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 REC_SH-SY5Y_1.mat, REC_SH-SY5Y_2.mat and REC_SH-SY5Y_3.mat consist of 7 variables: RECON – tomographic reconstruction of SH-SY5Y neuroblastoma cell; n_imm – refractive index of object immersion medium; dx – object space sample size in XY [\(\mu m\)]; rayXY – xy-coordinates of illumination vectors; maskManual – table with manually determined 3D binary masks of organelles; maskCellpose – 3D binary mask of biological cell obtained through Cellpose; maskODTSAS – table with 3D binary masks of biological cell and their organelles obtained through ODT-SAS. File REC_SH-SY5Y_4.mat 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. Access a particular 3D binary mask from 'maskManual' and 'maskODTSAS' tables, using the following names: 'Cell', 'Nucleoli', 'LS'. For example: cellMask = maskODTSAS.Cell{1}; [1] Stringer, C., Wang, T., Michaelos, M., & Pachitariu, M. (2021). Cellpose: a generalist algorithm for cellular segmentation. Nature methods, 18(1), 100-106.