Smooth 2d Manifold Extraction From 3d Image Stack

1 minute read

  • https://www.nature.com/articles/ncomms15554
  • An alternative to maximum intensity projection or wavelet based z-stacking/focus-stacking/extended depth of field
  • Fiji plugin available, no parameters to set
  • Idea: “fit a ‘smooth’, parameter-free, 2D manifold onto the foreground signal while ‘ignoring’ the background”
  • Works on 2.5D images, be careful with real 3D imaging
    • “in the case of a full 3D content, a single 2D extraction cannot be satisfactory”
  • “Selecting a reference channel to compute a unique index map for all channels might be regarded at first as a parameter. However, it is not, it should be a rational choice related to the biological question being tested with a given data set”
  • How it works
    • For each x,y position, the focal score is calculated as a function of z: F(z)
    • The Fourier transform (PSD) is then calculated for each F(z) - the idea being that x,y positions corresponding to forground objects have low-frequency power and not just (background) noise
    • A 3-class k-means is performed and each x,y position assigned a label: foreground, background, uncertain
    • The resulting 2D index map of the labels, build of these three classes is smoothed
    • A 2D image is formed, taking the intensity value for each x,y corresponding to the z position in the smoothed index map
  • I did not try to understand the details of how they smooth and why there are no free parameter. This is covered in their supplementary information