Deep-learning-based fully automatic spine centerline detection in CT data

• Published in: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Vol. 2019; pp. 2407 - 2410
• Main Authors: Jakubicek, Roman, Chmelik, Jiri, Ourednicek, Petr, Jan, Jiri
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01-07-2019
• Subjects: Algorithms; Computed tomography; Deep Learning; Humans; Image segmentation; Implants; Irrigation; Neural Networks, Computer; Spinal cord; Spine; Spine - diagnostic imaging; Tomography, X-Ray Computed
• ISSN: 1557-170X; 1558-4615
• DOI: 10.1109/EMBC.2019.8856528

In this contribution, we present a fully automatic approach, that is based on two convolution neural networks (CNN) together with a spine tracing algorithm utilizing a population optimization algorithm. Based on the evaluation of 130 CT scans including heavily distorted and complicated cases, it turned out that this new combination enables fast and robust detection with almost 90% of correctly determined spinal centerlines with computing time of fewer than 20 seconds.