Reinforcing Digital Pathology as the new Big Data

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Reinforcing Digital Pathology as the new Big Data

Microscope

Illustration with two recent studies

Nicolas Loménie

http://www.math-info.univ-paris5.fr/~lomn/

December 19

^th

, 2017

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The Big Picture

Mainstream Research Line in Medical Imaging

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Mohammad Havaei, Axel Davy, David Warde-Farley, Antoine Biard, Aaron Courville, Yoshua Bengio, Chris Pal, Pierre-Marc Jodoin, Hugo Larochelle, Brain tumor segmentation with Deep Neural Networks, Medical Image Analysis, Volume 35, January 2017, Pages 18-31

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The Big Picture

But we cannot model histopathological images the same way

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The Big Picture

But we cannot model histopathological images the same way

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Outline

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Building up a community ;

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Defining new methodologies or Reinforcing existing ones ? ;

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Building up a community

First Contact - 2009-2012

PUPH Frédérique Capron - Hospital La Pitié Salpêtrière

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In Singapore, UMI IPAL -> ANR MICO (2010-2013) project with Frédérique Capron and

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Explore the concept of cognitive microscopy

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Building up a community

Whole Slide Image (WSI)

Biopsy glass slide

Ü Scan at

40X Resolution :

0.2456µm per pixel

Ü

Whole Slide Image 8 GPixels

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Multi-page TIFF image

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File size (compressed) : Usually 1 to 2 GB

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Image size (uncompressed) : 15 to 25 GB

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Mitosis Detection Contest

An ICPR 2012 Contest

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Roux Ludovic, Racoceanu Daniel, Loménie Nicolas, Kulikova Maria, Irshad Humayun, Klossa Jacques, Capron Frédérique, Genestie Catherine, Le Naour Gilles, N Gurcan Metin Mitosis detection in breast cancer histological images : An ICPR 2012 contest J Pathol Inform 2013, 4 :8 (30 May 2013)

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Challenges (An ICPR 2012 Contest)

125 registered contestants in 39 countries - IPAL lab - Singapore

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Cited in IEEE Journal of Biomedical and Health Informatics, vol. 18, no. 2, March 2014 : Toward Automatic Mitotic Cell Detection and Segmentation in Multispectral Histopathological Images.

67 citations up to now

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Mitosis Detection + Nuclear Atypia Grand Challenge etc.

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Building up a community

2017 Refueling it : Master Internship : Denfeng CAO - Master BME (2017) HEGP - INSERM / PUPH Cecile Badoual

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Objective : Automated HPV Cancer analysis + Spatial analysis of cell microenvironement

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Correlate immune response and micro-environment analysis of the cell to cancer pronostic

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Building up a community

INFORM software

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Objective : Automated HPV Cancer analysis + Spatial analysis of cell microenvironement

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Unité de Technologies Chimiques et Biologiques pour la Santé - CNRS /INSERM

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PUPH Jean-François Emile (Ambroise Paré) - DR Nathalie Mignet

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Objective : Lymphocyte Infiltration : VISILOG software

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UTCBS - CNRS /INSERM

Master Internship : Tiede Armand DJIRO - Master Plurimedia (2017)

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PUPH Jean-François Emile (Ambroise Paré) - DR Nathalie Mignet

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Objective : Lymphocyte Infiltration : VISILOG software

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Analysis of the micro-environment of the tumor (mice model)

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CEA - MIRCen Thierry Delzescaux

PhD Mid-term committee - Clément Bouvier

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Science. 2013 Jun 21 ;340(6139) :1472-5. doi : 10.1126/science.1235381.

BigBrain : an ultrahigh-resolution 3D human brain model. Amunts K1, Lepage C, Borgeat L, Mohlberg H, Dickscheid T, Rousseau MÉ, Bludau S, Bazin PL, Lewis LB, Oros-Peusquens AM, Shah NJ, Lippert T, Zilles K, Evans AC

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Sainte-Anne - INSERM

PUPH Catherine Oppenheim and PhD. MD. Pascale Varlet

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Objective : IMABRAIN (Associate Member)

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Multi-scale analysis of the brain (pathology and development)

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Outline

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Building up a community ;

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Defining new methodologies or Reinforcing existing ones ? ;

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Methodological innovation...

