<h1>REAVER Vascular Networks Fluorescent Image Dataset</h1>

<h3>Creators</h3>

Corliss, Bruce A.

Doty, Richard

Yates, Paul A.

Peirce, Shayn M.

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<section id="description" class="rel-mt-2 rich-input-content" aria-label="Record description">

    <h2 id="description-heading" class="sr-only">Description</h2>

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      <p><strong>Fluorescent Images of Vessel Networks from Various Murine Tissues</strong></p>

<p>&nbsp;</p>

<p><strong>Purpose</strong>: Image dataset of vascular networks with a diverse range of vessel architectures. Dataset is used to evaluate performance of several image processing programs (AngioQuant<sup>1</sup>, AngioTool<sup>2</sup>, RAVE<sup>3</sup>, REAVER). Manual analysis from ImageJ is used as ground truth to compare other programs against.</p>

<ul>

    <li><strong>Labeling</strong>: IB4-Lectin with Alexa Flour 647</li>

    <li><strong>Modality</strong>: Confocal Microscope Nikon 80i CLSM</li>

    <li><strong>Objective</strong>: Mixture of 20x and 60x objective images</li>

    <li><strong>Image Format</strong>: Images originally acquired in Nikon IDS format, converted to 8-bit greyscale TIFs found in “_Original_Images” folder.</li>

    <li><strong>Questions</strong>: Email <a href="mailto:bac7wj@virginia.edu">bac7wj@virginia.edu</a> for inquiries.</li>

</ul>

<p>&nbsp;</p>

<p><strong>External Links</strong></p>

<ol>

    <li><strong>Manuscript</strong>:</li>

    <li><strong>Code repository: </strong><a href="https://github.com/bacorliss/REAVER_public">https://github.com/bacorliss/REAVER_public</a> for code to analyze this data (MATLAB 2019a).</li>

</ol>

<p>&nbsp;</p>

<p><strong>Dataset Summary:</strong></p>

<p>Each image folder contains 36 images. For each image:</p>

<ol>

    <li>The first channel (red) is the segmented image with values of 0 or 255 (false or true).</li>

    <li>The second channel (green) is the skeleton image with values of 0 or 255 (false or true).</li>

    <li>The third channel (blue) is empty except for the Manual images where the third channel contains the original raw image.</li>

</ol>

<p>&nbsp;</p>

<p><strong>Subfolders</strong></p>

<ol>

    <li><strong>_Original_Images</strong>: contains raw input images.</li>

    <li><strong>AngioQuant_Auto</strong>: contains output images from automated analysis in AngioQuant.</li>

    <li><strong>AngioTool_Auto</strong>: contains output images from automated analysis in AngioTool.</li>

    <li><strong>ImageJ_Auto</strong>: contains output images from automated analysis in ImageJ.</li>

    <li><strong>ImageJ_Manual</strong>: contains output images from manual analysis in ImageJ.</li>

    <li><strong>RAVE_Auto</strong>: contains output images from automated analysis in RAVE.</li>

    <li><strong>REAVER_Auto</strong>: contains output images from automated analysis in REAVER.</li>

</ol>

<p>&nbsp;</p>

<p><strong>Image Metadata and Output data</strong></p>

<p>Each image folder has a .mat file called “Results.mat” containing the results of analysis in the form of the following variables all of which are 1x36 arrays (one entry for each image) unless specified otherwise:</p>

<ol>

    <li><strong>branchpoint_RC</strong>: A 1x36 struct containing the row-column values for each branchpoint in the i<sup>th</sup> image (when organized in alphabetic order which is the order given everywhere else); Effectively the same as “BranchpointsByName.mat”</li>

    <li><strong>mean_diameter</strong>: The mean diameter of vessels in the image</li>

    <li><strong>num_branchpts</strong>: The number of branchpoints in the image</li>

    <li><strong>threshold_false_neg</strong>: The number of false negative pixels – a pixel is a false negative if the program has it as “false” and the manual image has the pixel as “true”</li>

    <li><strong>threshold_false_pos</strong>: The number of false positive pixels – a pixel is a false positive if the program has it as “true” and the manual image has the pixel as “false”</li>

    <li><strong>threshold_true_neg</strong>: The number of true negative pixels – a pixel is a true negative if the program has it as “false” and the manual image has the pixel as “false”</li>

    <li><strong>threshold_true_pos</strong>: The number of true positive pixels – a pixel is a false positive if the program has it as “true” and the manual image has the pixel as “true”</li>

    <li><strong>umppix</strong>: The length of the edge of one pixel in micrometers</li>

    <li><strong>vessel_area</strong>: The number of “true” pixels in the segmented image</li>

    <li><strong>vessel_length</strong>: The number of “true” pixels in the skeleton image</li>

</ol>

<p>&nbsp;</p>

<p><strong>Dataset Output Data</strong></p>

<p>The file “image_quantification.csv” in the base folder contains the aggregated results from each image folder. Each row contains the results for a given (Program, Image) pair. The columns are described below:</p>

<ol>

    <li><strong>Program</strong>: Designates the program used to calculate the data for that row</li>

    <li><strong>Tissue_Type</strong>: Gives the tissue type for the image</li>

    <li><strong>Image_Name</strong>: Gives the specific name of the given image</li>

    <li><strong>Vessel_Length</strong>: The number of “true” pixels in the skeleton image</li>

    <li><strong>Vessel_Area</strong>: The number of “true” pixels in the segmented image</li>

    <li><strong>Mean_Diameter</strong>: The mean diameter of vessels in the image</li>

    <li><strong>Num_Branchpoints</strong>: The number of branchpoints in the image</li>

    <li><strong>Sensitivity</strong>: (Number of True Positive pixels) / (Number of True Positive pixels + Number of False Negative pixels)</li>

    <li><strong>Specificity</strong>: (Number of True Negative pixels) / (Number of True Negative pixels + Number of False Positive pixels)</li>

    <li><strong>Accuracy</strong>: (Number of True Positive pixels + Number of True Negative pixels) / (Total number of pixels)</li>

    <li><strong>umppix</strong>: The length of the edge of one pixel in micrometers</li>

    <li><strong>pix_dim</strong>: The edge length in pixels of the square image</li>

</ol>

<p>&nbsp;</p>

<p><strong>References</strong></p>

<p>1.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Niemisto, A., Dunmire, V., Yli-Harja, O., Wei Zhang &amp; Shmulevich, I. Robust quantification of in vitro angiogenesis through image analysis. <em>IEEE Trans. Med. Imaging</em> <strong>24</strong>, 549–553 (2005).</p>

<p>2.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Zudaire, E., Gambardella, L., Kurcz, C. &amp; Vermeren, S. A Computational Tool for Quantitative Analysis of Vascular Networks. <em>PLOS ONE</em> <strong>6</strong>, e27385 (2011).</p>

<p>3.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Seaman, M. E., Peirce, S. M. &amp; Kelly, K. Rapid Analysis of Vessel Elements (RAVE): A Tool for Studying Physiologic, Pathologic and Tumor Angiogenesis. <em>PLoS ONE</em> <strong>6</strong>, e20807 (2011).</p>

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