advances in automated cytology screening

www.microsharp.co.uk52 Shrivenham Hundred Business ParkWatchfield, Oxfordshire, SN6 8TY. UK

Innovators inLight Management Technology

Advances inAutomated Cytology Screening

www.microsharp.co.uk

Nikolai [email protected]

Pathology Visions 2007Amsterdam Marriott Hotel4-5 December 2007

mailto:[email protected]

Cervical Cancer

07/11/2007 Advances in automated cytology screening 2

• Malignant cancer of the cervix• Represents a major women

health problem• In some developing countries

commonest female cancer• In developed countries

the widespreaduse of programsreduced the incidence of cervical cancer by 50%or more Age standardised incidence

and mortality rates in 2002

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


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


St George’s Healthcare

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 cancerstaging function in the feature space– modeling visual changes during

development of cancer


Fine Segmentation


Multi Hypothesis Segmentation

• Black line: minor hypothesis (rejected)• White line: dominant hypothesis (accepted)


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


Cell Context Analysis


2: 94%

Grading Problem Formalisation

• Problems– Border instances

– Expert subjectivism

• Solution– continuous cell state function as abnormality indicator


3: 2%

8: 3%

4: 1%

5: <1%

6: <1%

no further review (NFR) review

UKgrades

machinegrades

normal cancersdiscrim

ination threshold

borderline

severemild

moderate

7: <1%

Nonlinear regression of a cancer staging function


cell

ab

no

rmal

ity

cell features

roundness

density

…

normal

cancers

borderline

severe

mild

moderate

cancerchangefunction

Cell abnormality distribution: normal vs severe sample


←normal abnormal →

num

ber

of c

ells

on

slid

e

1

10

100

1000

10000

100000

1 2 3 4 5 6 7 8 9 10

normal

severe

Review Cell Spatial Density


Normal

Review

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


Graphic User Interface


class probability orabnormality degree

(useful for ranking and priority screening)


• 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

Precancerous and Cancerous StatusDetection Performance

Performance indicator Sept.Dec.

target

normal sensitivity (normals filtered as No Further Review)

>31% >30%

HSIL, cancerfalse negative rate (FNR)

<3% <1%

ASC-US, LSIL, ASC-Hfalse negative rate (FNR)

<20% <5%

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

0 0.2 0.4 0.6 0.8 1

Receiver Operating Characteristic (ROC) for HSIL+


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)

expert

mac

hin

e

type 1 error rates (false positives)

type

2 e

rror

rat

es (

fals

e ne

gativ

es)

current:FNR < 3%FPR ~ 68%

3 month targetFNR < 1%FPR ~ 70%

current:FNR < 3%FPR ~ 68%

3 month targetFNR < 1%FPR ~ 70%

Improving the Discrimination


NO

FU

RT

HE

R R

EV

IEW

RE

VIE

W

number of abnormal cells

num

ber

of n

orm

al s

qua

mou

s ce

lls

disc

rimin

atio

n lin

edi

scrim

inat

ion

line

inadequate slides

Slide Processing Speed


Operation / performance indicator Current Optimal

Scanning, compressing and archiving digital images 8 6

Image processing, cell segmentation, feature extraction 10 6

Cell classification, statistics estimation and slide classification

3 0.5

Average throughput with pipeline enabled 13 7

average time per slide, minutes

scan

process

scan scan scan scan scan

process process process

Aperio Scanscope:

Recognition server:

scanning and processing timeline

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


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


BACKUP SLIDES


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


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 slides4 Tb / 35 000 slides8 Tb / 70 000 slides


advances in automated cytology screening

Documents

automated cytology screening2

integrated screening

incidence of cervical

invasive cervical cancer

cancer stages

malignant cancer

cancer cases

cancer staging function