Abstract
Acquisition of the fetal abdominal standard plane (FASP) is crucial for prenatal ultrasound diagnosis. However, it requires a thorough knowledge of human anatomy and substantial experience. In this paper, we propose an automatic method to localize the FASP from US images. Unlike the previous methods that consider simple low-level features such as Haar features, we exploited the deep convolutional neural network to automatically learn the latent representation. In addition, we adopted the novel knowledge transfer method to enhance the learning performance by making use of the knowledge obtained in other domain. Experimental results on 219 fetal abdomen videos showed that the classification accuracy of our method was above 90%, outperforming other methods by a significant margin.
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Chen, H., Ni, D., Yang, X., Li, S., Heng, P.A. (2014). Fetal Abdominal Standard Plane Localization through Representation Learning with Knowledge Transfer. In: Wu, G., Zhang, D., Zhou, L. (eds) Machine Learning in Medical Imaging. MLMI 2014. Lecture Notes in Computer Science, vol 8679. Springer, Cham. https://doi.org/10.1007/978-3-319-10581-9_16
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DOI: https://doi.org/10.1007/978-3-319-10581-9_16
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10580-2
Online ISBN: 978-3-319-10581-9
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