www.microsharp.co.uk 52 shrivenham hundred business park watchfield, oxfordshire, sn6 8ty. uk...

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


www.microsharp.co.uk 52 shrivenham hundred business park watchfield, oxfordshire, sn6 8ty. uk...

Documents

cervical cancer screening

incidence of cervical

invasive cervical cancer

cancer stages

development of cancer

cancer cases

cervical screening

cell context analysis