1998 - Draper Laboratory

asymmetry, volume changes, etc.). Second, we obtained,courtesy of Massachusetts General Hospital’s Center forMorphometric Analysis, a set of MR images along withcorresponding hand segmentations by human experts(neuroanatomists). Although we will, in this section, refer to thishand segmentation as the “truth,” this is in fact a somewhatunfair procedure since the human experts clearly use moreinformation than simply image data. More specifically, there areoccasions in which the human experts have placed regionboundaries where we, as nonexperts, would not (for examplealong the bilateral symmetry line separating the two parts of thelateral ventricle when no such separation is visible in the imagedata). As such, some degree of discrepancy between oursegmentation algorithm and the human segmentation is to beexpected. Furthermore, in the discussions that follow, we willquantify our results with respect to two criteria: the handsegmentation results from human experts, and the handsegmentation results (as obtained by nonexperts ofneuroanatomy) based exclusively on image data (referred to asthe “image-based manual segmentation” below).Figure 4 shows the results of our algorithm as applied to theextraction of the lateral ventricles via an actual MR image. Theatlas used in the processing was derived from another brain.Figure 4(a) shows a close-up of a T 1 MR image of the region ofinterest. Figure 4(b) shows a dimmed version of the samegrayscale MR image, but with the human expert segmentationsuperimposed. Figure 4(c) shows a similar view, but oursegmentation superimposed. In all, the algorithm seems to havedone a reasonable job of delineating the relevant features,although both expert segmentation and our algorithm missed --in a purely image intensity sense -- the very tip of the ventricles.Figure 5 shows the same information as Figure 4, but as appliedto the caudate. In this case, the algorithm did not segment thetail structure (bottom of the caudate) that is present in thehuman expert segmentation. And in fact, this tail structure isvery difficult to identify with the untrained eye. The algorithmdid, however, err by converging to the dominant edge at the topof the ventricle instead of the lower contrast edge that separatesthe ventricle from the caudate.Based on our (small) sample of brains with two-dimensionalcoronal slices and corresponding human expert segmentations,the result of our algorithm is summarized in Table 1. Specifically,Table 1 shows the average absolute error in the ventricular andcaudate areas across all slices and all brains in our sample. Theerrors are expressed in percentages, computed with respect to thearea of the “true” segmentations performed by humans. The firstcolumn of data shows our error with respect to the human expertsegmentation; the second data column shows our error withFigure 4. Segmentation algorithm applied to MR data of ventricles: (a) MR T 1 ; (b) hand segmentation results byneuroanatomists superimposed on the MR image; (c) the result of our segmentation algorithm.Figure 5. Segmentation algorithm applied to MR data of the caudate: (a) MR T 1 ; (b) hand segmentation resultsby neuroanatomists superimposed on the MR image; (c) the result of our segmentation algorithm.Segmentation of MR Images Using Curve Evolution and Prior7