Graph-based analysis and Digital Histopathology ?

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A Hybrid Classification Model for Digital Pathology Using Structural and Statistical - Pattern Recognition - Erdem Ozdemir and Cigdem Gunduz-Demir -

IEEE Transactions on Image Processing - vol. 32, no. 2, February 2013

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...but we need basic,efficient software first...

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... leading to immediate valorisation

Figure:Diagram flow of the entire process line from WSI processing to infiltration curve generation

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... leading to immediate valorisation

with open solution like Cytomine

Figure:Annotation of the tumor boundary

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... leading to immediate valorisation

Figure:Illustration of lymphocyte detection. (a) RGB image of colorectal carcinoma. (b) staining séparation. (c) Otsu and K-means (two clusters)

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... leading to immediate valorisation

-> Innovation : SATT, Companies, Companion Test (Nov. 2017)

Figure:Illustration of creating similar borders. (a) Annotation of a tumor boundary identical to the ROI. (b) Normalised outside distance card. (c) Normalised inside distance card. (d) and (e) similar borders by distance 5µm (configurable).

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... and more to come,

possibly a portfolio by institutional partner.

Figure:Illustration of the process of creating curves. (a) Annotation of tumor border and region of interest (ROI), (b) treatment of ROI and creation of similar border at tumor border, (c) detection of lymphocytes, (d) repositioning of ROI and assignment of pixels and (e) the

quantitation curve of lymphocyte infiltration.

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... and more to come,

possibly a portfolio by institutional partner.

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... and more to come,

possibly a portfolio by institutional partner.

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... and more to come,

possibly a portfolio by institutional partner.

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Wrap up

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Emerging field ;

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More and more graph-based analysis ;

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Emerging Community : http://www.math-info.

univ-paris5.fr/~lomn/Workshop/WorkshopHisto.html

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Lomenie and Racoceanu. (2013).Point set morphological filtering and semantic spatial configuration modeling: application to microscopic image and bio-structure analysis,Pattern Recognition,45(8) :2894-2911.

Ta, Lezoray, Elmoataz and Schupp. (2009).Graph-based Toolsfor Microscopic Cellular Image Segmentation,Pattern Recognition,42(6) :1113-25.

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Wrap up

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Emerging field ;

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More and more graph-based analysis ;

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Emerging Community : http://www.math-info.

univ-paris5.fr/~lomn/Workshop/WorkshopHisto.html

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Andrew Janowczyk, Scott Doyle, Hannah Gilmore, Anant Madabhushi -A resolution adaptive deep hierarchical (radhical)learning scheme applied to nuclear segmentation of digital pathology images - 2016/4/1 - Computer Methods in Biomechanics and Biomedical Engineering : Imaging &

Visualization

Basavanhally, Ganesan, Agner, Monaco, Feldman, Tomaszewski. (2010) Computerized image-based detection andgrading of lymphocytic infiltrationin HER2+ breast cancer histopathology,IEEE Trans Biomed Eng.,57:642-653.

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Wrap up

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Digital histopathology and deep learning coming up ;

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Databases under construction with challenges :

• http://ccdb.ucsd.edu/

• https://cancergenome.nih.gov

• https://tma.im/cgi-bin/home.pl

• http://web.inf.ufpr.br/vri/breast-cancer-database

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When machine vision meets histology: A comparative evaluation of model architecture for classification of histology sections - Zhonget al.- Medical Image Analysis - 2017, Volume 35, Issue 3, Pages 530–543

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Horizon

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Coming into Focus : Computational Pathology asthe New Big Data Microscope- The American Journal of Pathology - Cellular and Molecular Biology of disease - Kevin A. Roth, r Kevin A. Roth - March 2015, Volume

185, Issue 3, Pages 600–601

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Thank You for your Attention

We miss grant call for us

Nicolas Loménie, Eng., Ph.D., HDR

Laboratoire d’Informatique de Paris Descartes (LIPADE)

Equipe d’Acceuil EA 2917

Groupe Systèmes Intelligents de Perception (SIP) http ://w3.mi.parisdescartes.fr/sip-lab/

&

IMABRAIN - INSERM

Hospital Sainte Anne

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