Deep learning‐based computer‐aided fetal echocardiography: Application to heart standard view segmentation for congenital heart defects detection

firdaus, firdaus (2021) Deep learning‐based computer‐aided fetal echocardiography: Application to heart standard view segmentation for congenital heart defects detection. Sensors, 21 (8007). ISSN 14248220

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Official URL: https://www.mdpi.com/1424-8220/21/23/8007

Abstract

Accurate segmentation of fetal heart in echocardiography images is essential for detecting the structural abnormalities such as congenital heart defects (CHDs). Due to the wide variations attributed to different factors, such as maternal obesity, abdominal scars, amniotic fluid volume, and great vessel connections, this process is still a challenging problem. CHDs detection with ex-pertise in general are substandard; the accuracy of measurements remains highly dependent on humans’ training, skills, and experience. To make such a process automatic, this study proposes deep learning‐based computer‐aided fetal heart echocardiography examinations with an instance segmentation approach, which inherently segments the four standard heart views and detects the defect simultaneously. We conducted several experiments with 1149 fetal heart images for predict-ing 24 objects, including four shapes of fetal heart standard views, 17 objects of heart‐chambers in each view, and three cases of congenital heart defect. The result showed that the proposed model performed satisfactory performance for standard views segmentation, with a 79.97% intersection over union and 89.70% Dice coefficient similarity. It also performed well in the CHDs detection, with mean average precision around 98.30% for intra‐patient variation and 82.42% for inter‐patient variation. We believe that automatic segmentation and detection techniques could make an im-portant contribution toward improving congenital heart disease diagnosis rates. © 2021 by the authors. Li-censee MDPI, Basel, Switzerland.

Item Type:	Article
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Mr Firdaus Firdaus
Date Deposited:	17 Mar 2023 23:35
Last Modified:	17 Mar 2023 23:35
URI:	http://repository.unsri.ac.id/id/eprint/90678

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