Evaluation on BSDS500. Higher is better for all measures except VI, for which lower is better.

<p>ODS uses the optimal scale for the entire dataset while OIS uses the optimal scale for each image.</p

Agglomerative learning improves merge probability estimates during agglomeration.

<p>(Flat learning is equivalent to 0 agglomerative training epochs.) (a) VI as a function of threshold for mean, flat learning, and agglomerative learning (5 epochs). Stars indicate minimum VI, circles indicate VI at . (b) VI as a function of the number of training epochs. The improvement in minimum VI afforded by agglomerative learning is minor (though significant), but the improvement at is much greater, and the minimum VI and VI at are very close for 4 or more epochs.</p

Example segmentations on natural images.

<p><i>Top row</i>: Despite having a very noisy boundary map, using additional cues allows us to segment the objects successfully. <i>Middle row</i>: Although there are many weak edges, region-based texture information helps give a correct segmentation. <i>Bottom row</i>: A failure case, where the similar texture of elephants causes them to be merged even though a faint boundary exists between them. For all rows, the VI ODS threshold was used. The rows correspond top to bottom to the points identified in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0071715#pone-0071715-g007" target="_blank">Figure 7</a>.</p

Evaluation of segmentation algorithms on BSDS500.

<p>Left: split-VI plot. Stars represent optimal VI (minimum sum of x and y axis), circles represent VI at threshold . Right: boundary precision-recall plot.</p

Split VI plot for different learning or agglomeration methods.

<p>Shaded areas correspond to mean standard error of the mean. “Best” segmentation is given by optimal agglomeration of superpixels by comparing to the gold standard segmentation. This point is not because the superpixel boundaries do not exactly correspond to those used to generate the gold standard. The standard deviation of this point () is smaller than the marker denoting it. Stars mark minimum VI (sum of false splits and false merges), circles mark VI at threshold 0.5.</p

Schematic of our approach.

<p><i>First column:</i> A 2D image has a given gold standard segmentation , a superpixel map (which induces an initial region adjacency graph, ), and a “best” agglomeration given that superpixel map <i>A</i>*. <i>Second column:</i> Our procedure gives training sets at all scales. “f” denotes a feature map. denotes graph agglomerated by policy after merges. Note that only increases when we encounter an edge labeled . <i>Third column:</i> We learn by simultaneously agglomerating and comparing against the best agglomeration, terminating when our agglomeration matches it. The highlighted region pair is the one that the policy, , determines should be merged next, and the color indicates the label obtained by comparing to <i>A</i>*. After each training epoch, we train a new policy and undergo the same learning procedure. For clarity, in the second and third columns, we abbreviate with just the index in the second and third arguments to the feature map. For example, indicates the feature map from graph and edge , corresponding to regions and .</p

Representative 3D EM data and sample reconstructions.

<p>Note that the data is isotropic, meaning it has the same resolution along every axis. The goal of segmentation here is to partition the volume into individual neurons, two of which are shown in orange and blue. The volume is densely packed by these thin neuronal processes taking long, tortuous paths.</p

Comparison of oriented mean and actively learned agglomeration.

<p>as measured by VI at the optimal dataset scale (ODS). Each point represents one image. Numbered and colored points correspond to the example images in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0071715#pone-0071715-g008" target="_blank">Figure 8</a>.</p

An integrative approach to characterize disease-specific pathways and their coordination: a case study in cancer-0

<p><b>Copyright information:</b></p><p>Taken from "An integrative approach to characterize disease-specific pathways and their coordination: a case study in cancer"</p><p>http://www.biomedcentral.com/1471-2164/9/S1/S12</p><p>BMC Genomics 2008;9(Suppl 1):S12-S12.</p><p>Published online 20 Mar 2008</p><p>PMCID:PMC2386054.</p><p></p