PURPOSE: Epicardial ultrasound (EUS) can be used to assess the quality of coronary artery bypass graft surgery (CABG) anastomoses by determining stenotic rates. Currently, no objective quantitative methods are available for the analysis of EUS images. Therefore, surgeons have to be trained in interpreting EUS images, which may limit the use of EUS in clinical practice. Automatic detection of vessel structures can enable the objective and quantitative quality assessment of anastomoses without user interaction to facilitate the revision of anastomoses during the primary surgery. METHODS: An automatic vessel detection algorithm extracted and detected image regions that uniquely intersected with the vessel lumen of anastomotic structures. First, an initial pixel-based segmentation was performed from regional minimums using a watershed segmentation and an adaptive thresholding approach. A region-based merging step was then performed to merge oversegmented vessel structures using a Bayesian classification of different region combinations constructed from the pixel-based segmentations. Finally, a vessel classification step was performed on the extracted regions after the region-based merging to determine the probabilities that the regions contained vessel structures. RESULTS: The performance of the vessel classifier was tested using m-fold cross-validation of 320 EUS images containing anastomotic vessel structures from 16 anastomoses made on healthy porcine vessels. An area under the curve of 0.966 (95 % CI 0.951-0.984) and 0.989 (95 % CI 0.985-0.993, p < 0.001) of a precision-recall and receiver operator characteristic curve, respectively, was obtained when detecting vessel regions extracted from the EUS images. CONCLUSIONS: The vessel detection algorithm can detect vessel regions in EUS images at a high accuracy. It can be used to enable the automatic analysis of EUS images for the quality assessment of CABG anastomoses.
PURPOSE: Epicardial ultrasound (EUS) can be used to assess the quality of coronary artery bypass graft surgery (CABG) anastomoses by determining stenotic rates. Currently, no objective quantitative methods are available for the analysis of EUS images. Therefore, surgeons have to be trained in interpreting EUS images, which may limit the use of EUS in clinical practice. Automatic detection of vessel structures can enable the objective and quantitative quality assessment of anastomoses without user interaction to facilitate the revision of anastomoses during the primary surgery. METHODS: An automatic vessel detection algorithm extracted and detected image regions that uniquely intersected with the vessel lumen of anastomotic structures. First, an initial pixel-based segmentation was performed from regional minimums using a watershed segmentation and an adaptive thresholding approach. A region-based merging step was then performed to merge oversegmented vessel structures using a Bayesian classification of different region combinations constructed from the pixel-based segmentations. Finally, a vessel classification step was performed on the extracted regions after the region-based merging to determine the probabilities that the regions contained vessel structures. RESULTS: The performance of the vessel classifier was tested using m-fold cross-validation of 320 EUS images containing anastomotic vessel structures from 16 anastomoses made on healthy porcine vessels. An area under the curve of 0.966 (95 % CI 0.951-0.984) and 0.989 (95 % CI 0.985-0.993, p < 0.001) of a precision-recall and receiver operator characteristic curve, respectively, was obtained when detecting vessel regions extracted from the EUS images. CONCLUSIONS: The vessel detection algorithm can detect vessel regions in EUS images at a high accuracy. It can be used to enable the automatic analysis of EUS images for the quality assessment of CABG anastomoses.
Authors: Ricardo P J Budde; Rudy Meijer; Thomas C Dessing; Cornelius Borst; Paul F Gründeman Journal: J Thorac Cardiovasc Surg Date: 2005-05 Impact factor: 5.209
Authors: Per Kristian Hol; Kai Andersen; Helge Skulstad; Per Steinar Halvorsen; Per Snorre Lingaas; Rune Andersen; Jacob Bergsland; Erik Fosse Journal: Ann Thorac Surg Date: 2007-09 Impact factor: 4.330
Authors: M J Mack; J A Magovern; T A Acuff; R J Landreneau; D M Tennison; E J Tinnerman; J A Osborne Journal: Ann Thorac Surg Date: 1999-08 Impact factor: 4.330