Download View Slides - Digital Pathology Association

Pathology Visions 2007
Amsterdam Marriott Hotel
4-5 December 2007
Advances in
Automated Cytology Screening
Nikolai Ptitsyn
[email protected]
Innovators in
Light Management Technology
52 Shrivenham Hundred Business Park
Watchfield, Oxfordshire, SN6 8TY. UK
www.microsharp.co.uk
Cervical Cancer
• Malignant cancer of the cervix
• Represents a major women
health problem
• In some developing countries
commonest female cancer
• In developed countries
the widespread
use of programs
reduced the incidence of
cervical cancer by 50%
or more
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Age standardised incidence
and mortality rates in 2002
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Cervical Cancer Screening in England 2006
•
•
•
•
4.4 million women were invited for screening
3.6 million women were screened
4 million samples examined
75% of cancer cases prevented in women who attend
regularly cervical screening
• £150 millions is the overall cost to NHS
• Recent introduction of the vaccine against HPV
requires making more cost-effective use of limited
resources
– Eduardo Franco, "Process of care failures in invasive cervical cancer:
Systematic review and meta-analysis", Elsevier, Preventive Medicine, Volume
45, number 2-3, August-September 2007
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St George’s Healthcare
Project Summary
• Phase 1 : January 2005 – September 2007
–
–
–
–
digitise and analyse 3 500 cervical slides at 20-40X
develop methodology, algorithms and software
build the integrated screening system on top of Scanscope
complete a clinical study
• Phase 2 : October 2007 – December 2007
–
–
–
–
switch from class-based to regression-based model
extend the training dataset
fix recognition problems
repeat the tests against the existing dataset
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Key Research Outcome: New Diagnostic Approach
• Accurate quantification of different cells across entire
cytological specimen at full resolution in 5-10 minutes
– 103-106 cells found on ∅20 mm monolayer spot
• Cell global statistics
– tissue types, cancer stages, spatial densities
• Cell context description and analysis
– cell relationships and concurrence
• Nonlinear regression of a cancer
staging function in the feature space
– modeling visual changes during
development of cancer
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Fine Segmentation
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Multi Hypothesis Segmentation
• Black line: minor hypothesis (rejected)
• White line: dominant hypothesis (accepted)
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Cell Features
• Nucleus features
–
–
–
–
Shape (area, ellipse parameters, irregularity)
Border contrast and snake energy
Luminance and colors (average intensities per channel)
Chromatin distribution (radial, irregularities, particles)
• Cytoplasm features
– Shape (area, roundness)
– Fluid luminance and radial distribution
• Context features
– Local cell density
– Aggregated features of neighbour cells
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Cell Context Analysis
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Grading Problem Formalisation
• Problems
– Border instances
– Expert subjectivism
• Solution
– continuous cell state function as abnormality indicator
borderline
UK
grades
8: 3%
2: 94%
machine
grades
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no further review (NFR)
discrimination threshold
normal
moderate
mild
severe
7: <1%
3: 2%
cancers
5: <1%
4: 1%
6: <1%
review
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cell abnormality
Nonlinear regression of a cancer staging function
cancers
cancer
change
function
severe
moderate
mild
borderline
normal
…
cell features
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Cell abnormality distribution: normal vs severe sample
100000
number of cells on slide
10000
1000
normal
100
severe
10
1
1
2
3
4
←normal
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5
6
7
8
9
10
abnormal →
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Review Cell Spatial Density
Normal
Review
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Knowledge Database Problems Solved
• Unbalanced dataset
– majority normals taking over important reviews
• False positive and false negative error types are not
assigned different weights during classifier optimization
• Lack of ground truth, highly variable image quality
• Borderline objects
– on one side: are very important for earlier diagnostics
– on the other side: cannot be used for training and system
optimization
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Graphic User Interface
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class probability or
abnormality degree
(useful for ranking and
priority screening)
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Precancerous and Cancerous Status
Detection Performance
Performance indicator
Sept.
Dec.
target
normal sensitivity
(normals filtered as No Further Review)
>31%
>30%
HSIL, cancer
false negative rate (FNR)
<3%
<1%
ASC-US, LSIL, ASC-H
false negative rate (FNR)
<20%
<5%
• Source of false negatives (being fixed)
– Poor differentiation/weighting of precancer and cancer stages
– Segmentation problems with rare types of abnormal cells
– Trial dataset include low quality images from 2006
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Receiver Operating Characteristic (ROC) for HSIL+
expert
1
machine
type 2 error rates (false negatives)
1.2
0.8
0.6
review
(positive)
normal
(negative)
review
(positive)
true positive
(no error)
false positive
(type I error)
normal
(negative)
false negative
(type II error)
true negative
(no error)
0.4
current:
FNR < 3%
FPR ~ 68%
0.2
0
0
0.2
0.4
0.6
0.8
1
-0.2
3 month target
FNR < 1%
FPR ~ 70%
type 1 error rates (false positives)
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number of normal squamous cells
Improving the Discrimination
inadequate
slides
number of abnormal cells
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Slide Processing Speed
scanning and processing timeline
Aperio Scanscope:
scan
scan
scan
Recognition server: process
scan
process
scan
scan
process
process
average time per slide, minutes
Operation / performance indicator
Current
Optimal
8
6
10
6
3
0.5
13
7
Scanning, compressing and archiving digital images
Image processing, cell segmentation, feature extraction
Cell classification, statistics estimation and slide
classification
Average throughput with pipeline enabled
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Summary
• Cell abnormality statistics – new diagnostic approach
• Automated screening workflow
– 30% of normal slides can be assigned No Further Review
– remaining slides can be reviewed by an expert in minutes
thanks to the graphic annotation and probability ranking
– overall productivity increase at least 2 fold
• FNR will be reduced to <1% by the end of 2007
– easy to validate against the existing image dataset
• Cost-effectiveness of screening is becoming more
important with the introduction of the HPV vaccine
• Other applications: histology
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Summary: Digital Era Solution
• Compact and flexible storage of clinical data
• Remote access over a standard broadband
– remote screening
– easier multi-peer review and multi-site collaboration
• Ideal for clinical studies and education
• Feasible solution for the developing world
and sparse/remote countries
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BACKUP SLIDES
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System Configuration
• Image analysis software running on
running on Dual Xeon 3.2 GHz, 4
Mb, Windows x64
• Aperio Scanscope system T2X
– Olympus objective lense
UPlanSApo 20x / 0.75
– 120 slide autoloader
• Aperio digital pathology information
management system release 8
• Promise Ultratrek SX8000 RAID
storage 2 Tb at level 5
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Digital Slide Size and Storage Capacity
ThinPREP slide scan area
23 mm x 21 mm
Objective lens
Olympus UPlanSApo 20x / 0.75
Microns per pixel (MPP)
0.5
Digital image dimensions
46 000 x 42 000 pixels
Uncompressed image size
5.5 Gb
Average compressed file size
(including a 3 layer pyramid)
112 Mb (1:49 ratio)
Slide metadata
(including review annotation)
1 Mb
Typical storage capacity
2 Tb / 17 000 slides
4 Tb / 35 000 slides
8 Tb / 70 000 slides
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