Automating analyses of the distal femur articular geometry based on three-dimensional surface data.
Quantitative knowledge of the distal femur morphology is critical to understanding the relation between the anatomy and function of the knee joint. Prior knowledge was contaminated by manual procedures and subjective visual inspections in extracting geometric information from image data. This article proposes a new computational framework to enable automated analysis of the distal femur articular geometry based on 3D surface data. The framework consists of a pattern recognition algorithm for sectioning the sagittal-view condyle profiles, a least-squares algorithm for fitting and analyzing the profiles, and an optimization algorithm for establishing a unified sagittal plane. An application of the proposed framework to 12 knee surface models demonstrated that it can analyze the condyle contour profiles and extract geometric measures automatically and accurately. The proposed framework also facilitated a simulation-based analysis of the uncertainty associated with conventional manual approaches, elucidating how subjective determination of the sagittal plane and flexion facet can hinder accurate understanding of the distal femur morphology and related kinematics.
Li
Kang
K
Department of Orthopaedic Surgery, University of Pittsburgh, PA 15203, USA.
Tashman
Scott
S
Fu
Freddie
F
Harner
Christopher
C
Zhang
Xudong
X
D000328:Adult; D016118:Anterior Cruciate Ligament / Q000033:anatomy & histology / Q000000981:diagnostic imaging; D000070598:Anterior Cruciate Ligament Injuries; D003198:Computer Simulation; D005260:Female; D005269:Femur / Q000033:anatomy & histology / Q000000981:diagnostic imaging*; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D007717:Knee / Q000033:anatomy & histology / Q000000981:diagnostic imaging; D016018:Least-Squares Analysis; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D011859:Radiography; D055815:Young Adult