A Semi-Automated Algorithm for Segmentation of the Left Atrial Appendage Landing Zone: Application in Left Atrial Appendage Occlusion Procedures
Publish Year: 1399
نوع سند: مقاله ژورنالی
زبان: English
View: 88
This Paper With 10 Page And PDF Format Ready To Download
- Certificate
- من نویسنده این مقاله هستم
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
JR_JBPE-10-2_012
تاریخ نمایه سازی: 1 بهمن 1402
Abstract:
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.
Keywords:
Authors
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
مراجع و منابع این Paper:
لیست زیر مراجع و منابع استفاده شده در این Paper را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود Paper لینک شده اند :