Automatic quality assessment of apical four-chamber echocardiograms using deep convolutional neural networks

Amir H. Abdi; Christina Luong; Teresa Tsang; Gregory Allan; Saman Nouranian; John Jue; Dale Hawley; Sarah Fleming; Ken Gin; Jody Swift; Robert Rohling; Purang Abolmaesumi

doi:10.1117/12.2254585

24 February 2017 Automatic quality assessment of apical four-chamber echocardiograms using deep convolutional neural networks

Amir H. Abdi, Christina Luong, Teresa Tsang, Gregory Allan, Saman Nouranian, John Jue, Dale Hawley, Sarah Fleming, Ken Gin, Jody Swift, Robert Rohling, Purang Abolmaesumi

Author Affiliations +

Proceedings Volume 10133, Medical Imaging 2017: Image Processing; 101330S (2017) https://doi.org/10.1117/12.2254585
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

Echocardiography (echo) is the most common test for diagnosis and management of patients with cardiac condi- tions. While most medical imaging modalities benefit from a relatively automated procedure, this is not the case for echo and the quality of the final echo view depends on the competency and experience of the sonographer. It is not uncommon that the sonographer does not have adequate experience to adjust the transducer and acquire a high quality echo, which may further affect the clinical diagnosis. In this work, we aim to aid the operator during image acquisition by automatically assessing the quality of the echo and generating the Automatic Echo Score (AES). This quality assessment method is based on a deep convolutional neural network, trained in an end-to-end fashion on a large dataset of apical four-chamber (A4C) echo images. For this project, an expert car- diologist went through 2,904 A4C images obtained from independent studies and assessed their condition based on a 6-scale grading system. The scores assigned by the expert ranged from 0 to 5. The distribution of scores among the 6 levels were almost uniform. The network was then trained on 80% of the data (2,345 samples). The average absolute error of the trained model in calculating the AES was 0.8 ± 0:72. The computation time of the GPU implementation of the neural network was estimated at 5 ms per frame, which is sufficient for real-time deployment.

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Amir H. Abdi, Christina Luong, Teresa Tsang, Gregory Allan, Saman Nouranian, John Jue, Dale Hawley, Sarah Fleming, Ken Gin, Jody Swift, Robert Rohling, and Purang Abolmaesumi "Automatic quality assessment of apical four-chamber echocardiograms using deep convolutional neural networks", Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 101330S (24 February 2017); https://doi.org/10.1117/12.2254585

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