# Celltracker possibilities?

**URL:** <https://discourse.julialang.org/t/celltracker-possibilities/46736>\
**Category:** Biology, Health, and Medicine\
**Tags:** question\
**Created:** [September 16, 2020, 11:14pm UTC](https://discourse.julialang.org/t/celltracker-possibilities/46736 "2020-09-16T23:14:02Z")\
**Posts on this page:** 1\
**Showing post:** 4

<div class="post-metadata">

**Author:** ![mschauer](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/mschauer/32/13946_2.png) [@mschauer](https://discourse.julialang.org/u/mschauer)\
**Post date:** [September 17, 2020, 12:28pm UTC](https://discourse.julialang.org/t/celltracker-possibilities/46736/4 "2020-09-17T12:28:36Z")

</div>

We have 3d image data for `cell`s and 3d image data for `nucleus`.  
Think of the cells creating worms in the space-time picture (the third axis is the time).

After some preprocessing (e.g. `imfilter!` with `Kernel.gaussian((2,2,0))`),  
we start finding cells in the first frame by doing a feature transform on their nuclei:

```julia
using Images, ImageSegmentation, ImageFiltering 
seeds1_ = distance_transform(feature_transform((nucleus[:,:,1]) .< t1))) .> t2
seeds1 = (label_components(seeds1_))

```

I typically use a Makie script to find good thresholds:

```julia

println("Sliders to tune thresholds")
t1slider, t1_ = textslider(0.01:0.01:1.0, "t1", start = t1);
t2slider, t2_ = textslider(0.01:0.01:1.0, "t2", start = t2);
seed_img = lift(t1_, t2_) do t1, t2
	nlz(distance_transform(feature_transform(testimage) .< t1))) .> t2
end
p3 = hbox(image(seed_img), t1slider, t2slider)

```

The next step puts the recognised nuclei into the picture and fixes some background pixels

```julia
seeds = zeros(UInt16, m, n, T)
seeds[:,:,1] = seeds1
seeds[end,1,1] = seeds[1,end,1] = seeds[end,end,1] = seeds[1,1,1] = maximum(seeds1) + 1

```

Then I am segmenting the cells on a picture with cell boundaries being the “walls” separating cell-interiors and background through watershed

```julia
nlz(x, (a,b) = (minimum(x), maximum(x)), (c,d) = (zero(eltype(x)), one(eltype(x)))) = clamp.(c .+ (x .- a) .* ((d-c)/(b-a)), c, d)
cell2 = N0f16.(1 .-nlz(abs.(cell .- t))) # normalize
segments = watershed(cell2, seeds)

```

Basically I am flooding the worms and the outside.

Later I will explain the post processing giving me the cell trajectories.

 ![segmentation2](https://global.discourse-cdn.com/julialang/original/3X/9/b/9bc4a080eca500b46ea432c0b3e1a41be8b588ad.gif)

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