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CD30 Cell Graphs of Hodgkin lymphoma are not
scale-free — an Image Analysis Approach
Tim Schäfer
Goethe-University Frankfu...
Pathology workflow at Dr. Senckenberg Institute
of Pathology
Biopsy Staining
Tissue & cell morphology
Suggestion for treat...
Digital pathology
Biopsy
Slide scanner
Whole slide images (WSIs)
Digital image analysis
Cell properties
Quantification
Gui...
Classical Hodgkin Lymphoma (cHL)
● Malignancy of the lymphatic
system, Thomas Hodgkin
● No solid tumor is formed
● Hodgkin...
Classical Hodgkin Lymphoma (cHL)
● Malignancy of the lymphatic
system, Thomas Hodgkin
● No solid tumor is formed
● Hodgkin...
Classical Hodgkin Lymphoma (cHL)
● Pathologists inspect small image
regions
● Morphology of some interesting
CD30+
cell ce...
Project goals
● Work on the full WSI
● Systematic analysis and
quantification of:
●
CD30+
cell properties
– Typical cell s...
Project goals
● Long term goals:
●
Understand how CD30+
cells spread
through the lymph node and
lymphatic system
● Find ou...
Dataset
● Dr. Senckenberg Institute of Pathology
● 35 whole slide images (WSIs)
● Pyramidal image format
● Resolution 0.25...
Dataset
● Dr. Senckenberg Institute of Pathology
● 35 whole slide images (WSIs)
● Pyramidal image format
● Resolution 0.25...
NS cHL
Input image
NS cHL
Detected cells, colored by
morphological properties
The Impro Image Processing Software
● Java Advanced Imaging API
● Interfaces to CellProfiler and ImageJ
## Impro PIPELINE
...
Parallel processing of image tiles
Database
CellProfiler: Kamentsky et al.,
Bioinformatics 27(8): 1179-1180, 2011.
CellPro...
Impro: Detect region of interest
● Images contain one or more tissue patches and background
● We are interested in the tis...
Impro: Detect region of interest
● Images contain one or more tissue patches and background
● We are interested in the tis...
Imaging Pipeline
1. Input image
Imaging Pipeline
1. Input image
2. Color deconvolution
Color deconvolution method: Ruifrok,
Analytical and Quantitative Cy...
Imaging Pipeline
1. Input image
2. Color deconvolution
3. Segmentation,
Region growing,
Shape descriptors
Color deconvolut...
Imaging Pipeline
Color deconvolution method: Ruifrok,
Analytical and Quantitative Cytology and
Histology, 19(2):107–113, 1...
Validation
Manual annotation Software
● For a selection of randomly chosen image tiles:
Validation
+
Validation
False positive (FP)
True positive (TP)
False negative (FN)
}
Sensitivity = TP / (TP + FN)
Precisio...
WSIs are large and heterogeneous
Blood vessels Very high cell density
Stain residues, broken and folded tissue Stitching e...
Positions of cells detected by
the imaging pipeline, colored by
morphological properties
How to model it?
Unit disk graphs
x
y
Unit disk graphs
t
Distance threshold t
Unit disk graphs
t
Unit disk graphs
t
NS cHL
Tissue and detected cells
Tissue and graph
CD30 cell graph:
up to 90,000 cells and
7,000,000 edges
Clustering? Typical patterns?
Does this graph contain any information?
● Could it be random?
● What would we expect a random unit disk graph to look lik...
Null hypothesis – Create equivalent random cell
graph for an image
● For a single image,
distribute the same
number of cel...
Vertex degree distribution of the null model:
Poisson and simulation
Vertex degree k
p(k)
Null model for a single Nodular ...
Null model and measured cell graph
Vertex degree k
p(k)
Data for a single Nodular sclerosis case
Gamma distribution fit to cell graph
Vertex degree k
p(k)
Data for a single Nodular sclerosis case
CD30+
cells cluster in the tissue
Vertex degree k
p(k)
Data for a single Nodular sclerosis case
Cell graph analysis
● Comparison of vertex degree
distributions and clustering
by disease type revealed
differences betwee...
Work in progress: neighborhood analysis by cell
shape and size
Jennifer Scheidel,
Unpublished results
Work in progress: neighborhood analysis by cell
shape and size
Jennifer Scheidel,
Unpublished results
● Next neighbor clas...
Summary
● Imaging pipeline for cell detection in whole slide images
● Quantification of cell and image properties (typical...
Acknowledgments
Molecular Bioinformatics Group
Goethe-University Frankfurt am Main
Ina Koch
Hendrik Schäfer, Jörg Ackerman...
Acknowledgments
Dr. Senckenberg Institute of Pathology
Prof. Dr. Dr. h.c. Martin-Leo Hansmann
Prof. Dr. Sylvia Hartmann
Dr...
Thank you for your attention!
Questions?
