METODOLOGIA DE LOS MAPAS DE ACTIVIDAD ELECTRICA CEREBRAL

To test the efficacy of this template building approach on segmentation, Manjari Rao and I performed a study on the human kidney in CT images using m-rep deformable model segmentation. These results were compared to a previous study using a constant profile type per point, that of the Gaussian derivative. The right and left kidneys were segmented using different templates since the anatomical context is not the same on the two sides. Training of the templates was based on 52 greyscale images, with testing on 12 different CT scans. Most images had an in-plane resolution of 512× 512 with voxel dimensions of 0.98 mm × 0.98 mm and an inter-slice distance between 3 and 5 mm. The images were non-contrast enhanced, acquired in Radiation Oncology at UNC on a Somatom 4+, and the test images were windowed to make the kidney intensity consistent across images. In this section I present details of this study.

Expert manual segmentations were performed on the training images using the Plan- UNC interactive contouring tool (Tewell and Adams, 2004; Chaney, 2004). This resulted in a set of binary image files, bright for kidney and dark for non-kidney voxels. An m- rep model of the kidney was then automatically deformed into each binary, leaving a set of fitted m-rep models. The template used for the image match in these binaries consisted at each point of a positive Gaussian derivative profile, with the inflection at the boundary. This is an effective edge detector for high-contrast images. By then placing the fitted m-reps into the associated greyscales, the profiles were sampled at the same model boundary coordinates for each case.

The anatomical context of the kidney organ of the abdomen is somewhat consistent across cases. For example, the right kidney usually has the liver abutting or near the superior ventral lateral corner, and a rib supports the dorsal side. The context provides a wide variation in terms of image intensity regions across the surface of the kidney. In CT scans, bone and liver and spleen (the latter superior to the left kidney) all tend to have brightness similar to the kidney, which is in turn brighter than other surrounding

Figure 3.6: Top: kidneys colored according to the profile type selected at each point, using the same color scheme as before (see Figure 3.4). Bottom: the colored kidney model in situ in an axial CT slice. Note the grey-to-light type where the liver often abuts.

tissue. As well, in that these organs do not always abut, there can be darker regions between bright tissues. Thus as suggested in the previous section, one might expect there to be three profile types: from inside to outside the kidney, a light to dark (for most areas), a dark to light (for abutting liver and other bright tissue), and a light to dark to light again — a notch (Figure 3.1). As well, the three types can be found at similar position in the training kidneys, supporting our approach to find the best profile type to use over all cases, not for any particular case.

The three expected profile types were analytically modeled with positive and neg- ative Gaussian derivatives and a negative Gaussian. These seeds quickly converged to prominent observed profile types (Figure 3.4). The profile type per position on the sur- face was then chosen based on correlation of the training data with these cluster centers (see Section 3.3.2). Figure 3.6 shows the choice per point as a coloring on the surface of

Figure 3.7: Example segmentation results on three axial/coronal slice pairs. The dark contour is the result of the Gaussian derivative template, while the lighter contour comes from the clustered template.

the kidney. The types chosen have intuitive appeal, considering general anatomy. For example, on the right kidney, the profile type chosen to represent the boundary inten- sity region where the liver often abuts is the dark to light type. Where bright tissue is generally nearby (within the domain of the profiles), there is the notch type.

The left and right templates were then used in m-rep automatic segmentations of the test images. For each case, the m-rep model was placed in position initially by hand, and then it was deformed automatically using the posterior optimization described in Section 2.4. The test images had two expert manual segmentations of the left and right kidneys each. To compare the m-rep model to an expert segmentation of a test case, I used the compare byu tool described in Section 2.5.

3.4.1

Experimental results

Tables 3.1 and 3.2 summarize the median performances of the constant Gaussian deriva- tive template (Gaus) and clustered profile template (Prof) against the two experts re- spectively. The comparisons are made on the basis of volume overlap (VOL), which is the intersection over union, average surface distance (ASD), and the Hausdorff distance (HAUS).

Side GausVOL ProfVOL GausASD ProfASD GausHAUS ProfHAUS

Left 85.9% 84.7 1.25mm 1.48 13.3 8.30

Right 83.3 83.5 1.52 1.70 12.4 6.30

Table 3.1: Kidney median overlap, surface distance and Hausdorff distance per side when comparing against expert 1. The better value of each pair is shown in bold.

Side GausVOL ProfVOL GausASD ProfASD GausHAUS ProfHAUS

Left 84.9% 85.0 1.41mm 1.63 11.6 5.45

Right 79.9 86.0 1.95 1.53 11.5 4.95

Table 3.2: Kidney median overlap, surface distance and Hausdorff distance per side when comparing against expert 2.

Segmentation using the cluster template approach showed an improvement in 52% of the cases when considering average surface distance to the experts and in 58% of cases considering volume overlap. The average increase in the volume overlap of automatic and expert was 1.3%. There was a marked improvement in the Hausdorff maximum distance between the two templates, with an average improvement of over 5 mm. A possible explanation for this result is that in images where the liver or spleen abuts the kidney, the Gaussian derivative template forces the geometry to the nearest strong edge, which may be far away. Figure 3.7 shows several example results obtained with the two templates.

The major improvement in using the cluster template came qualitatively, in the degree of automation. In segmenting with a template of the Gaussian derivative profile type at every position, many cases required modified parameter settings in order to

achieve the best results. Such parameters include the relative weights of geometric typicality and image match (see Section 1.2). In contrast, all parameters were constant across the segmentations when the cluster template was used. The result is that slightly improved results were obtained with significantly less human input.