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

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 $\left(P<0.05\right)$ . 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 $\left(P<0.05\right)$ . 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.