Appendix slides follow
MCcHL case
LA case
NScHL case
Color deconvolution
CD30 (Fuchsine red) Haematoxilin
Method: Ruifrok et al., 2004
Heterogeneous images
High intensity Low intensity
Unspecific staining
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SchaeferTP105

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SchaeferTP105

  1. 1. CD30 Cell Graphs of Hodgkin lymphoma are not scale-free — an Image Analysis Approach Tim Schäfer Goethe-University Frankfurt am Main, Germany Institute of Computer Science Department of Molecular Bioinformatics ISMB 2016, Orlando July 12, 2016 H. Schäfer1 ,T. Schäfer1 , J. Ackermann1 , N. Dichter1 , C. Döring2 , S. Hartmann2 , M.-L. Hansmann2 , and I. Koch1 . Bioinformatics, 32(1):122–129, 2016. 1 Molecular Bioinformatics, Goethe-University Frankfurt, Germany 2 Dr. Senckenberg Institute of Pathology, University Hospital Frankfurt, Germany
  2. 2. Pathology workflow at Dr. Senckenberg Institute of Pathology Biopsy Staining Tissue & cell morphology Suggestion for treatment
  3. 3. Digital pathology Biopsy Slide scanner Whole slide images (WSIs) Digital image analysis Cell properties Quantification Guidance Data for a model - Lymphadenitis - Hodgkin lymphoma - NScHL - MCcHL Staining Tissue & cell morphology Suggestion for treatment
  4. 4. Classical Hodgkin Lymphoma (cHL) ● Malignancy of the lymphatic system, Thomas Hodgkin ● No solid tumor is formed ● Hodgkin-Reed-Sternberg (HRS) cells ● Complex tumor microenvironment, shaped by HRS cells
  5. 5. Classical Hodgkin Lymphoma (cHL) ● Malignancy of the lymphatic system, Thomas Hodgkin ● No solid tumor is formed ● Hodgkin-Reed-Sternberg (HRS) cells ● Complex tumor microenvironment, shaped by HRS cells ● CD30 marker: HRS cells and activated lymphocytes, e.g., in – cHL Subtypes: ● Nodular sclerosis (NScHL) ● Mixed cellularity (MccHL) – Lymphadenitis (LA) Blue: Cell nuclei (haematoxilin) Red: CD30+ cells (fuchsine red)
  6. 6. Classical Hodgkin Lymphoma (cHL) ● Pathologists inspect small image regions ● Morphology of some interesting CD30+ cell cells ● Tissue morphology ● Patterns? Blue: Cell nuclei (haematoxilin) Red: CD30+ cells (fuchsine red)
  7. 7. Project goals ● Work on the full WSI ● Systematic analysis and quantification of: ● CD30+ cell properties – Typical cell size, shape, ... ● The spatial distribution of CD30+ cells – Cell density, neighborhood, ... – Patterns … in the different diseases and disease sub types. Blue: Cell nuclei (haematoxilin) Red: CD30+ cells (fuchsine red)
  8. 8. Project goals ● Long term goals: ● Understand how CD30+ cells spread through the lymph node and lymphatic system ● Find out more about the composition of and the cellular communication in the tumor microenvironment ● Combine data from different projects to build a model Blue: Cell nuclei (haematoxilin) Red: CD30+ cells (fuchsine red)
  9. 9. Dataset ● Dr. Senckenberg Institute of Pathology ● 35 whole slide images (WSIs) ● Pyramidal image format ● Resolution 0.25 μm per pixel (px) ● Dimension up to 100,000 x 100,000 px ● Uncompressed file size up to 30 GB
  10. 10. Dataset ● Dr. Senckenberg Institute of Pathology ● 35 whole slide images (WSIs) ● Pyramidal image format ● Resolution 0.25 μm per pixel (px) ● Dimension up to 100,000 x 100,000 px ● Uncompressed file size up to 30 GB Lymphadenitis Mixed Cellularity cHL Nodular sclerosis cHL
  11. 11. NS cHL Input image
  12. 12. NS cHL Detected cells, colored by morphological properties
  13. 13. The Impro Image Processing Software ● Java Advanced Imaging API ● Interfaces to CellProfiler and ImageJ ## Impro PIPELINE ImproImageViewerPlugin;CreateImproImage ImproImageMultiResClusteringPlugin;IdentifyROI:m inLayer=3;maxLayer=2;saveROI=true;onlyProcessROI =true;noImageOutput=false ImproCellProfilerAdapter;SplitImage:tileSize=102 4;border=100;useROI=true;createSubfolder=false;o utputDir=$WORKSPACE$/tmpDirs/ ImproCellProfilerAdapter;IdentifyCellObjects:pip elineFile=pipeline_CD30.cp;dir=$WORKSPACE$/tmpDi rs/;useBatchFile=true;numberOfProcesses=4 MainApp;ExitProgram Command file (headless mode) Graphical user interface CellProfiler: Lamprecht et al., Biotechniques, 42:71, 2007. ImageJ: Abramoff et al., Biophotonics International, 11(7):36–42, 2004.
  14. 14. Parallel processing of image tiles Database CellProfiler: Kamentsky et al., Bioinformatics 27(8): 1179-1180, 2011. CellProfiler Pipeline Impro
  15. 15. Impro: Detect region of interest ● Images contain one or more tissue patches and background ● We are interested in the tissue area
  16. 16. Impro: Detect region of interest ● Images contain one or more tissue patches and background ● We are interested in the tissue area ● Detect tissue on low-resolution image ● Reduces data by ~40% on average Schäfer, et al. Computational Biology and Chemistry, 46:1–7, 2013.
