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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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