureteroscopic images. In fact, renal calculi are common source of urological obstruction, and laser lithotripsy during ureteroscopy is a possible therapy. A laser-based system to sweep the calculus surface and vaporize it was developed to automate a very tedious manual task. The distal tip of the ureteroscope is directed using image guidance, and this operation is not possible without an efficient segmentation of renal calculi on the ureteroscopic images. METHODS: We proposed and developed a region growing algorithm to segment renal calculi on ureteroscopic images. Using real video images to compute ground truth and compare our segmentation with a reference segmentation, we computed statistics on different image metrics, such as Precision, Recall, and Yasnoff Measure, for comparison with ground truth. RESULTS: The algorithm and its parameters were established for the most likely clinical scenarii. The segmentation results are encouraging: the developed algorithm was able to correctly detect more than 90% of the surface of the calculi, according to an expert observer. CONCLUSION: Implementation of an algorithm for the segmentation of calculi on ureteroscopic images is feasible. The next step is the integration of our algorithm in the command scheme of a motorized system to build a complete operating prototype.
PURPOSE: The purpose of the study is to develop an algorithm for the segmentation of renal calculi on ureteroscopic images. In fact, renal calculi are common source of urological obstruction, and laser lithotripsy during ureteroscopy is a possible therapy. A laser-based system to sweep the calculus surface and vaporize it was developed to automate a very tedious manual task. The distal tip of the ureteroscope is directed using image guidance, and this operation is not possible without an efficient segmentation of renal calculi on the ureteroscopic images. METHODS: We proposed and developed a region growing algorithm to segment renal calculi on ureteroscopic images. Using real video images to compute ground truth and compare our segmentation with a reference segmentation, we computed statistics on different image metrics, such as Precision, Recall, and Yasnoff Measure, for comparison with ground truth. RESULTS: The algorithm and its parameters were established for the most likely clinical scenarii. The segmentation results are encouraging: the developed algorithm was able to correctly detect more than 90% of the surface of the calculi, according to an expert observer. CONCLUSION: Implementation of an algorithm for the segmentation of calculi on ureteroscopic images is feasible. The next step is the integration of our algorithm in the command scheme of a motorized system to build a complete operating prototype.
Authors: John R van Sörnsen de Koste; Suresh Senan; Catharina E Kleynen; Ben J Slotman; Frank J Lagerwaard Journal: Int J Radiat Oncol Biol Phys Date: 2005-11-18 Impact factor: 7.038
Authors: Corollos Abdelshehid; Michael T Ahlering; David Chou; Hyung Keun Park; Jay Basillote; David Lee; Isaac Kim; Louis Eichel; Dmitriy Protsenko; Brian Wong; Elspeth McDougall; Ralph V Clayman Journal: J Urol Date: 2005-06 Impact factor: 7.450