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Advances in Automated Cytology Screening

Pathology Visions 2007 Amsterdam Marriott Hotel 4-5 December 2007. Advances in Automated Cytology Screening. Nikolai Ptitsyn N.Ptitsyn@microsharp.co.uk. www.microsharp.co.uk. Cervical Cancer. Malignant cancer of the cervix Represents a major women health problem

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Advances in Automated Cytology Screening

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  1. Pathology Visions 2007Amsterdam Marriott Hotel 4-5 December 2007 Advances inAutomated Cytology Screening Nikolai Ptitsyn N.Ptitsyn@microsharp.co.uk www.microsharp.co.uk

  2. Cervical Cancer • Malignant cancer of the cervix • Represents a major womenhealth problem • In some developing countriescommonest female cancer • In developed countriesthe widespreaduse of programsreduced the incidence of cervical cancer by 50%or more Age standardised incidence and mortality rates in 2002 Advances in automated cytology screening

  3. 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 Advances in automated cytology screening

  4. 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 Advances in automated cytology screening

  5. 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 duringdevelopment of cancer Advances in automated cytology screening

  6. Fine Segmentation Advances in automated cytology screening

  7. Multi Hypothesis Segmentation • Black line: minor hypothesis (rejected) • White line: dominant hypothesis (accepted) Advances in automated cytology screening

  8. 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 Advances in automated cytology screening

  9. Cell Context Analysis Advances in automated cytology screening

  10. Grading Problem Formalisation • Problems • Border instances • Expert subjectivism • Solution • continuous cell state function as abnormality indicator borderline normal moderate cancers severe mild UKgrades 8: 3% 7: <1% 5: <1% discrimination threshold 2: 94% 3: 2% 4: 1% 6: <1% machinegrades no further review (NFR) review Advances in automated cytology screening

  11. Nonlinear regression of a cancer staging function cancers cancer changefunction severe cell abnormality moderate mild borderline roundness normal density … cell features Advances in automated cytology screening

  12. Cell abnormality distribution: normal vs severe sample number of cells on slide ←normal abnormal → Advances in automated cytology screening

  13. Review Cell Spatial Density Normal Review Advances in automated cytology screening

  14. 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 Advances in automated cytology screening

  15. class probability orabnormality degree(useful for ranking and priority screening) Graphic User Interface Advances in automated cytology screening

  16. Precancerous and Cancerous StatusDetection Performance • 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 Advances in automated cytology screening

  17. Receiver Operating Characteristic (ROC) for HSIL+ expert machine type 2 error rates (false negatives) current:FNR < 3%FPR ~ 68%3 month target FNR < 1%FPR ~ 70% type 1 error rates (false positives) Advances in automated cytology screening

  18. Improving the Discrimination REVIEW discrimination line NO FURTHER REVIEW number of normal squamous cells inadequate slides number of abnormal cells Advances in automated cytology screening

  19. Slide Processing Speed scanning and processing timeline Aperio Scanscope: scan scan scan scan scan scan Recognition server: process process process process average time per slide, minutes Advances in automated cytology screening

  20. 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 Advances in automated cytology screening

  21. 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 worldand sparse/remote countries Advances in automated cytology screening

  22. BACKUP SLIDES Advances in automated cytology screening

  23. 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 Advances in automated cytology screening

  24. Digital Slide Size and Storage Capacity Advances in automated cytology screening

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