Literature DB >> 31259202

Atlas-based algorithm for automatic anatomical measurements in the knee.

Michael Brehler1, Gaurav Thawait2, Jonathan Kaplan3, John Ramsay3, Miho J Tanaka4, Shadpour Demehri2, Jeffrey H Siewerdsen1,2, Wojciech Zbijewski1.   

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.

Keywords:  anatomical landmarks; anatomical measurements; atlas; automatic measurement; image analysis; image registration

Year:  2019        PMID: 31259202      PMCID: PMC6582228          DOI: 10.1117/1.JMI.6.2.026002

Source DB:  PubMed          Journal:  J Med Imaging (Bellingham)        ISSN: 2329-4302


  34 in total

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4.  Diffeomorphic demons: efficient non-parametric image registration.

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5.  The geometry of the tibial plateau and its influence on the biomechanics of the tibiofemoral joint.

Authors:  Javad Hashemi; Naveen Chandrashekar; Brian Gill; Bruce D Beynnon; James R Slauterbeck; Robert C Schutt; Hossein Mansouri; Eugene Dabezies
Journal:  J Bone Joint Surg Am       Date:  2008-12       Impact factor: 5.284

6.  Automated identification of anatomical landmarks on 3D bone models reconstructed from CT scan images.

Authors:  K Subburaj; B Ravi; Manish Agarwal
Journal:  Comput Med Imaging Graph       Date:  2009-04-02       Impact factor: 4.790

7.  Multi-atlas-based segmentation with local decision fusion--application to cardiac and aortic segmentation in CT scans.

Authors:  Ivana Isgum; Marius Staring; Annemarieke Rutten; Mathias Prokop; Max A Viergever; Bram van Ginneken
Journal:  IEEE Trans Med Imaging       Date:  2009-01-06       Impact factor: 10.048

8.  Atlas-based recognition of anatomical structures and landmarks and the automatic computation of orthopedic parameters.

Authors:  J Ehrhardt; H Handels; W Plötz; S J Pöppl
Journal:  Methods Inf Med       Date:  2004       Impact factor: 2.176

9.  Automatic anatomical brain MRI segmentation combining label propagation and decision fusion.

Authors:  Rolf A Heckemann; Joseph V Hajnal; Paul Aljabar; Daniel Rueckert; Alexander Hammers
Journal:  Neuroimage       Date:  2006-07-24       Impact factor: 6.556

10.  Mindboggle: automated brain labeling with multiple atlases.

Authors:  Arno Klein; Brett Mensh; Satrajit Ghosh; Jason Tourville; Joy Hirsch
Journal:  BMC Med Imaging       Date:  2005-10-05       Impact factor: 1.930

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  1 in total

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Authors:  R Han; A Uneri; M Ketcha; R Vijayan; N Sheth; P Wu; P Vagdargi; S Vogt; G Kleinszig; G M Osgood; J H Siewerdsen
Journal:  Phys Med Biol       Date:  2020-07-17       Impact factor: 4.174

  1 in total

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