基于Mask R-CNN的超声图像中胎儿头围测量方法

doi:10.3969/j.issn.0258-8021.2021.01.02

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Abstract Fetal head circumference is one of the most important biological characteristics in prenatal ultrasound evaluation of fetal growth and development. However, manual measurement is time-consuming and labor-consuming and may have errors by the operator. According to the feature of fetal head close to ellipse shape in ultrasound image, the head circumference measurement loss function was proposed in this paper. After the segmentation branch of Mask R-CNN, ElliFit algorithm was used to fit the ellipse of the segmentation mask. Ramanujan formula was used to calculate the fitting ellipse circumference as the measurement value of the head circumference. The mean square error of the real value of the head circumference and the measurement value was added into the original loss function as head circumference measurement loss function to allow the training process of the model to be closely related to the measurement task. By this way the measurement accuracy and speed was improved. One hundred and ninety ultrasound images of fetal head were tested. Dice’s coefficient was 96.89%±1.01%, and the measurement error was (0.33±1.54) mm. The average processing time of one ultrasound image was 0.33 s. Compared with the traditional manual measurement method or the current machine learning methods, the proposed method improved the speed between 1.13 seconds and 16.87 seconds, and improved the accuracy between 0.21 mm and 1.68 mm. The results showed that the improved Mask R-CNN increased the efficiency of doctors in measuring fetal head circumference, which met the clinical needs.

Key words： ultrasound image Mask R-CNN ellipse fitting fetal head circumference measurement loss function

Received: 10 December 2019

PACS:

R318

Corresponding Authors: *E-mail: jhzhang@ynu.edu.cn

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	Li Zonggui
	Zhang Junhua
	Mei Liye

Cite this article:

Li Zonggui,Zhang Junhua,Mei Liye. Method for Measuring Fetal Head Circumference in Ultrasound Images Based on Mask R-CNN[J]. Chinese Journal of Biomedical Engineering, 2021, 40(1): 12-18.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2021.01.02 OR http://cjbme.csbme.org/EN/Y2021/V40/I1/12

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