Automatic segmentation of the ventricular system from MR images of the human brain.

ABSTRACT

An algorithm was developed that automatically segments the lateral and third ventricles from T1-weighted 3-D-FFE MR images of the human brain. The algorithm is based upon region-growing and mathematical morphology operators and starts from a coarse binary total brain segmentation, which is obtained from the 3-D-FFE image. Anatomical knowledge of the ventricular system has been incorporated into the method in order to find all constituting parts of the system, even if they are disconnected, and to avoid inclusion of nonventricle cerebrospinal fluid (CSF) regions. A test of the method on a synthetic MR brain image produced a segmentation overlap of 0.98 between the simulated ventricles ("model") and those defined by the algorithm. Further tests were performed on a large data set of 227 1.5 T MR brain images. The algorithm yielded useful results for 98% of the images. The automatic segmentations had intra-class correlation coefficients of 0.996 for the lateral ventricles and 0.86 for the third ventricle, with manually edited segmentations. Comparison of ventricular volumes of schizophrenia patients compared with those of healthy control subjects showed results in agreement with the literature.