Re: counting cells in multiclour fluorescence images

Posted by Gabriel Landini on Nov 20, 2008; 8:41am
URL: http://imagej.273.s1.nabble.com/counting-cells-in-multiclour-fluorescence-images-tp3694456p3694458.html

On Wednesday 19 November 2008, Matthias Kirsch wrote:
> The main problems start with a real good solution for separating touching
> nuclei. Watersheding as provided in ImageJ does not work too reliably in
> many instances.

It does do what is intended (ie watershed separation). The problem is that the
nuclei clusters do not always give clues as to where the separating lines
should be placed.

> Does anybody know of an alternative routine?? If one could
> make a wish, a solution which implements user knowledge would be helpful.

There is a Watershed plugin from Daniel Sage that can process greyscale images
and might be useful (search in goolge).
Be aware, however, that if you use that plugin and set up a threshold *within
the plugin*, there is a problem obtaining 8-connected basins (i.e.
4-connected watershed lines).  I have been in contact with Daniel in the past
about this problem, but I do not think it has been solved yet. If you use the
whole greyscale space (i.e. minLevel=0, maxLevel=255) the plugin works fine,
but you might be segmenting also areas you do not need, so some workaround is
necessary (i.e. set all background areas to 0 or someting like that).
So, to summarise, if your set min and max levels, the plugin will produce
watershed lines, but you cannot isolate separated cells with the particle
analyzer or the particles8 plugins. If you set levels to 0 and 255 the plugin
works fine.

> generate 'annuli' around
> the identified nuclei. However, if the cells are closely spaced, the area
> around a particular nucleus is splitted, in the end producing more
> particles than cells.

You can still select those many fragments which are continous to a particular
nucleus and count them as one.

> Another problem obviously comes from processes of neighbouring cells
> running close to a nucleus of a cell belonging to another class (i.e. not
> stained). This could of course screw up the classification of cells quite
> substantially.

I guess that is not a problem that can be solved in 2D images. Cells overlap
and one has lost 1 dimension, so which part of the cytplsasm belongs to which
level?
One solution is to look at cells which are isolated, but in itself is in a way
introducing bias in the analysis as cell contacts must surely change the way
they behave.

When separation is not possible, you can still look at the behavious of
clustered cells vs isolated cells. That might give you some information too.

I hope it helps

Gabriel