Abstract
Background and Objective: In the first trimester of pregnancy, fetal growth, and abnormalities can be assessed using the exact middle sagittal plane (MSP) of the fetus. However, the ultrasound (US) image quality and operator experience affect the accuracy. We present an automatic system that enables precise fetal MSP detection from three-dimensional (3D) US and provides an evaluation of its performance using a generative adversarial network (GAN) framework. Method: The neural network is designed as a filter and generates masks to obtain the MSP, learning the features and MSP location in 3D space. Using the proposed image analysis system, a seed point was obtained from 218 first-trimester fetal 3D US volumes using deep learning and the MSP was automatically extracted. Results: The experimental results reveal the feasibility and excellent performance of the proposed approach between the automatically and manually detected MSPs. There was no significant difference between the semi-automatic and automatic systems. Further, the inference time in the automatic system was up to two times faster than the semi-automatic approach. Conclusion: The proposed system offers precise fetal MSP measurements. Therefore, this automatic fetal MSP detection and measurement approach is anticipated to be useful clinically. The proposed system can also be applied to other relevant clinical fields in the future.
Highlights
Ultrasound (US), as a convenient, powerful, and effective tool, is widely used for prenatal growth assessment and plays an important role in prenatal diagnosis
The image volumes were acquired by the appropriate training of sonographers and adherence to a standard technique in accordance with the guidelines that were established by The Fetal Medicine Foundation (FMF)
Bland–Altman plots were used to assess the bias of the automatic semi-automatic system detectioninvolved methods. manual determination of the seed point
Summary
Ultrasound (US), as a convenient, powerful, and effective tool, is widely used for prenatal growth assessment and plays an important role in prenatal diagnosis. Most major fetal abnormalities can be identified by US before delivery, even in the first trimester of pregnancy [1]. Few unexpected findings and some major structural abnormality with thick nuchal translucency could be identified in first trimester scans of patients with negative cell-free DNA [3,4]. In the first trimester of pregnancy, fetal growth, and abnormalities can be assessed using the exact middle sagittal plane (MSP) of the fetus. We present an automatic system that enables precise fetal MSP detection from three-dimensional (3D) US and provides an evaluation of its performance using a generative adversarial network (GAN) framework. Using the proposed image analysis system, a seed point was obtained from 218 first-trimester fetal 3D US volumes using deep learning and the MSP was automatically extracted
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