A Deep Convolutional
Neural Network based Hybrid Framework for Fetal Head Standard Plane
Identification
Jingyu Ye1*, Ruizhi Liu2*, Bing
Zou1*, Hongyang Zhang2, Nianji
Zhan1, Cong Han2, Ying
Yang1,3, Hongguo Zhang2, Jian
Guo1, Fang Chen1,3, Shida
Zhu1#, Shucheng Hua2#
1 BGI-Shenzhen, Shenzhen 518083, China;2 Reproductive Medicine & Prenatal Diagnosis Center,
The First Hospital, Jilin University, Changchun, China.;3 Shenzhen Engineering Laboratory for Birth Defects
Screening, BGI-Shenzhen, Shenzhen 518083, China;*These authors contributed equally;#Correspondence author: Shucheng Hua, The First
Hospital, Jilin University, No.71 Xinmin Street, Changchun 130021,
China. Tel. +86043188782707. Email: shuchenghua@126.com; Shida Zhu,
BGI-Shenzhen, Building 11, Beishan Industrial Zone, Yantian District,
Shenzhen 518083, China. Tel. +8615814003823. Email: zhushida@bgi.com
Abstract :
Objective To develop more effective deep convolutional neural
network based technology for automatic identify five types of fetal head
standard view planes, i.e., Transventricular plane (TV), Transthalamic
plane (TT), Transcerebellar plane (TC), Coronal view of eyes (Eyes),
Coronal view of nose (Nose), and other non-standard fetal ultrasound
images (Background) in ultrasound images.
Design Randomly select samples for the research including the
abnormal fetuses.
Setting Historical records of prenatal examinations from The
First Hospital of Jilin University.
Samples 19928 2-D fetal ultrasound images of fetuses with the
gestational age ranging from 18 weeks to 31 weeks.
Methods A novel convolutional neural network based hybrid
framework is designed for automatic identification of standard fetal
head view planes in ultrasound images which is an important topic of
fetal screening and diagnosis. In the proposed framework, YOLO-V3 is
applied for locating possible fetal head region in the candidate image.
Then, a group of object classification networks which includes ResNet50,
ResNeXt50, InceptionResNet-V2, and SonoNet64 are employed to make
predictions on the located fetal head region. The predictions of the
classification models are stacked so as to generate the final prediction
for the suspected fetal head region, and the original image as well.
Main Outcome Measures The average precision, average recall,
average F-1 score, and the average accuracy on identifying six
categories of fetal ultrasound images for each method.
Results The proposed deep convolution neural network based
framework achieves the average precision of 89.67%, the average recall
of 89.61%, the average F-1 score of 89.61%, and the average accuracy
of 89.61% which demonstrates the state-of-the-art performance. The
average AUC is 0.9893.
Conclusions The experimental results indicate that the proposed
framework is effective on identifying fetal head standard view planes in
ultrasound images. Since the experiments are designed to reproduce the
scenario happened in the real life, the proposed method could be
potentially applied to the automatic fetal screening and diagnosis.
Keywords : fetal ultrasound screening, deep convolutional neural
network, fetal head standard plane, model stacking.