Manual segmentation gives the best and most reliable results when identifying structures for a particular clinical task. Usually, a medical practitioner with knowledge of anatomy utilizes a mouse-based software to outline or fill regions of target structures on each image slice in an image stack, i.e. a volume. The segmenter needs to mentally reconstruct a structure in 3D because only 2D image slices can be viewed. Segmentation of each slice requires that the segmenter view adjacent images in the stack in order to correctly reconstruct an object in 3D. The task is very tedious and time consuming for the segmenter, and thus does not serve the need of daily clinical use well. On the other hand, no fully automatic method exists that is able to provide comparable segmentation quality to manual segmentation. Our algorithm lends itself to a semi-automatic approach that is easy to use and fast to generate results, thus providing the segmenter a valuable tool to attain acceptable segmentation quality.
Figure 12 compares the performance of our approach in segmenting MRI volume datasets with manual segmentation performed by a medical technician. Using a segmentation software available from the National Institute of Health on the Apple Macintosh platform, the task required the segmenter many hours. To have a closer comparison, we display in Fig. 13 one horizontal section and its segmentation in Fig. 12. The sample section from the volume dataset is shown in Fig. 13a, and Fig. 13d shows the manually segmented brain for this section. Fig. 13b shows the segmented brain from the same sample image using G-LEGION. A simple post-processing step is then performed to remove the tiny noise artifacts that are collected in the background, and the result is then shown in Fig. 13c. A comparison between Fig. 13c and Fig. 13d reveals that the G-LEGION algorithm is able to provide more details that are omitted in the corresponding manual segmentation. For example, the brain ventricles and many fissures are correctly outlined in Fig. 13c by our algorithm, but are too tedious to outline in manual segmentation.