Automatic Measurement Method for Fetal Head Circumference Based on Convolution Neural Network

doi:10.12068/j.issn.1005-3026.2023.11.008

Abstract

Abstract: In prenatal ultrasound screening， in order to help doctors measure the fetal head circumference quickly and accurately on the standard plane of the thalamus， a novel two-branch convolution neural network is proposed to directly segment the fetal skull boundary. The two branches promote each other through the shared layer， which can improve the segmentation accuracy of the skull boundary effectively. In particular， the proposed method has good segmentation effects and high robustness for locally unclear or discontinuous boundaries. Furthermore， the measurement process of the proposed method does not require excessive post-processing operations， and the model belongs to a lightweight network， which is easy to deploy. Good results were achieved on the HC18 dataset of Grand-Challenge and 300 cases collected from hospitals. Compared with other mainstream segmentation networks such as U-Net， Res-U-Net， U-Net++， CE-Net， etc.， the proposed method is with higher segmentation accuracy and smaller measurement error.

Key words: prenatal ultrasound; head circumference measurement; boundary segmentation; convolution neural network; lightweight

CLC Number:

TP391

YANG Chao-ran， LIAO Shan-shan， CHEN Da， KANG Yan. Automatic Measurement Method for Fetal Head Circumference Based on Convolution Neural Network[J]. Journal of Northeastern University(Natural Science), 2023, 44(11): 1571-1577.

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