A three-dimensional active shape model for the detection of anatomical landmarks on the back surface

Author(s):  
T Huysmans ◽  
R Van Audekercke ◽  
J Vander Sloten ◽  
H Bruyninckx ◽  
G Van der Perre

In this study relations between anatomical landmarks on the dorsal surface of the human torso corresponding to underlying skeletal structures are established. By examining the statistics of the positions of the landmarks in a training set of subjects a point distribution model is derived. Rotations of the pelvis are simulated in order to show that the main mode shapes of variation are consistent with rotations of the pelvis relative to the trunk. The parameters of these mode shapes can therefore be used as independent measures of clinical parameters such as pelvic inclination, pelvic tilt, etc. The point distribution model is further applied to improve reliability and robustness for an automatic and objective detection of the anatomical landmarks on the back surface (active shape model). The results show that it is possible to replace radiographs by surface measurements in order to measure position and orientation of the pelvis, which is particularly valuable in the case of functional examinations that normally involve a large number of radiographs (e.g. to measure the position of the pelvis in a scoliosis).

Author(s):  
Simant Prakoonwit

This article discusses a possible method to use a small number, e.g. 5, of conventional 2D X-ray images to reconstruct multiple 3D bone surfaces intraoperatively. Each bone's edge contours in X-ray images are automatically identified. Sparse 3D landmark points of each bone are automatically reconstructed by pairing the 2D X-ray images. The reconstructed landmark point distribution on a surface is approximately optimal covering main characteristics of the surface. A statistical shape model, dense point distribution model (DPDM), is then used to fit the reconstructed optimal landmarks vertices to reconstruct a full surface of each bone separately. The reconstructed surfaces can then be visualised and manipulated by surgeons or used by surgical robotic systems.


2009 ◽  
Vol 29 (10) ◽  
pp. 2710-2712 ◽  
Author(s):  
Li-qiang DU ◽  
Peng JIA ◽  
Zong-tan ZHOU ◽  
De-wen HU

2021 ◽  
Vol 69 ◽  
pp. 102807
Author(s):  
Yasser Ali ◽  
Soosan Beheshti ◽  
Farrokh Janabi-Sharifi

2003 ◽  
Author(s):  
Hans C. van Assen ◽  
Rob J. van der Geest ◽  
Mikhail G. Danilouchkine ◽  
Hildo J. Lamb ◽  
Johan H. C. Reiber ◽  
...  

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