Download Application of Pattern Recognition Software on

Application of pattern
recognition software on
preclinical and safety studies
Alison Bigley CSci
CSci,, FIBMS
“There are only patterns, patterns on top of patterns, patterns that affect other patterns.
Patterns hidden by patterns. Patterns within patterns.
What we call chaos are patterns we haven't recognized. What we call random are patterns
we can't decipher. what we can't understand we call nonsense”
Charles Michael “Chuck” Palahniuk (Author – 2013)
The field of pattern recognition is concerned with the automatic discovery
of regularities in data through the use of computer algorithms and with the
use of these regularities classifying the data into different categories
(Prof. Chris Bishop)
Background noise
Calculus
Uncertainty
Linear algebra
Finite data sets
Probability theory
Pattern recognition software with a view to:
 Hierarchical/tiered workflows and utilisation on preclinical
and safety tissues prior to or in conjunction with ‘routine
analysis’
 Comparison of 4 different pattern recognition software
systems in the evaluation of progesterone receptor in
breast TMA
 Current applications and future integrations within the drug
discovery process and potential translation to companion
algorithms
Pattern recognition software – approach to analysis
optimisation
TRAINING DATA
feature
selection
/extraction
Input image
features
label
pattern
recognition
algorithm
classifier
(model)
STUDY/TEST DATA
feature
extraction
features
classifier
(model)
Input image
label
> Classifiers > Time to run
> Image Resolution > Time to run
> Complexity > Class variation < Accuracy
Validation against ‘ground truth’
Generally classification systems utilise a single tier approach
results data
Pattern recognition single tiered / hierarchical workflows
ROI / CELL
SLIDE
SCANNING
ANALYSIS /
MEASURES
(TISSUE/ROI
DETECTION)
Tier 1
Low Res
x1.0 – 2.5
CELL
DETECTION
PATTERN
RECOGNITION
TISSUE
DETECTION
Tier 3
Tier 2
High Res
x10-40
Med Res
x5.0 – 10
TUMOUR
DETECTION
H&E – normal tissues / tumour detection / classification
x2.5 magnification
x5 magnification
IHC labelled – Kidney cell component classification
x10 magnification
Pattern recognition application
• Value of pattern recognition in digital pathology
• Enables a more practical solution for ROI identification/selection on large
studies by multiple users
• Reduces degree of pre-conceived ideas, obviates bias
• Potential identification/elimination of inherent experimental variation in
tissue sections
• Potential to use misclassification rate as a marker of response
• Current PR software user-friendly
• Validation of pattern recognition classifiers in digital pathology
• Need to confirm robustness of classifiers
• Range of slides, several runs, multiple studies
• Increase size of data sets - re-check classifier performance
Pattern recognition - considerations
• Establishing classifications based on identification of a
single tissue type or tumour type:
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Potential lack of robustness due to insufficient training data
Single application may not translate to other ‘like’ studies
Tumour/tissue heterogeneity may not be accounted for
Impact on experimental design
Incompatibility with range of staining applications
• Specific object orientation
• May require associated image registration to eliminate effect of
orientation e.g. polarised cells
• Stereological approach may be more applicable for anisotropic
(orientation dependent)
Pattern recognition - considerations
• Artefacts/facts
• Elimination of background artefacts that may hinder recognition
• Ensuring suitability of quantifiable tissue
• Influence of staining on PR spectral and texture feature detection
• Classifiers
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Appropriate segmentation of training data
Wide range of representative image features per class
Insufficient optimisation
Too many classes – increase computational requirements
Too few classes – noisy data
Misclassification
RESEARCH
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Project specific research areas
Disease models
Complementary technology
Imaging groups integration/collaboration
WHAT ARE THE QUESTIONS THAT
PATTERN RECOGNITION
IN DIGITAL PATHOLOGY
IS TRYING TO ADDRESS?
 Target expression &
functionality
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Disease models
Efficacy
Species variation
Toxicity, Safety
Primary diagnosis
Patient stratification
Prognostic value
Companion algorithms
Clinical translation
CLINICAL
PRE-CLINICAL
OBSERVE
Clinical biomarkers
DETECT
MEASURE
DEMONSTRATE
TRANSLATE
Pattern recognition software comparison
– Breast tumour TMA, progesterone receptor, IHC stained
Feature
selection
Interactive ROI
tools
Object based,
Paintbrush
Interactive ROI
tools
Paint brush
Interactive
object tools
Image
features
RGB spectral,
texture, density
RGB spectral,
object & pixel
based
RGB spectral,
density,
morphology
RGB spectral ,
texture & edges
Multispectral
Classifier
model
Mahalanobis
k-NN, SVM,
Bayes, CART
Bayes
Random forest
decision tree
Number of
classes/labels
2-
2-
Membership
functions &
conditional
reclassification
for fine tuning
Additions
Cell level
analysis
2-8
Requires
separate
algorithms
Integral for
continuous
workflow
2-
2-
Minimum class
area refinement
Requires
separate
algorithms
Integral for
continuous
workflow
Integral for
continuous
workflow
Time taken to develop classifiers ranging from 10 - 30 mins
Comparative image analysis: Breast TMA evaluation of Progesterone Receptor
labelling index using tumour pattern recognition and nuclear detection
Future application of Pattern recognition
technology and workflows
TISSUE
ARCHITECTURE
SPATIAL MAPPING
MORPHOLOGICAL
PATTERNS
TISSUE / TUMOUR
REGISTRATION
TUMOUR MAPPING
PHENOTYPIC FINGERPRINTING
TIER 2
INTRA-TUMOUR
SCAFFOLDING
‘NORMAL TISSUE
ENVIRONMENT’
DISTRIBUTION
Tissue/Cells
‘Exception
classification’
CONNECTIVITY
‘Misclassification
levels’
(SIMPLE/COMPOUND,
TUBULAR/ALVEOLAR)
NEIGHBOURHOOD
RELATIONSHIPS
TIER 3
CELL-CELL
HETEROGENEITY
ENVIRONMENT
DISTANCE
PROFILING
PATIENT
SELECTION
NUCLEAR
FEATURES
CLINICAL
OUTCOME
Future perspective of Digital Pathology Imaging
IHC, ISH, FISH
PLA, multiplexing
Right patient
Right molecular change
Right treatment
Brightfield,
fluorescence &
multispectral
Improved understanding of
Analysis of large data sets of WSI tumour heterogeneity and
associated tissue
reducing need for manual
microenvironment changes,
interactive processes.
both morphological &
Serial section combination
molecular level, in relation to
12% growth in the digital
analysis (SSCA)
tumour progression, prognosis
pathology scanning platform
and personalised medicines.
Increased utilisation of pattern
technology. Supporting pharma,
recognition, neural network
Companion algorithms.
research and clinical.
algorithms (machine learning)
Tumour/tissue pattern library.
Utilised on analysis of large
with improved data analysis,
modelling & mining applied to
image datasets.
Image & analysis standardisation. protein and molecular pathology.
Virtual microscopy to replace lab Alignment with other technologies
microscope.
e.g. mass spec imaging