Lumbar disc localization and labeling with a probabilistic model on both pixel and object features.
Repeatable, quantitative assessment of intervertebral disc pathology requires accurate localization and labeling of the lumbar region discs. To that end, we propose a two-level probabilistic model for such disc localization and labeling. Our model integrates both pixel-level information, such as appearance, and object-level information, such as relative location. Utilizing both levels of information adds robustness to the ambiguous disc intensity signature and high structure variation. Yet, we are able to do efficient (and convergent) localization and labeling with generalized expectation-maximization. We present accurate results on 20 normal cases (96%) and a promising extension to a pathology case.
Corso
Jason J
JJ
Department of Computer Science and Engineering, University at Buffalo, State University of New York, USA. jcorso@cse.buffalo.edu
Alomari
Raja S
RS
Chaudhary
Vipin
V
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007403:Intervertebral Disc / Q000473:pathology*; D007405:Intervertebral Disc Displacement / Q000473:pathology*; D008159:Lumbar Vertebrae / Q000473:pathology*; D008954:Models, Biological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity