The accuracy of a segmentation algorithm can be described by the percentage of voxels correctly classified as inside of the object (TPF or sensitivity), the fraction of voxels falsely classified as outside the object (FNF), and the fraction of voxels falsely classified as inside the object (FPF or 1-specificity).

For our experiments we used reconstructions from selectively stained, plastic embedded spiny dendrites (; ) (a well-known biological structure). The operators initially created two sets of seed points corresponding to the foreground and background, respectively. However, after inspecting the results it became obvious that either new object classes or new seed points were necessary. Image (a) shows a plane of the reconstruction number 2 where artifacts are visible. Image (b) presents the result, at the same plane as (a), of using two classes where some of the artifacts were classified as objects (pointed by red arrows). Image (c) presents the final result, at the same plane as (a), in which three classes were used (two foreground classes and background) and no artifacts were included. Image (d) shows a thresholded version of the reconstruction in (a) at an appropriate threshold. Finally, (e) shows the manual delineation for such a plane.

Visualization of the same plane for (a) the original reconstruction number 9, (b) the result obtained by thresholding the reconstruction, (c) the result obtained by the first operator, and (d) the result obtained by the second operator.

The plots in this figure represent the true-positive and false-positive fractions, produced by and . The upper row represents the results for face (ω) and face-edge (β) adjacencies for foreground and background classes, respectively. The bottom row represents the results for foreground and background classes using face (ω) adjacency. The diamonds and circles represent the results for the first and second operators, respectively.

The plots in this figure represent the true positive and false positive fractions, produced by the STAPLE method (). The upper row represents the results for face (ω) and face-edge (β) adjacencies for foreground and background classes, respectively. The bottom row represents the results of using face (ω) adjacency for both foreground and background objects.

Manual delineation suffers from user bias and non-reproducibility, this is demonstrated in these images. Three operators segmented the same object and we created an average of such delineations. The darker the gray level value, the more disagreement between operators. We can see that in this case, expert operators greatly disagreed in what should be included in an object.

The plots in this figure represent the precision, produced by , for all the original seed points with respect to randomly modified seed points (see the text for details of how the seed points were modified). The upper row represents the results for face (ω) and face-edge (β) adjacencies for foreground and background classes, respectively. The bottom row represents the results for foreground and background classes using face (ω) adjacency.

The plots in this figure represent the precision, produced by , for all data sets. The left image represents the results for foreground and background classes using face (ω) adjacency. The right image represents the results for face (ω) and face-edge (β) adjacencies for foreground and background classes, respectively.