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

Authors: Mazur, Martyna; Krauze, Wojciech;

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

Abstract

This dataset includes 3 files with segmentation results for 3 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 and 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. 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.

Keywords

optical diffraction tomography, quantitative phase imaging, biological cells, subcellular structures, volumetric segmentation

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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