A Semi-Automated Algorithm for Segmentation of the Left Atrial Appendage Landing Zone: Application in Left Atrial Appendage Occlusion Procedures
محل انتشار: مجله فیزیک و مهندسی پزشکی، دوره: 10، شماره: 2
سال انتشار: 1399
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 113
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شناسه ملی سند علمی:
JR_JBPE-10-2_012
تاریخ نمایه سازی: 1 بهمن 1402
چکیده مقاله:
Background: Mechanical occlusion of the Left atrial appendage (LAA) using a purpose-built device has emerged as an effective prophylactic treatment in patients with atrial fibrillation at risk of stroke and a contraindication for anticoagulation. A crucial step in procedural planning is the choice of the device size. This is currently based on the manual analysis of the “Device Landing Zone” from echocardiographic images. Objective: We aimed to develop an algorithm for automated segmentation of the LAA landing zone from ۳D echocardiographic images of the LAA.Material and Methods: In this experimental study, ۲D axial images were derived from the ۳D echo datasets. After image pre-processing, binary images were created using a thresholding method. A binary image matrix was then formed and scanned using ۸-adgacency approach resulting in segmentation of the objects with a closed circumference within the image. Erosion/dilation techniques were then applied to remove small objects. A feature-based approach was then used to firstly detect the LAA region and secondly to identify the device landing zone. Results: A total of ۲۲ datasets were used in this study. The algorithm produced up to ۹ axial images as the proposed landing zone. The selected axial images were compared to the echocardiographic images. In ۱۸ cases (۸۱.۸%), the algorithm successfully segmented the LAA and proposed the landing zone based on the defined features. Conclusion: We have developed a simple and fast algorithm for semi-automated segmentation of the LAA landing zone. Further studies are needed to assess the accuracy of the proposed landing zones by this method.
کلیدواژه ها:
نویسندگان
A Pakizeh Moghadam
PhD candidate, Department of Electrical and Computer Engineering, Hakim Sabzevari University, Sabzevar, Iran
M Eskandari
MD, Department of Cardiology, King’s College Hospital, London, UK
M J Monaghan
PhD, Department of Cardiology, King’s College Hospital, London, UK
J Haddadnia
PhD, Department of Electrical and Computer Engineering, Hakim Sabzevari University, Sabzevar, Iran
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