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Proceedings Paper

Automated classification and measurement of fetal ultrasound images with attention feature pyramid network
Author(s): Pengfei Liu; Huaici Zhao; Peixuan Li; Feidao Cao
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Paper Abstract

Segmentation of anatomical structures in ultrasound images required radiological technology and a great deal of ultrasonic experience. The manual segmentation is often dependent on expertise of clinicians and time-consuming. Therefore, we present an automatic system for segmentation and measurement of ultrasound images. We propose a scale attention feature pyramid network (SAFNet) for fetal biometric measurements from two-dimensional ultrasound images. The scale attention module is steered to form feature pyramid at each level. Auxiliary layer is used to learn object boundary definition with deep supervision. Further, we present a two-stage framework which is an automatic classification measurement system (ACMS), firstly classifies the image type which has three labels: head, abdomen and femur. Then outputs the final segmentation result. The SAFNet results better performance on our datasets compared to the baseline U-Net. Experiments show that the ACMS results in classification accuracy of 95.27%/90.94%/94.93% of fetal head, abdomen and femur test set, respectively. Feature pyramid and attention mechanism inside the network for feature selection results in improvement in the segmentation accuracy. The ACMS can conveniently obtain segmentation result no matter what type is given.

Paper Details

Date Published: 31 January 2020
PDF: 6 pages
Proc. SPIE 11427, Second Target Recognition and Artificial Intelligence Summit Forum, 114272R (31 January 2020);
Show Author Affiliations
Pengfei Liu, Shenyang Institute of Automation/Institutes for Robotics and Intelligent Manufacturing (China)
Univ. of Chinese Academy of Sciences (China)
Key Labs. of Opto-Electronic Information Processing/Image Understanding and Computer Vision (China)
Huaici Zhao, Shenyang Institute of Automation/Institutes for Robotics and Intelligent Manufacturing (China)
Key Labs. of Opto-Electronic Information Processing/Image Understanding and Computer Vision (China)
Peixuan Li, Shenyang Institute of Automation/Institutes for Robotics and Intelligent Manufacturing (China)
Univ. of Chinese Academy of Sciences (China)
Key Labs. of Opto-Electronic Information Processing/Image Understanding and Computer Vision (China)
Feidao Cao, Shenyang Institute of Automation/Institutes for Robotics and Intelligent Manufacturing (China)
Univ. of Chinese Academy of Sciences (China)
Key Labs. of Opto-Electronic Information Processing/Image Understanding and Computer Vision (China)


Published in SPIE Proceedings Vol. 11427:
Second Target Recognition and Artificial Intelligence Summit Forum
Tianran Wang; Tianyou Chai; Huitao Fan; Qifeng Yu, Editor(s)

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