Abstract
This paper develops a novel approach to find the plane in a 3D fetal ultrasound scan which corresponds to the 2D diagnostic plane used in cranial ultrasound of a neonate to allow image-based biomarkers to be tracked from pre-birth through the first weeks of post-birth life. We propose a method based on regression forests (RF) with important algorithm design considerations taken into account to provide an accurate plane-finding solution. Specifically, the new method constrains the RF method by 1) using informative voxels and voxel informative strength as a weighting within the training stage objective function u, and 2) introducing regularization of the RF by proposing a geometrical feature within the training stage. Results on clinical data indicate that the new automated method is more reproducible than manual plane finding.
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Yaqub, M., Kopuri, A., Rueda, S., Sullivan, P.B., McCormick, K., Noble, J.A. (2014). A Constrained Regression Forests Solution to 3D Fetal Ultrasound Plane Localization for Longitudinal Analysis of Brain Growth and Maturation. 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_14
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DOI: https://doi.org/10.1007/978-3-319-10581-9_14
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10580-2
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