Abstract
Ultrasound is widely used for fetal screening. It allows for detecting abnormalities at an early gestational age, while being time and cost effective with no known adverse effects. Searching for optimal ultrasound planes for these investigations is a demanding and time-consuming task. Here we describe a method for automatically detecting the spine centerline in 3D fetal ultrasound images. We propose a two-stage approach combining deep learning and classic image processing techniques. First, we segment the spine using a deep learning approach. The resulting probability map is used as input for a tracing algorithm. The result is a sequence of points describing the spine centerline. This line can be used for measuring the spinal length and for generating view planes for the investigation of anomalies.
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References
Papageorghiou AT, et al. International standards for fetal growth based on serial ultrasound measurements: The fetal growth longitudinal study of the INTERGROWTH-21st project. The Lancet. 2014;384:869–879.
Lorenz C, et al. Automated abdominal plane and circumference estimation in 3D US for fetal screening. Procs SPIE. 2018;10574:105740I.
Upasani V, et al. Prenatal diagnosis and assessment of congenital spinal anomalies: Review for prenatal counseling. World J Orthop. 2016;7:406–417.
Brosch T, Saalbach A. Foveal fully convolutional nets for multi-organ segmentation. Procs SPIE. 2018;10574:105740U.
Lenga M, et al. Deep learning based rib centerline extraction and labeling. Lect Notes Computer Sci. 2018;11404:99–113.
Ulm M, et al. Ultrasound evaluation of fetal spine length between 14 and 24 weeks of gestation. PND. 1999;19:637–641.
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© 2021 Der/die Autor(en), exklusiv lizenziert durch Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
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Franz, A., Schmidt-Richberg, A., Orasanu, E., Lorenz, C. (2021). Deep Learning-based Spine Centerline Extraction in Fetal Ultrasound. In: Palm, C., Deserno, T.M., Handels, H., Maier, A., Maier-Hein, K., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2021. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-33198-6_63
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DOI: https://doi.org/10.1007/978-3-658-33198-6_63
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