Neural Network Algorithm-Based Three-Dimensional Ultrasound Evaluation in the Diagnosis of Fetal Spina Bifida

Lei Chen,Yingying Tian,Yujie Deng,Enas Abdulhay

doi:10.1155/2021/3605739

Lei Chen, Yingying Tian + Show 2 more

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https://doi.org/10.1155/2021/3605739

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Abstract

In order to realize the automatic recognition and diagnosis in ultrasound images of fetal spina bifida, the U-Net algorithm was improved in this study to obtain a new convolutional neural network algorithm—Oct-U-Net. 3,300 pregnant women were selected as the research objects, who underwent three-dimensional (3D) ultrasound examinations. Then, Oct-U-Net was applied to evaluate the diagnostic effect of fetal spina bifida by recall rate, precise rate, mean standard error, pixel accuracy (PA), mean intersection over union (MIoU), and running time. Besides, the fully convolutional network (FCN) algorithm and the U-Net algorithm were introduced for comparison. Results showed that recall rate, precise rate, PA, and MioU of Oct-U-Net were 0.93, 0.96, 0.949, and 0.917, respectively, which were markedly higher than those of FCN and U-Net P < 0.05 . The mean standard error of Oct-U-Net was 4.1243, and its average running time was 12.15 seconds. The values of the above two indicators were sharply lower than those of FCN and U-Net P < 0.05 . In conclusion, Oct-U-Net had a better diagnostic effect on 3D ultrasound images of fetal spina bifida, with higher segmentation accuracy and shorter running time, so it was worthy of clinical application.

Highlights

Fetal spina bifida is a common fetal congenital malformation, with a clinical incidence of about 0.1%, which is one of the main causes of neonatal death and disability [1]
Comparison on Recognition Accuracy of the ree Algorithms. 3D ultrasound examinations were performed on 3,300 pregnant women who participated in this study, and 2 ultrasound experts made judgments based on fetal ultrasound images
A total of 24 cases with spina bifida malformation were detected in the 3D ultrasound examinations. e 24 pregnant women were confirmed as fetal spina bifida during labor induction or after delivery in hospital. e diagnosis by ultrasound experts was correct, and the diagnosis accuracy rate was 100%. en, fully convolutional network (FCN), U-Net, and Oct-U-Net were adopted to judge the ultrasound images of all pregnant women, and the recall rate and precise rate of different algorithms were calculated

Summary

Introduction

Fetal spina bifida is a common fetal congenital malformation, with a clinical incidence of about 0.1%, which is one of the main causes of neonatal death and disability [1]. Open spina bifida will cause bulging of the contents of the fetal spinal canal, and the continuous interruption can be often observed in the skin of the lesion, often accompanied by back masses and cerebrospinal fluid exudation from the defect [5]. The detection rate of fetal spina bifida is closely related to the Scientific Programming operating experience of clinicians, fetal position, AFP, AchE, and MS-AFP [10]. 3D ultrasound examination is convenient, noninvasive, and economical and will not cause adverse effects on the mother and fetus It is an indispensable detection method in the prenatal examination of the mother [12]

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