Automated segmentation of blood cells in Giemsa stained...
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© FIMM - Institiute for Molecular Medicine Finland www.fimm.fi dd.mm.yyyy © FIMM - Institiute for Molecular Medicine Finland www.fimm.fi 8 June 2012 Automated segmentation of blood cells in Giemsa stained digitized thin blood films Margarita Walliander Institute for Molecular Medicine Finland University of Helsinki
Transcript of Automated segmentation of blood cells in Giemsa stained...
© FIMM - Institiute for Molecular Medicine Finland www.fimm.fi
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© FIMM - Institiute for Molecular Medicine Finland www.fimm.fi
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Automated segmentation of blood cells in Giemsa stained digitized thin blood films Margarita Walliander
Institute for Molecular Medicine Finland
University of Helsinki
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What is a Giemsa stained thin blood film?
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What is a Giemsa stained thin blood film?
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What is a Giemsa stained thin blood film?
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Metafer, MetaSystems, Altlussheim and Axio Imager Z2, Carl Zeiss MicroImaging, using a 63X objective (NA 1.4 oil immersion, pixel size 0.10µm)
fimm.webmicroscope.net/Research/Momic/tp2012 *
* Linder et al. Web-based virtual microscopy for parasitology: a novel tool for education and quality assurance. PLoS Negl Trop Dis. 2008;2(10)
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What is a Giemsa stained thin blood film?
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Why do we need a thin blood film? • Analysis of erythrocytes and leucocytes in thin blood films is an
important task for accurate assessment of infectious disease diagnostics.
• Manual counting of cells is currently the only method available for quantifying parasitaemia in infected blood.
• Manual cell counting is time consuming and subject to variability*.
• We propose an automatized method for separating and counting cells.
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* O'Meara et al: Reader technique as a source of variability in determining malaria parasite density by microscopy. Malaria Journal 2006, 5:118
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Materials
• 500 fields of view from each blood film sample were captured.
• 6 of the samples were infected with Plasmodium falciparum,
• 4 were non-infected controls.
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Methods
• Adaptive histogram thresholds
• Background/foreground separation
• Recognition of different objects that compose the foreground
• Cell counting
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Original image
Foreground Background
Round Cells
Found Cells
Approx Cells
Total RBC
WBC
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Original image
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Green channel
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Adaptive histogram thresholds
The color distribution of a thin blood film presents a bimodal shape.
B is the threshold that separates background from foreground.
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Separation of background and foreground
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Separation of background and foreground original
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Round objects selection
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RoundCells RoundCells = Objects with similar area (mean +/- σ)
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RoundCells
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106 cells
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Detection of heavily stained objects
H is the threshold that will define all the heavily stained objects included in the foreground
• WBCs
• parasites
• platelets
• debris
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Detection of heavily stained objects
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Heavily stained objects > AvgRBC
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Heavily stained objects > AvgRBC
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WBCs
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Foreground - WBC - RoundCells
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Hough transform
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Hough transform
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Hough transform
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Detection of centers
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Dilation of detected centers
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Foundcells
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44 cells
AvgRBC2 = average ( FoundCells )
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Foreground-WBC-RoundCells-FoundCells
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# ApproxCells
Area ( Background - RoundCells – WBCs – FoundCells)
Area (AvgRBC2)
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# ApproxCells
Area ( Background - RoundCells – WBCs – FoundCells)
Area (AvgRBC2)
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79 cells
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Total cell counting
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# RoundCells
# FoundCells
# ApproxCells Σ"Total amount of
Red Blood Cells =
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Example Image 1500x1500 pixels ~ 1.9 fields of view
Cells Counted 106
Cells Found 44
Cells Approximated 79
Total 229 RBC s
2 WBCs
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Validation results
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Red blood cells White blood cells
Sample Annotated Automated Error % Annotated Automated Error % I1 3145 3160 0,47 20 20 0,00 I2 4048 4058 0,24 34 34 0,00 I3 2796 2782 0,50 22 22 0,00 I4 2972 2958 0,47 30 32 6,66 I5 3047 3042 0,16 77 75 2,59 I6 3513 3514 0,02 71 72 1,38 C1 3396 3389 0,20 75 75 0,00 C2 3491 3482 0,25 55 56 1,81 C3 3093 3087 0,19 42 42 0,00 C4 3197 3206 0,28 49 50 2,04
TOTAL 32698 32678 0,06 476 477 0,21 % 100 99,9388 100 99,79
30 fields of view per sample
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Test results
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Red blood cells
sample! #RoundCells! #FoundCells! #ApproxCells! #TotalRBC!
I1! 17935! 40512! 886! 59333!I2! 16458! 52600! 1178! 70236!I3! 23068! 20475! 430! 43973!I4! 14237! 32438! 305! 46980!I5! 13670! 31918! 502! 46090!I6! 16656! 41098! 275! 58029!C1! 18379! 38993! 388! 57760!C2! 23704! 29669! 272! 53645!C3! 22290! 22669! 503! 45462!C4! 18108! 32674! 823! 51605!
TOTAL! 184505! 343046! 5562! 533113!
500 fields of view per sample
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Conclusions
• An unsupervised tool for separating red blood cells and white blood cells in Giemsa stained thin blood films.
• An automated red blood cell and white blood cell counting tool.
• The segmentation of blood cells in thin blood films can be used as a pre-processing step to specify the regions of interest for a secondary algorithm.
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Thanks!
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