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Updated: Jun 23, 2026
Evaluation of a Novel Laser-assisted Coronary Anastomotic Connector - the Trinity Clip - in a Porcine Off-pump Bypass Model
Published on: November 24, 2014
A laboratory feasibility study using a computer algorithm for anastomosis segmentation of epicardial ultrasonography
Alex Skovsbo Jørgensen1, Martin Siemienski Andersen1, Lasse Riis Østergaard1
1Department of Health Science and Technology, Aalborg University, Selma Lagerløfs Vej 249, Gistrup, 9260, Denmark.
Background:
The outcome of coronary artery bypass grafting (CABG) depends on several factors, including the quality of the distal anastomoses to the coronary arteries. Early graft failure may be caused by, e.g., technical suture failures, and such failures may be detected using intraoperative quality assessment. High-intensity epicardial ultrasonography (ECUS) allows anatomical visualization of the anastomoses during surgery, but currently, the images must be assessed manually. Here, we aim to describe an automatic quality assessment of distal coronary anastomoses using in-house software for vessel area and diameter extraction.
Methods:
A postoperative, laboratory, investigational feasibility study comparing computer readings of longitudinal and transverse ultrasonographic images of distal coronary artery anastomoses with manual readings was performed, including ECUS images from 30 patients undergoing elective, isolated on-pump CABG. Vessel and anastomosis segmentation performance metrics from images obtained intraoperatively were compared to assess agreement between the manual and automatic segmentation methods. Scatter plots, the Dice coefficient and correlation analyses were used as measures of similarity between the two readings. p < 0.05 was considered significant.
Results:
The number of dimensions of anastomotic vessel structures that are relevant for stenosis quantification and the Dice coefficient were 0.888 between the automatic and manual segmentations. The correlation coefficient between the manual and automatic stenotic rates was 0.674.
Conclusions:
An anastomosis segmentation software for automatic and objective extraction of the anatomical dimensions of relevant distal coronary anastomotic structures from ECUS images obtained during CABG was developed. The framework allows for quantifying stenotic in the anastomotic structures and has the potential to assist surgeons during quality assessment of coronary anastomoses when the described segmentation of vessels and anastomoses is available for real-time epicardial ultrasonography use during surgery.
Trial Registration:
The study was registered on September 29, 2016, before enrolment of the first participant (ClinicalTrials.gov ID: NCT02919124).
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