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Spatial Analysis on Histological
Images Using Spark
Wei-Yi Cheng and Franziska Mech
Roche Pharma Research and Early Development (pRED)
Informatics, Data Science
Roche Innovation Center New York / Munich
Disclaimer
• This presentation is …
– NOT about computer vision / image processing
– NOT about drug, biology, or biochemistry
– NOT about new algorithm or infrastructure
Disclaimer
• This presentation is …
– An application of Spark on spatial analysis on
biomedical images
– A proof of concept of a small module in a complex
pipeline
– A work in progress
Towards a systematic
characterization of tumor context
Challenges:
• Location and density of different
immune cell populations are
associated with patient prognosis and
prediction outcome
• Huge variation in immune infiltrates
across tumor entities and patients
• Inconsistent data in the literature
Nat Rev Drug Discov. 2015;14(9):603-22.
Roche pRED Tissue image analysis workflow
T cell location
and subtype
Vessel location, class
assignment and object
features
Objects in same
reference coordinate
system
Whole slide
image analysis In silico
multiplexing
>10,000 slides per year
(~ 6 slides per block)
>100,000 objects per slide
Imaging
results
Spatial profiling
Goal: actionable data
POC data set
• 57 “blocks“ of 3 cancer types
• Each block contains 6 or 7 slides
• histology stain
• cancer biomarker
• microenvironment biomarker
• Each object annotated with object
type (T cell, lymph vessels, etc.),
shape (point / polygon), and
coordinate
Distance calculation
• Basic statistic for distribution, co-localization, spatial clustering, etc.
• Distance: shortest distance between two contours
• Total number of pairwise distances: 5.3 trillion (1012) pairs – prohibitive
• Workaround: only calculate the distance between each object and its
“neighbors” within a window r.
Spatial Spark
• An open source library developed by
Dr. Simin You and Prof. Jianting Zhang
from CUNY.
• Divide-and-conquer, following similar
designs of HadoopGIS.
• Support multiple spatial partitioning
methods: sort-tile partition, binary-split
partition, fixed-grid partition
http://simin.me/projects/spatialspark/
Spatial profiling workflow
Distance calculation: run time
• Using 18 cores, 4 threads / core (72X
parallelization).
• Radius = 232.5 μm
• Under shared test environment
• Execute time is roughly linear to
number of object (theoretical time
complexity).
Co-localization
• Enables profiling of between-indication
variation and between-tumor variation.
Distance distribution from immune
cell to blood vessel
Spatial clustering of objects: DBSCAN
• Density-based spatial clustering of applications with noise (DBSCAN)
clusters closely connected points into the same cluster, marking high-density
regions.
• Parameters:
• minPts: minimum number of neighbors
• Eps: maximum distance to define a neighbor
minPts = 3
A: core point
B, C: reachable
N: outlier
https://en.wikipedia.org/wiki/DBSCAN
http://scikit-learn.org/stable/modules/clustering.html
DBSCAN: run time
• Directly load distance from
parquet.
• Run time is linear to the number
of objects (given already
calculated distances).
Future works
• Clinical information
• Genomic data
• Scale up and upstream integration
• UI integration
Acknowledgement
• Spark exploratory / support
– Sittichoke Saisanit (Roche pREDi)
– Xing Yang (Roche pREDi)
– Padmanabha Udupa (Roche pREDi)
– Ivan San Antonio Martinez (Roche
IDW)
– Zayed Albertyn (Novocraft)
• Tumor image spatial analysis research
– Angelika Fuchs (Roche pREDi)
– Gerlind Herberich (Roche pREDi)
– Jurriaan Brouwer (Roche pREDi)
• Spatial Spark
– Jianting Zhang (CUNY)
– Simin You (CUNY)
Doing now what patients need next
THANK YOU.
Wei-Yi Cheng
wei-yi.cheng@roche.com

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Spatial Analysis On Histological Images Using Spark

  • 1. Spatial Analysis on Histological Images Using Spark Wei-Yi Cheng and Franziska Mech Roche Pharma Research and Early Development (pRED) Informatics, Data Science Roche Innovation Center New York / Munich
  • 2. Disclaimer • This presentation is … – NOT about computer vision / image processing – NOT about drug, biology, or biochemistry – NOT about new algorithm or infrastructure
  • 3. Disclaimer • This presentation is … – An application of Spark on spatial analysis on biomedical images – A proof of concept of a small module in a complex pipeline – A work in progress
  • 4. Towards a systematic characterization of tumor context Challenges: • Location and density of different immune cell populations are associated with patient prognosis and prediction outcome • Huge variation in immune infiltrates across tumor entities and patients • Inconsistent data in the literature Nat Rev Drug Discov. 2015;14(9):603-22.
  • 5. Roche pRED Tissue image analysis workflow T cell location and subtype Vessel location, class assignment and object features Objects in same reference coordinate system Whole slide image analysis In silico multiplexing >10,000 slides per year (~ 6 slides per block) >100,000 objects per slide Imaging results Spatial profiling
  • 7. POC data set • 57 “blocks“ of 3 cancer types • Each block contains 6 or 7 slides • histology stain • cancer biomarker • microenvironment biomarker • Each object annotated with object type (T cell, lymph vessels, etc.), shape (point / polygon), and coordinate
  • 8. Distance calculation • Basic statistic for distribution, co-localization, spatial clustering, etc. • Distance: shortest distance between two contours • Total number of pairwise distances: 5.3 trillion (1012) pairs – prohibitive • Workaround: only calculate the distance between each object and its “neighbors” within a window r.
  • 9. Spatial Spark • An open source library developed by Dr. Simin You and Prof. Jianting Zhang from CUNY. • Divide-and-conquer, following similar designs of HadoopGIS. • Support multiple spatial partitioning methods: sort-tile partition, binary-split partition, fixed-grid partition http://simin.me/projects/spatialspark/
  • 11. Distance calculation: run time • Using 18 cores, 4 threads / core (72X parallelization). • Radius = 232.5 μm • Under shared test environment • Execute time is roughly linear to number of object (theoretical time complexity).
  • 12. Co-localization • Enables profiling of between-indication variation and between-tumor variation.
  • 13. Distance distribution from immune cell to blood vessel
  • 14. Spatial clustering of objects: DBSCAN • Density-based spatial clustering of applications with noise (DBSCAN) clusters closely connected points into the same cluster, marking high-density regions. • Parameters: • minPts: minimum number of neighbors • Eps: maximum distance to define a neighbor minPts = 3 A: core point B, C: reachable N: outlier https://en.wikipedia.org/wiki/DBSCAN http://scikit-learn.org/stable/modules/clustering.html
  • 15. DBSCAN: run time • Directly load distance from parquet. • Run time is linear to the number of objects (given already calculated distances).
  • 16. Future works • Clinical information • Genomic data • Scale up and upstream integration • UI integration
  • 17. Acknowledgement • Spark exploratory / support – Sittichoke Saisanit (Roche pREDi) – Xing Yang (Roche pREDi) – Padmanabha Udupa (Roche pREDi) – Ivan San Antonio Martinez (Roche IDW) – Zayed Albertyn (Novocraft) • Tumor image spatial analysis research – Angelika Fuchs (Roche pREDi) – Gerlind Herberich (Roche pREDi) – Jurriaan Brouwer (Roche pREDi) • Spatial Spark – Jianting Zhang (CUNY) – Simin You (CUNY)
  • 18. Doing now what patients need next