Deformable organisms for automatic medical image analysis.
We introduce a new approach to medical image analysis that combines deformable model methodologies with concepts from the field of artificial life. In particular, we propose "deformable organisms", autonomous agents whose task is the automatic segmentation, labeling, and quantitative analysis of anatomical structures in medical images. Analogous to natural organisms capable of voluntary movement, our artificial organisms possess deformable bodies with distributed sensors, as well as (rudimentary) brains with motor, perception, behavior, and cognition centers. Deformable organisms are perceptually aware of the image analysis process. Their behaviors, which manifest themselves in voluntary movement and alteration of body shape, are based upon sensed image features, pre-stored anatomical knowledge, and a deliberate cognitive plan. We demonstrate several prototype deformable organisms based on a multiscale axisymmetric body morphology, including a "corpus callosum worm" that can overcome noise, incomplete edges, considerable anatomical variation, and interference from collateral structures to segment and label the corpus callosum in 2D mid-sagittal MR brain images.
McInerney
Tim
T
School of Computer Science, Ryerson University, Toronto, Ontario M5B 2K3, Canada. tmcinern@scs.ryerson.ca
Hamarneh
Ghassan
G
Shenton
Martha
M
Terzopoulos
Demetri
D
D000465:Algorithms; D003337:Corpus Callosum / Q000033:anatomy & histology* / Q000502:physiology; D005103:Expert Systems; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging; D008954:Models, Biological; D015233:Models, Statistical; D009068:Movement / Q000502:physiology; D012680:Sensitivity and Specificity