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19 June 2019 Atlas-based algorithm for automatic anatomical measurements in the knee
Michael Brehler, Gaurav Thawait, Jonathan Kaplan, John Ramsay, Miho J. Tanaka, Shadpour Demehri, Jeffrey H. Siewerdsen, Wojciech Zbijewski
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Abstract
We present an algorithm for automatic anatomical measurements in tomographic datasets of the knee. The algorithm uses a set of atlases, each consisting of a knee image, surface segmentations of the bones, and locations of landmarks required by the anatomical metrics. A multistage volume-to-volume and surface-to-volume registration is performed to transfer the landmarks from the atlases to the target volume. Manual segmentation of the target volume is not required in this approach. Metrics were computed from the transferred landmarks of a best-matching atlas member (different for each bone), identified based on a mutual information criterion. Leave-one-out validation of the algorithm was performed on 24 scans of the knee obtained using extremity cone-beam computed tomography. Intraclass correlation (ICC) between the algorithm and the expert who generated atlas landmarks was above 0.95 for all metrics. This compares favorably to inter-reader ICC, which varied from 0.19 to 0.95, depending on the metric. Absolute agreement with the expert was also good, with median errors below 0.25 deg for measurements of tibial slope and static alignment, and below 0.2 mm for tibial tuberosity-trochlear groove distance and medial tibial depth. The automatic approach is anticipated to improve measurement workflow and mitigate the effects of operator experience and training on reliability of the metrics.
© 2019 Society of Photo-Optical Instrumentation Engineers (SPIE) 2329-4302/2019/$25.00 © 2019 SPIE
Michael Brehler, Gaurav Thawait, Jonathan Kaplan, John Ramsay, Miho J. Tanaka, Shadpour Demehri, Jeffrey H. Siewerdsen, and Wojciech Zbijewski "Atlas-based algorithm for automatic anatomical measurements in the knee," Journal of Medical Imaging 6(2), 026002 (19 June 2019). https://doi.org/10.1117/1.JMI.6.2.026002
Received: 20 December 2018; Accepted: 4 June 2019; Published: 19 June 2019
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Cited by 2 scholarly publications.
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