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Cell segmentation. Oleg Sklyar, Gregoire Pau, EMBL-EBI Cambridge [email protected] Experimental setup. RNAi cell-array End-point assay HeLa cells Channels Actin (TRITC) Tubulin (Alexa 488) DNA (Hoechst). Cell segmentation. Challenging problem Cells superposition Noisy channels

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Cell segmentation

Cell segmentation

Oleg Sklyar, Gregoire Pau, EMBL-EBI Cambridge

[email protected]


Experimental setup
Experimental setup

  • RNAi cell-array

  • End-point assay

  • HeLa cells

  • Channels

    • Actin (TRITC)

    • Tubulin (Alexa 488)

    • DNA (Hoechst)


Cell segmentation1
Cell segmentation

  • Challenging problem

    • Cells superposition

    • Noisy channels

    • Artefacts (cracks, tubulin spots)

    • Knocked-down cells  little a priori information

  • Algorithm design issues:

    • Low a priori a priori knowledge,

    • Joint usage of channels is desirable


Issues
Issues

  • Addressed issues

    • Actin protusions are sometimes outside a cell membrane

    • Cell membranes are sometimes underestimated (ie. flat)

    • Nuclei are sometimes outside a cell membrane

  • Pending issues

    • Superposed cells are often not correctly segmented (ie. Elongated)

    • Bi-nucleated cells and close cells are sometimes wrongly segmented (bi-nucleated are often segmented as two different cells and close different cells are sometimes segmented as unique ones)


Proposed algorithm
Proposed algorithm

  • Algorithm sketch:

    • Find nuclear envelopes on DNA channel

    • Filter “bad” nuclear envelopes

    • Find cells given nuclear envelopes, on Tubulin and Actin channels

    • Filter “bad” cells

    • Iterate previous steps until stabilization

  • Simple and easy to understand/model

  • Joint usage of all channels

  • Iterative converging algorithm


Finding nuclear envelopes
Finding nuclear envelopes

  • Let denote by H the DNA channel

  • Nuclei in different condensation states

  • Different brightness

HHH

ATH


Finding nuclear envelopes1
Finding nuclear envelopes

  • Global thresholding approach

    • Nmask = H > t

    • How t can be set ?

    • There is no optimal t: this appoach cannot work

t too large: mangled nuclei

t too small: unseparated nuclei


Finding nuclear envelopes2
Finding nuclear envelopes

  • Local thresholding approach

    • Nmask = (H - Hm) > t'

    • where Hm is a local H average, Hm=HM, with window M:

    • M should be of size twice than the average nucleus size

    • OK !

Hm

(H - Hm) > t'

H


Filtering bad nuclei
Filtering 'bad' nuclei

  • Given some external rules:

    • Too small or too large

    • Too pale or too bright

    • Mangled because located on the borders

    • Too much empty space

  • Rules should be set by a biologist

  • But can be also automatically set (and tuned afterwards)


Finding cell membranes
Finding cell membranes

  • Cell membranes determination is done in two steps:

    • Compute the binary cell mask (cells / no matter)

    • Boundaries between cells is determined using Voronoi tesselation

Step 1: Cmask Nmask

Step 2: Cmask Nmask Cbound


Cell mask determination
Cell mask determination

  • Global thresholding approach

    • Let be Z = A + T,a matter indication function

    • Cmask = (ZN > t), with a short filter N to prevent noise

    • N should be as small as the smallest cell detail we want to spot

  • Threshold t is computed such as:

    • Nuclei shoud be inside cells  Nmask  Cmask

    • Visible actin should be inside cells  (TN > v)  Cmask

    • Visible tubulin should be inside cells  (AN >v)  Cmask

    • With v, visibility threshold

Actin Tubulin Actin+Tubulin Cbound


Finding cell boundaries
Finding cell boundaries

  • Using Voronoi tesselation

    • Given a set of centers, what are the regions that contain the closest points to them ?

    • Voronoi graph = Dual k-means centroids graph

    • Using here nuclei (Nmask) as centers and Cmask as matter mask

    • Using an Euclidian/geodesic -mixed metric, based on Z = A + T gradient

Geodesic =1e5

Euclidian =0


Filtering bad cells
Filtering 'bad' cells

  • Given some external rules:

    • Too small or too large

    • Too little or too large tubulin amount

    • Too little or too large actin amount

    • ...

  • Remove ‘bad’ cells

    • Debris

    • Tubulin bright spots

    • Cracks artefacts


Iterate until stabilization
Iterate until stabilization

  • Algorithm sketch:

    • Find nuclear envelopes on DNA channel

    • Filter “bad” nuclear envelopes

    • Find cells given nuclear envelopes, on Tubulin and Actin channels

    • Filter “bad” cells

    • Iterate previous steps until stabilization

  • Iterative algorithm

  • Bounded (Nmask, Cmask) inclusive sequence  Convergence

  • Effectively carry joint information from step to step




Pending issues
Pending Issues

  • Superposed cells are not correctly segmented

    • Require a superposed model

    • Much harder problem !

  • Nuclei are sometimes wrongly segmented

    • Binucleated cells whose nuclei are too far to each other

    • Normal cells whose nuclei are too close together

    • Could be alleviated using T and A channels during nuclei segmentation


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