Landmark-Based Software for Anatomical Measurements: A Precision Study
2009 (English)In: Clinical anatomy (New York, N.Y. Print), ISSN 0897-3806, E-ISSN 1098-2353, Vol. 22, no 4, 456-462 p.Article in journal (Refereed) Published
The aim of this study was to develop a software program, called Landmarker, which would aid studies of complex anatomical morphometry by simplifying the manual identification of landmarks in 3D images. We also tested its precision on routine magnetic resonance imaging (MRI) scans. To understand human biological variation, there is a need to identify morphological characteristics from the exterior and the interior of human anatomy. MRI, as opposed to other radiographic methods (mainly based on X-ray techniques), supplies good soft tissue contrast, which allows for more complex assessments than what bony landmarks can provide. Because automation of this assessment is highly demanding, one of the primary goals for the new software was to enable more rapid identification of landmark sets in 3D image data. Repeat acquisition of head MRIs having a resolution of 0.94 x 0.94 x 1.20 mm3 were performed on 10 volunteers. Intra- and interoperator, as well as interacquisition variations of manual identification of exterior, craniofacial interior, and brain landmarks were studied. The average distances between landmarks were <1.8 mm, <2.3 mm, and <2.0 mm in the intra- and interoperator, and interacquisition evaluations, respectively. This study presents new software for time efficient identification of complex craniofacial landmarks in 3D MRI. To the best of our knowledge, no evaluation of software for rapid landmark-based analysis of complex anatomies from 3D MR data has yet been presented. This software may also be useful for studies in other anatomical regions and for other types of image data.
Place, publisher, year, edition, pages
2009. Vol. 22, no 4, 456-462 p.
morphometry, 3D MRI, shape analysis, craniofacial
Medical and Health Sciences
IdentifiersURN: urn:nbn:se:uu:diva-102340DOI: 10.1002/ca.20793ISI: 000265721800004PubMedID: 19306317OAI: oai:DiVA.org:uu-102340DiVA: diva2:214724