Iranian Journal of Medical Physics

Iranian Journal of Medical Physics

Algorithm for Recognition of Left Atrial Appendage Boundaries in Echocardiographic Images

Document Type : Original Paper

Authors
Hosein Ghayoumi Zadeh 1
Ali Fayazi 1
Narbeh Melikian 2
Mark J Monaghan 3
Mehdi Eskandari 4
1 1. Non-communicable Diseases Research Center, Rafsanjan University of Medical Sciences, Rafsanjan, Iran 2. Department of Electrical Engineering, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran
2 MD, MRCP, King’s College Hospital, London, United Kingdom
3 FBSE, FACC, FESC, King’s College Hospital, London, United Kingdom
4 MD, FRACP, King’s College Hospital, London, United Kingdom
10.22038/ijmp.2020.43736.1663
Abstract
Introduction: The left atrial appendage )LAA( occlusion using a purpose-built device is a growing procedure. This study aimed to develop a computer-aided diagnostic system for the recognition of the LAA in echocardiographic images.
Material and Methods: The three-dimensional (3D) echocardiographic images of the LAA of 26 patients successfully treated with an LAA occluder were used in this study. A total of 208 3D derived two-dimensional images in the axial plane were derived from each 3D dataset. Then, 562 images in which the LAA boundaries were highly recognizable were selected for this study. The proposed convolutional neural network (CNN) in this study was based on open-source object identification and classification platform compiled under the You Only Look Once algorithm. Finally, 419 and 143 images were used for training and testing the algorithm, respectively.
Results: Algorithm performance on the identification of the LAA region on a set of 143 images was compared to that reported for the traced regions on the same images by an expert in echocardiography using an intersection over the union (IOU) algorithm. The algorithm was able to correctly identify the LAA region in all 143 examined images with an average IOU of 90.7%. 
Conclusion: The proposed image-based CNN algorithm in this study showed high accuracy in the recognition of the LAA boundaries in the echocardiographic images. The method can be used in the development of algorithms for the automated analysis of the area of the LAA used for device sizing and procedural planning in the LAA occlusion procedures.
Keywords
Artificial Intelligence
Atrial Fibrillation
Computer Vision
Echocardiographic
Left Atrial Appendage
Subjects
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Iranian Journal of Medical Physics
Volume 18, Issue 2
March and April 2021
Pages 123-132

  • Receive Date 11 October 2019
  • Revise Date 13 January 2020
  • Accept Date 31 January 2020