Improved segmentation accuracy by context-aware approach on FIBSEM data.

<p>(a) one plane of input volume, (b) mitochondria detection on that plane, (c) the output of GALA [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0125825#pone.0125825.ref015" target="_blank">15</a>] (context oblivious), and (d)the output of proposed context aware method. The segmented region labels are overlaid on the image using random artificial colors. S and M on images indicate locations of false split and merge respectively</p

Distribution of predicted boundary confidences on cytoplasm-mitochondria borders (blue) and correct cell boundaries (red).

<p>The plot is clipped at <i>y</i> = 1500 for better visualization. Notice the overlap between these two distributions within confidence range [0,0.6].</p

Runtime for different algorithms.

<p>Runtime for different algorithms.</p

<b>Algorithm 2</b>: Delayed Agglomerative Segmentation.

<p><b>Algorithm 2</b>: Delayed Agglomerative Segmentation.</p

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Split-VI of cytoplasm segmentation of two FIBSEM volumes.

<p>Left column: test volume 1, right column: test volume 2. Each curve is the average of results in 5 trials. Each point represents either a stopping point for clustering or bias parameter.</p

Sample planes from the test volume 1.

<p>Sample planes from the test volume 1.</p

Segmentation error in terms of split-VI and split-RE on two FIBSEM volumes.

<p>Top: Test volume 1 and bottom: Test volume 2. Left column shows split-VI error: <i>VI</i>_<i>UE</i> in x-axis, <i>VI</i>_<i>OE</i> in y-axis; right column shows split-RE: <i>RE</i>_<i>UE</i> in x-axis, <i>RE</i>_<i>OE</i> in y-axis. Each curve is the average of results in 5 trials. Each point represents either a stopping point for clustering or bias parameter for [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0125825#pone.0125825.ref007" target="_blank">7</a>].</p

<b>Algorithm 1</b>: Existing Agglomerative Segmentation.

<p><b>Algorithm 1</b>: Existing Agglomerative Segmentation.</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