Abstract
During the diagnosis and analysis of complex congenital heart malformation, it is time-consuming and tedious for doctors to search for standard image planes by hand from among the huge amounts of patients’ three-dimensional (3D) ultrasound heart images. To relieve the laborious manual searching task for echocardiographers, especially for non-physicians, this paper focuses on the auto-detection of five standard image planes suggested by experts in the 3D echocardiographic images. Firstly, the four-chamber (4C) image plane is auto-detected by template matching, and then the other standard image planes are obtained according to their spatial relation with the 4C image plane. We have tested our methods on 28 normal and 22 abnormal datasets, and the error rates are 7.1% and 13.6%, respectively. With low computational complexity and simple operation, the method of auto-detection of standard planes in 3D echocardiographic images shows encouraging prospects of application.
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Peng, W., Liu, X., Wu, L. (2021). Automated Detection of Standard Image Planes in 3D Echocardiographic Images. In: Gao, H., Wang, X., Iqbal, M., Yin, Y., Yin, J., Gu, N. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 350. Springer, Cham. https://doi.org/10.1007/978-3-030-67540-0_23
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DOI: https://doi.org/10.1007/978-3-030-67540-0_23
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