Automated measurement network for accurate segmentation and parameter modification in fetal head ultrasound images
- PDF / 2,366,463 Bytes
- 14 Pages / 595.224 x 790.955 pts Page_size
- 34 Downloads / 218 Views
ORIGINAL ARTICLE
Automated measurement network for accurate segmentation and parameter modification in fetal head ultrasound images Peixuan Li1,2,3,4,5 · Huaici Zhao1,2,4,5
· Pengfei Liu1,2,3,4,5 · Feidao Cao1,2,3,4,5
Received: 24 July 2019 / Accepted: 27 July 2020 © International Federation for Medical and Biological Engineering 2020
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 endto-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. Keywords Fetal head measurement · Ultrasound image segmentation · Fully convolutional networks · Feature pyramid · ROI pooling
1 Introduction Ultrasonic imaging is widely used in clinical examination since it does not use ionizing radiation and more lowcosting compared with computed tomography (CT) and magnetic resonance imaging (MRI), which make it to be the first choice of prenatal care. A clear and accurate
Huaici Zhao
[email protected] 1
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
anatomical structure measurement is required in many clinical ultrasound diagnoses. In particular, the fetal head measurement can be used to estimate gestational age and monitor growth patterns [1]. In general, these measurements are performed by experienced clinical sonographers on account of ultrasound images, which are operator-dependent and machine-specific [2] l
Data Loading...
