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Automated measurement network for accurate segmentation and parameter modification in fetal head ultrasound images
Li PX(李培玄)1,2,3,4,5; Zhao HC(赵怀慈)1,2,4,5; Liu PF(刘鹏飞)1,2,3,4,5; Cao FD(曹飞道)1,2,3,4,5
Department光电信息技术研究室
Source PublicationMEDICAL & BIOLOGICAL ENGINEERING & COMPUTING
ISSN0140-0118
2020
Volume58Issue:11Pages:2879-2892
Indexed BySCI ; EI
EI Accession number20204009252533
WOS IDWOS:000572723300002
Contribution Rank1
KeywordFetal head measurement Ultrasound image segmentation Fully convolutional networks Feature pyramid ROI pooling
Abstract

Measurement of anatomical structures from ultrasound images requires the expertise of experienced clinicians. Moreover, there are artificial factors that make an automatic measurement complicated. In this paper, we aim to present a novel end-to-end deep learning network to automatically measure the fetal head circumference (HC), biparietal diameter (BPD), and occipitofrontal diameter (OFD) length from 2D ultrasound images. Fully convolutional neural networks (FCNNs) have shown significant improvement in natural image segmentation. Therefore, to overcome the potential difficulties in automated segmentation, we present a novelty FCNN and add a regression branch for predicting OFD and BPD in parallel. In the segmentation branch, a feature pyramid inside our network is built from low-level feature layers for a variety of fetal head in ultrasound images, which is different from traditional feature pyramid building methods. In order to select the most useful scale and reduce scale noise, attention mechanism is taken for the feature's filter. In the regression branch, for the accurate estimation of OFD and BPD length, a new region of interest (ROI) pooling layer is proposed to extract the elliptic feature map. We also evaluate the performance of our method on large dataset: HC18. Our experimental results show that our method can achieve better performance than the existing fetal head measurement methods.

Language英语
WOS SubjectComputer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Mathematical & Computational Biology ; Medical Informatics
WOS KeywordCHARTS
WOS Research AreaComputer Science ; Engineering ; Mathematical & Computational Biology ; Medical Informatics
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/27688
Collection光电信息技术研究室
Corresponding AuthorZhao HC(赵怀慈)
Affiliation1.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
3.University of Chinese Academy of Sciences, Beijing 100049, China
4.Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang 110016, China
5.Key Lab of Image Understanding and Computer Vision, Liaoning Province, Shenyang 110016, China
Recommended Citation
GB/T 7714
Li PX,Zhao HC,Liu PF,et al. Automated measurement network for accurate segmentation and parameter modification in fetal head ultrasound images[J]. MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING,2020,58(11):2879-2892.
APA Li PX,Zhao HC,Liu PF,&Cao FD.(2020).Automated measurement network for accurate segmentation and parameter modification in fetal head ultrasound images.MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING,58(11),2879-2892.
MLA Li PX,et al."Automated measurement network for accurate segmentation and parameter modification in fetal head ultrasound images".MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 58.11(2020):2879-2892.
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