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

This dataset includes 4 files with segmentation results for 4 different ODT reconstructions of SH-SY5Y neuroblastoma cell. The segmentation results contain: 3D binary masks of biological cells obtained through Cellpose [1] and ODT-SAS; 3D binary masks of organelles: nucleoli and lipid structures (LS) obtained through slice-by-slice manual segmentation and ODT-SAS. All files are .*mat files. The files <em>REC_SH-SY5Y_1.mat, REC_SH-SY5Y_2.mat</em> and<em> REC_SH-SY5Y_3.mat</em> consist of 7 variables: RECON – tomographic reconstruction of SH-SY5Y neuroblastoma cell;<br> n_imm – refractive index of object immersion medium;<br> dx – object space sample size in XY [\(\mu m\)];<br> rayXY – xy-coordinates of illumination vectors; maskManual – table with manually determined 3D binary masks of organelles;<br> maskCellpose – 3D binary mask of biological cell obtained through Cellpose;<br> maskODTSAS – table with 3D binary masks of biological cell and their organelles obtained through ODT-SAS. File <em>REC_SH-SY5Y_4.mat</em> 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 'maskManual' and 'maskODTSAS' tables, using the following names: 'Cell', 'Nucleoli', 'LS'.<br> For example: <pre><code>cellMask = maskODTSAS.Cell{1};</code></pre> <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.