  17. 17. Imaging Pipeline 1. Input image
  18. 18. Imaging Pipeline 1. Input image 2. Color deconvolution Color deconvolution method: Ruifrok, Analytical and Quantitative Cytology and Histology, 19(2):107–113, 1997.
  19. 19. Imaging Pipeline 1. Input image 2. Color deconvolution 3. Segmentation, Region growing, Shape descriptors Color deconvolution method: Ruifrok, Analytical and Quantitative Cytology and Histology, 19(2):107–113, 1997. CellProfiler: Kamentsky et al., Bioinformatics 27(8): 1179-1180, 2011.
  20. 20. Imaging Pipeline Color deconvolution method: Ruifrok, Analytical and Quantitative Cytology and Histology, 19(2):107–113, 1997. CellProfiler: Kamentsky et al., Bioinformatics 27(8): 1179-1180, 2011. 1. Input image 2. Color deconvolution 3. Segmentation, Region growing, Shape descriptors 4. Filtered resulting objects
  21. 21. Validation Manual annotation Software ● For a selection of randomly chosen image tiles:
  22. 22. Validation + Validation False positive (FP) True positive (TP) False negative (FN) } Sensitivity = TP / (TP + FN) Precision = TP / (TP + FP)
  23. 23. WSIs are large and heterogeneous Blood vessels Very high cell density Stain residues, broken and folded tissue Stitching error from scanning process
  24. 24. Positions of cells detected by the imaging pipeline, colored by morphological properties How to model it?
  25. 25. Unit disk graphs x y
  26. 26. Unit disk graphs t Distance threshold t
  27. 27. Unit disk graphs t
  28. 28. Unit disk graphs t
  29. 29. NS cHL Tissue and detected cells
  30. 30. Tissue and graph
  31. 31. CD30 cell graph: up to 90,000 cells and 7,000,000 edges Clustering? Typical patterns?
  32. 32. Does this graph contain any information? ● Could it be random? ● What would we expect a random unit disk graph to look like? ● How do the properties of our measured CD30 cell graph differ from a random unit disk graph?
  33. 33. Null hypothesis – Create equivalent random cell graph for an image ● For a single image, distribute the same number of cells on the same area ● Poisson point process: ● The position of each cell is chosen randomly ● Each position is equally probable ● Locations of existing cells do not influence new cells
  34. 34. Vertex degree distribution of the null model: Poisson and simulation Vertex degree k p(k) Null model for a single Nodular sclerosis case Avg. degree of the case ~ 11
  35. 35. Null model and measured cell graph Vertex degree k p(k) Data for a single Nodular sclerosis case
  36. 36. Gamma distribution fit to cell graph Vertex degree k p(k) Data for a single Nodular sclerosis case
  37. 37. CD30+ cells cluster in the tissue Vertex degree k p(k) Data for a single Nodular sclerosis case
  38. 38. Cell graph analysis ● Comparison of vertex degree distributions and clustering by disease type revealed differences between LA and the cHL sub types ● Extend analysis ● More graph properties ● Integration of data like cell shape ● Additional markers for other cell types
  39. 39. Work in progress: neighborhood analysis by cell shape and size Jennifer Scheidel, Unpublished results
  40. 40. Work in progress: neighborhood analysis by cell shape and size Jennifer Scheidel, Unpublished results ● Next neighbor class ● Next neighbor distance
  41. 41. Summary ● Imaging pipeline for cell detection in whole slide images ● Quantification of cell and image properties (typical cell size, cell counts,...) ● Images and cases are very heterogeneous ● Definition of cell graphs to model cells in space ● Comparison with null model revealed ● Cell graphs are not random ● CD30+ cells cluster in the tissue – Attraction, cell division, lymph node structure? ● Vertex degree distribution of cell graphs can be modeled by the Gamma distribution ● Cell graphs show differences between lymphadenitis and the Hodgkin lymphoma sub types
  42. 42. Acknowledgments Molecular Bioinformatics Group Goethe-University Frankfurt am Main Ina Koch Hendrik Schäfer, Jörg Ackermann, Jennifer Scheidel, Patrick Wurzel, Tanmay Pradhan, Marie Hebel, Sonja Scharf, Norbert Dichter Travel funding to ISMB 2016 was generously provided by IRB-Group.
  43. 43. Acknowledgments Dr. Senckenberg Institute of Pathology Prof. Dr. Dr. h.c. Martin-Leo Hansmann Prof. Dr. Sylvia Hartmann Dr. Claudia Döring
  44. 44. Thank you for your attention! Questions?
  45. 45. Appendix slides follow
  46. 46. MCcHL case
  47. 47. LA case
  48. 48. NScHL case
  49. 49. Color deconvolution CD30 (Fuchsine red) Haematoxilin Method: Ruifrok et al., 2004
  50. 50. Heterogeneous images High intensity Low intensity Unspecific staining

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