Fetal Head and Abdomen Measurement Using Convolutional Neural Network, Hough Transform, and Difference of Gaussian Revolved along Elliptical Path (Dogell) Algorithm
This addresses fetal health monitoring in Indonesia to reduce neonatal deaths, but it is incremental as it combines existing methods like CNN and Hough transform with a new algorithm.
The paper tackled automated measurement of fetal head and abdomen circumferences from ultrasound images to detect abnormalities, achieving improved accuracy and speed with the Dogell algorithm compared to Hough transform.
The number of fetal-neonatal death in Indonesia is still high compared to developed countries. This is caused by the absence of maternal monitoring during pregnancy. This paper presents an automated measurement for fetal head circumference (HC) and abdominal circumference (AC) from the ultrasonography (USG) image. This automated measurement is beneficial to detect early fetal abnormalities during the pregnancy period. We used the convolutional neural network (CNN) method, to preprocess the USG data. After that, we approximate the head and abdominal circumference using the Hough transform algorithm and the difference of Gaussian Revolved along Elliptical Path (Dogell) Algorithm. We used the data set from national hospitals in Indonesia and for the accuracy measurement, we compared our results to the annotated images measured by professional obstetricians. The result shows that by using CNN, we reduced errors caused by a noisy image. We found that the Dogell algorithm performs better than the Hough transform algorithm in both time and accuracy. This is the first HC and AC approximation that used the CNN method to preprocess the data.