Volumetric segmentation of biological cells and subcellular structures for optical diffraction tomography images - dataset

<p>This dataset includes 4&nbsp;files with segmentation results for 4&nbsp;different ODT reconstructions of SH-SY5Y neuroblastoma cell. The segmentation results contain:</p> <ol> <li>3D binary masks of biological cells obtained through Cellpose [1] and <a href="https://github.com/biopto/ODT-SAS.git">ODT-SAS</a>;</li> <li>3D binary masks of organelles: nucleoli and lipid structures (LS) obtained through slice-by-slice manual segmentation&nbsp;and ODT-SAS.</li> </ol> <p>All files are .*mat files.</p> <p>The files <em>REC_SH-SY5Y_1.mat,&nbsp;REC_SH-SY5Y_2.mat</em> and<em>&nbsp;REC_SH-SY5Y_3.mat</em>&nbsp;consist of 7 variables:</p> <p>RECON &ndash;&nbsp;tomographic reconstruction of SH-SY5Y neuroblastoma cell;<br> n_imm &ndash;&nbsp;refractive index of object immersion medium;<br> dx &ndash;&nbsp;object space sample size in XY [<span class="math-tex">\(\mu m\)</span>];<br> rayXY &ndash;&nbsp;xy-coordinates of illumination vectors;</p> <p>maskManual &ndash;&nbsp;table with manually determined 3D binary masks of organelles;<br> maskCellpose &ndash;&nbsp;3D binary mask of biological cell obtained through Cellpose;<br> maskODTSAS &ndash;&nbsp;table with 3D binary masks of biological cell and their organelles obtained through ODT-SAS.</p> <p>File <em>REC_SH-SY5Y_4.mat</em>&nbsp;includes masks for the ODT-SAS and Cellpose segmentation of three closely packed cells and consists of 5 variables: RECON, n_imm, dx, maskCellpose and maskODTSAS.<br> <br> Access a particular 3D binary mask from &#39;maskManual&#39; and &#39;maskODTSAS&#39; tables, using the following names: &#39;Cell&#39;, &#39;Nucleoli&#39;, &#39;LS&#39;.<br> For example:</p> <pre><code>cellMask = maskODTSAS.Cell{1};</code></pre> <p><br> [1] Stringer, C., Wang, T., Michaelos, M., &amp; Pachitariu, M. (2021). Cellpose: a generalist algorithm for cellular segmentation. Nature methods, 18(1), 100-106.</p> <p>&nbsp;</p>