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摘要
Objective: Ultrasound fetal head circumference measurement is crucial for monitoring fetus growth and estimating the gestational age. Computer-aided measurement of fetal head circumference is valuable for sonographers who are short of experiments in ultrasound examinations. Through computer-aided measurement, they can further accurately detect fetal head edge and quickly finish an examination. Fetal head edge detection is necessary for the automatic measurement of fetal head circumference. Ultrasound fetal head image boundary is fuzzy, and the gray scale of fetal head is similar to the mother's abdominal tissue, especially in the first trimester. Ultrasound shadow leads to the loss of head edge and incomplete fetal head in the image, which brings certain difficulties in detecting the complete fetal head edge and fit head ellipse. The structures of the amniotic fluid and uterine wall are similar to the head texture and gray scale, often leading to misclassification of this part as fetal head. All these factors result in challenges to ultrasound fetal head edge detection. Therefore, we propose a method for detecting the ultrasound fetal head edge by using convolutional neural network to segment the fetal head region end-to-end. Method: The model proposed in this paper is based on UNet++. In deep supervised UNet++, every output is different and can provide a predicted result of the region of interest, but only the best predicted result will be used to predict the region of fetal head. Generally, the output results increase in accuracy from left to right. Four feature blocks exist before four outputs of UNet++. The left feature contains location information, and the right one contains sematic information. To utilize the feature map before outputs fully, we fuse them by concatenation and further extract fused features. The improved model is named Fusion UNet++. To prevent overfitting, we introduce spatial dropout after each convolutional layer instead of standard dropout, which extends the dropout value across the entire feature map. The idea of fetal head circumference measurement is as follows: first, we use Fusion UNet++ to learn the features of 2D ultrasound fetal head image and obtain the semantic segmentation result of the fetal head by using fetal head probability map. Second, on the basis of the image segmentation result, we extract the fetal head edge by using an edge detection algorithm and use the direct least square ellipse fitting method to fit the head contour. Finally, the fetal head circumference can be calculated using the ellipse circumference formula. Result: The open dataset of the automated measurement of fetal head circumference of the 2D ultrasound image named HC18 on Grand Challenges contains the first, second, and third trimester images of fetal heads. All fetal head images are the standard plane of measuring fetal head circumference. In the HC18 dataset, 999 2D ultrasound images have annotations of fetal head circumference in the train set, and 335 2D ultrasound fetal head images have no annotations in the test set. We use the train set to train the convolutional neural network and submit the predicted results of the test set to participate in the model evaluation on HC18, Grand Challenges. We use the Dice coefficient, Hausdorff distance (HD), and absolute difference (AD) as assessment indexes to evaluate the proposed method quantitatively. With the proposed method, for the dataset of fetal head images for all three trimesters, the Dice coefficient of the fetal head segmentation is 98.06%, the HD is 1.21±0.69 mm, and the AD of the fetal head circumference measurement is 1.84±1.73 mm. The skull in the second trimester is visible and appears as a bright structure; it is invisible in the first trimester and visible but incomplete in the third trimester. Seeing the complete skull is difficult in the first and third trimesters; thus, the measurement result of the fetal head circumference in the second trimester is the best among all trimesters. Most algorithms measure the fetal head circumference only in the second trimester or in the second and third trimester fetal head ultrasound images. For the second trimester, the Dice coefficient of the fetal head segmentation is 98.24%, the HD is 1.15±0.59 mm, and the AD of the fetal head circumference measurement is 1.76±1.55 mm. On the basis of the results presented in the open test set, our Dice ranked the 3rd, HD is the 2nd, and AD is the 10th. Conclusion: In comparison with the traditional and machine learning methods, the proposed method can effectively overcome the interference of fuzzy boundary and lack of edge and can accurately segment the fetal head region. In comparison with existing neural network methods, the proposed method surpasses the other methods in the second trimester of pregnancy in fetal head segmentation and head circumference measurement. The proposed method achieves the state-of-the-art results of fetal head segmentation.
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