Purpose: Fetal head circumference (HC) is an important biometric parameter that can be used to assess fetal development in obstetric clinical practice. Most of the existing methods use deep neural network to accomplish the task of automatic fetal HC measurement from two-dimensional ultrasound images, and some of them achieved relatively high prediction accuracy. However, few of these methods focused on optimizing model efficiency performance. Our purpose is to develop a more efficient approach for this task, which could help doctors measure HC faster and would be more suitable for deployment on devices with scarce computing resources.
Methods: In this paper, we present a very lightweight deep convolutional neural network to achieve automatic fetal head segmentation from ultrasound images. By using sequential prediction network architecture, the proposed model could perform much faster inference while maintaining a high prediction accuracy. In addition, we used depthwise separable convolution to replace part of the standard convolution in the network and shrunk the input image to further improve model efficiency. After getting fetal head segmentation results, post-processing, including morphological processing and least-squares ellipse fitting, was applied to obtain the fetal HC. All experiments in this work were performed on a public dataset, HC18, with 999 fetal ultrasound images for training and 335 for testing. The dataset is publicly available on https://hc18.grand-challenge.org/ and the code for our method is also publicly available on https://github.com/ApeMocker/CSM-for-fetal-HC-measurement.
Results: Our model has only 0.13 million [M] parameters and can achieve an inference speed of 28 [ms] per frame on a CPU and 0.194 [ms] per frame on a GPU, which far exceeds all existing deep learning-based models as far as we know. Experimental results showed that the method achieved a mean absolute difference of 1.97( ± 1.89) [mm] and a Dice similarity coefficient of 97.61( ± 1.72) [%] on HC18 test set, which were comparable to the state of the art.
Conclusion: We presented a very lightweight deep learning-based model to realize fast and accurate fetal head segmentation from two-dimensional ultrasound image, which is then used for calculating the fetal HC. The proposed method could help obstetricians measure the fetal HC more efficiently with high accuracy, and has the potential to be applied to the situations where computing resources are relatively scarce.
Keywords: deep neural network; fetal head circumference; lightweight architecture; ultrasound image.
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