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)2,3,4,5; Liu PF(刘鹏飞)1,2,3,4,5; Cao FD(曹飞道)1,2,3,4,5 | |
刊名 | MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING |
2020 | |
卷号 | 58期号:11页码:2879-2892 |
关键词 | Fetal head measurement Ultrasound image segmentation Fully convolutional networks Feature pyramid ROI pooling |
ISSN号 | 0140-0118 |
产权排序 | 1 |
英文摘要 | 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. |
WOS关键词 | CHARTS |
WOS研究方向 | Computer Science ; Engineering ; Mathematical & Computational Biology ; Medical Informatics |
语种 | 英语 |
WOS记录号 | WOS:000572723300002 |
内容类型 | 期刊论文 |
源URL | [http://ir.sia.cn/handle/173321/27688] |
专题 | 沈阳自动化研究所_光电信息技术研究室 |
通讯作者 | Zhao HC(赵怀慈) |
作者单位 | 1.University of Chinese Academy of Sciences, Beijing 100049, China 2.Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang 110016, China 3.Key Lab of Image Understanding and Computer Vision, Liaoning Province, Shenyang 110016, China 4.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 5.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China |
推荐引用方式 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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