Automatic Quality Assessment of Whole Slide Images

8/11/2019 Automatic Quality Assessment of Whole Slide Images

1/14

Towards Better Digital Pathology Workflows:

Programming libraries for high-speedsharpness assessment of Whole Slide Images

David Ameisen - [email protected]
mailto:[email protected]:[email protected]


2/14

Quality assessment in WSI:

a key step towards systematic use WSI scanners - FDA Class III Medical Devices :

13% of Whole Slide Images need to be scanned at least twice.Campbell WS et al., Human Path, 2012;

Concordance between whole-slide imaging and light microscopy for routine surgical pathology.

A posteriori visual quality verification is insufficient: partial verification.

Automatic Quality Assurance tools are not adapted to high dimensions (>10 9 pixels).

Our perspective: Make WSI Quality Assessment tools fast enough for any pathology workflow.


3/14

7 years ofresearch in

WSIcollaborations

Quality

Assurance?

Major problem:

Blur

Research

Teaching

Clinical

Manual / Visual

Non-systematic

Non-quantified

Towards awidespread use

of WSI inpathology

Background


4/14

State of the artCurrent methods and limits of automated image quality assurance

No Fast Blur Detection and Quantification

Solution for Whole Slide Images

Auto-focus One shot

Comparison Gold standard

No reference Too slow

Z-stack Too big


5/14

Sharp; partially blurred; out of focus Absolute

Blur/SharpnessQuantification

Highly scalable, multi-core, multi-node

Compatibility: Windows, Mac OSX, Linux

No gold standard required

A Fast Blur Detection and QuantificationSolution for Whole Slide Images

3 billion pixels analyzed per minute on a recent computer(10 to 100 times faster than other methods)


6/14

6

Whole Slide Imagechecked at all magnifications


7/14

20x : 1.7 Billion pixels6589 tiles of 512x512px24% relevant tiles (non-blank)amon which 25% are


8/14

Video : real-time

analysis

https://www.youtube.com/watch?v=eIunSx-a-ug
https://www.youtube.com/watch?v=eIunSx-a-ughttps://www.youtube.com/watch?v=eIunSx-a-ug


9/14

5000 images analyzed in our labInternet survey:

100 images, 7500+ answers


10/14

Correlation algorithm/user votesImage classification into three groups:Out of focus, Partially blurred, Sharp


11/14

DuringWSI

digitization

Applications :Saving time, increasing the quality

Identification and reacquisition of blurred tiles during thescanCertification of the WSIs general image quality

Just afterWSI

digitization

Reacquisition of the WSI with more suitable profiles

Notification sent to reacquire the WSI

ContinuousQuality

Assusrance

Notification sent to recalibrate the WSI acquisition system

WSI quality scoring integrated in the files metadata


12/14

C++Library

ImplementationsScaling

Optimized, multi-core, multi-node

Highly scalable to match any throughput

APIsC++Java

Python

Embeddable in Whole Slide Image acquisition systems

Embeddable in Whole Slide Image management platforms

Embeddable in Whole Slide Image viewers


13/14

Our references

Talk, 11th European Congress on Telepathology5th International Congress on Virtual Microscopy6-9 June 2012, Venice, Italy

Poster, Pathology Visions Conference ,October, 28-31, 2012, Baltimore, MA, USA.

Talk, 12th European Congress on Digital Pathology18-21 June 2014, Paris, France

FlexMIm - Projet R&D - 4 989 K Fonds Unique Interministriel n 142013-2015, Paris, France.

Talk, Pathology Visions Conference ,October, 20-21, 2014, San Francisco, CA, USA.

pre-seed funding


14/14

Contact :

Laboratory : David Ameisen - [email protected] Idfinnov : Robert Marino [email protected]
mailto:[email protected]:[email protected]:[email protected]:[email protected]

Automatic Quality Assessment of Whole Slide Images

Documents

Transcript of Automatic Quality Assessment of Whole Slide Images