Automated classification and measurement of fetal ultrasound images with attention feature pyramid network

Pengfei Liu; Huaici Zhao; Peixuan Li; Feidao Cao

doi:10.1117/12.2552701

31 January 2020 Automated classification and measurement of fetal ultrasound images with attention feature pyramid network

Pengfei Liu, Huaici Zhao, Peixuan Li, Feidao Cao

Author Affiliations +

Proceedings Volume 11427, Second Target Recognition and Artificial Intelligence Summit Forum; 114272R (2020) https://doi.org/10.1117/12.2552701
Event: Second Target Recognition and Artificial Intelligence Summit Forum, 2019, Changchun, China

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.

Citation Download Citation

Pengfei Liu, Huaici Zhao, Peixuan Li, and Feidao Cao "Automated classification and measurement of fetal ultrasound images with attention feature pyramid network", Proc. SPIE 11427, Second Target Recognition and Artificial Intelligence Summit Forum, 114272R (31 January 2020); https://doi.org/10.1117/12.2552701

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