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Cell segmentation

Cell segmentation

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Cell segmentation

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  1. Cell segmentation Oleg Sklyar, Gregoire Pau, EMBL-EBI Cambridge gregoire.pau@ebi.ac.uk

  2. Experimental setup • RNAi cell-array • End-point assay • HeLa cells • Channels • Actin (TRITC) • Tubulin (Alexa 488) • DNA (Hoechst)

  3. 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

  4. 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)

  5. 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

  6. Finding nuclear envelopes • Let denote by H the DNA channel • Nuclei in different condensation states • Different brightness HHH ATH

  7. 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

  8. 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

  9. 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)

  10. 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

  11. 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

  12. 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

  13. 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

  14. 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

  15. Results

  16. Results

  17. 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