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

dead spot

dead spotQuery File [check]not usedNot usedQuerie

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 2</b>: Delayed Agglomerative Segmentation.

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

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

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

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