Knowledge-based interpretation of MR brain images

We have developed a method for fully automated segmentation and labeling of 17 neuroanatomic structures such as thalamus, caudate nucleus, ventricular system, etc, in magnetic resonance (MR) brain images, Our method is based on a hypothesize-and-verify principle and uses a genetic algorithm (GA) optimization technique to generate and evaluate image interpretation hypotheses in a feedback loop, Our method was trained in 20 individual T1-weighted MR images, Observer-defined contours of neuroanatomic structures were used as a priori knowledge. The method's performance was validated in eight MR images by comparison to observer-defined independent standards, The GA-based image interpretation method correctly interpreted neuroanatomic structures in all images from the test set, Computer-identified and observer-defined neuroanatomic structure areas correlated very well (r = 0.99, y = 0.95x - 2.1), Border positioning errors were small, with a root mean square (rms) border positioning error of 1.5 +/- 0.6 pixels. Our GA-based image interpretation method represents a novel approach to image interpretation and has been shown to produce accurate labeling of neuroanatomic structures in a set of MR brain images.