Abstract
Accurate automatic detection of measurement points in ultrasound video sequences is challenging due to noise, shadows, anatomical differences, and scan plane variation. This paper proposes to address these challenges by a Fully Convolutional Neural Network (FCN) trained to regress the point locations. The series of convolutional and pooling layers is followed by a collection of upsampling and convolutional layers with feature forwarding from the earlier layers. The final location estimates are produced by computing the center of mass of the regression maps in the last layer. The temporal consistency of the estimates is achieved by a Long Short-Term memory cells which processes several previous frames in order to refine the estimate in the current frame. The results on automatic measurement of left ventricle in parasternal long axis view of the heart show detection errors below 5% of the measurement line which is within inter-observer variability.
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Sofka, M., Milletari, F., Jia, J., Rothberg, A. (2017). Fully Convolutional Regression Network for Accurate Detection of Measurement Points. In: Cardoso, M., et al. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support . DLMIA ML-CDS 2017 2017. Lecture Notes in Computer Science(), vol 10553. Springer, Cham. https://doi.org/10.1007/978-3-319-67558-9_30
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DOI: https://doi.org/10.1007/978-3-319-67558-9_30
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