Spinal crawlers: deformable organisms for spinal cord segmentation and analysis.
Spinal cord analysis is an important problem relating to the study of various neurological diseases. We present a novel approach to spinal cord segmentation in magnetic resonance images. Our method uses 3D "deformable organisms" (DefOrg) an artificial life framework for medical image analysis that complements classical deformable models (snakes and deformable meshes) with high-level, anatomically-driven control mechanisms. The DefOrg framework allows us to model the organism's body as a growing generalized tubular spring-mass system with an adaptive and predominantly elliptical cross section, and to equip them with spinal cord specific sensory modules, behavioral routines and decision making strategies. The result is a new breed of robust DefOrgs, "spinal crawlers", that crawl along spinal cords in 3D images, accurately segmenting boundaries, and providing sophisticated, clinically-relevant structural analysis. We validate our method through the segmentation of spinal cords in clinical data and provide comparisons to other segmentation techniques.
McIntosh
Chris
C
Medical Image Analysis Lab, School of Computing Science, Simon Fraser University, BC, Canada. cmcintos@cs.sfu.ca
Hamarneh
Ghassan
G
D000465:Algorithms; D001185:Artificial Intelligence; D032701:Biomimetics / Q000379:methods; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013116:Spinal Cord / Q000166:cytology*