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Table 4 A summary of segmentation assumptions in the surveyed literature

From: Survey statistics of automated segmentations applied to optical imaging of mammalian cells

Assumptions Sub-category Description References
Biological assumptions Image Contrast Strong staining to get high SNR for actin fibers [113]
   Optophysical principle of image formation is known [44]
Cell brightness significantly higher than background [114, 115]
Cell signal higher than noise level in an acquired z-stack [49, 116118]
Object Shape Biological assumptions about mitotic events like mother roundness and brightness before mitosis [119122]
Nucleus shape is round [123]
Specifically designed for dendritic cells [83]
Cell line falls into one a few object models. Cell must have smooth borders. E.coli model assumes a straight or curved rod shape with a minimum volume darker than background. Human cells assume nearly convex shape. [124]
Cells posses only one nucleus [125]
Algorithmic assumptions Background/Foreground Boundary Initializing level sets functions based on k-means clustering [126]
Background Background intensities are between the low and high intensities in the image [127]
Local background must be uniform [128, 129]
Background is piecewise linear and its intensities are between the low and high intensities in the image [130]
Foreground Clear distinction between touching cell edge pixel intensities [122]
Foreground pixels are drawn from a different statistical model than the background pixels [131]
Features computed based on their gray-scale invariants [132]
Time The first image of a time sequence should be segmented first by another algorithm like watershed [69]
Intensity Distributions Image pixel intensities follow bi-modal histogram [42]
The statistical properties of the foreground and background are distinct and relatively uniform & foreground is bright, while the background is dark [133]
Foreground and background follow Gaussinan distribution [134]
Image pre-processing Background flatfield correction Image pre-processing: such as correcting inhomogeneous illuminated background intensities using a machine learning based approach to resolve differences in illumination across different locations on the cell culture plate and over time [81]
Filters Smoothing the image using Gaussian filter [132]
Downsampling (binning) the images [64]
Image smoothing and automatic seed placement are used [56]
Hessian-based filtering for better cell location and shape detection [44]
Non-linear transformation Image pre-conditioning where the image is transformed to bright field before applying the threshold [48]
Manual input Manual interactivity is needed to compute segmentation [84]