Transfer Learning of Tissue Photon Interaction in Optical Coherence Tomography towards In vivo Histology of the Oral Mucosa

Debdoot Sheet, Satarupa Banerjee, Sri Phani Krishna Karri, Swarnendu Bag, Anji Anura, Ajoy K. Ray@ School of Medical Science and Technology, Indian Institute of Technology Kharagpur, IndiaAmita Giri, @ Department of Pathology, North Bengal Medical College and Hospital, Darjeeling, India.Ranjan Rashmi Paul, Mousumi Pal, @ Oral and Maxillofacial Pathology, Guru Nanak Institute of Dental Sc. and Res., Kolkata, India.Badal C. Sarkar, Ranjan Ghosh, @ Oral and Maxillofacial Pathology, North Bengal Dental College and Hospital, Darjeeling, India.Amin Katouzian, Nassir Navab@ Chair for Computer Aided Medical Procedures, TU Munich, Germany

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Motivation• Mucosa forms the general internal lining of

the oral cavity protecting it from harsh external influences.

• Stratified organization– Stratified squamous epithelium– Basement membrane– Lamina propria

• Cancers and Pre-cancers – Major pathological injury – Loss of stratified structure– Dysplasia

• Clinical challenge in management– Early diagnosis of cancer / pre-cancer onset– Patient specific intervention– In situ investigation of pre-cancer and cancer

progression is challenge2 May 2014

Where do we stand now?

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Intuitive and descriptive biology of tissues

Histology, molecular pathology and semi-quantitative evaluation

Joint analysis of structural and molecular attributes and co-located complexity of tissues through multimodal imaging

Learning of uncertainty in tissue energy interaction in imaging to understand co-located tissue heterogeneity towards in situ Histopathology

This Paper

2 May 2014

Text books

R. K. Das (2012), PhD Thesis

A. Barui (2011), PhD Thesis

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State of the Art

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• In situ investigation– Optical Coherence Tomography (OCT)

• Rebol, (2008)• Jung et al., (2005)

• In situ Histology with OCT– G. van Soest et al., (2010) –

Cardiovascular OCT– A. Barui et al., (2011) – Cutaneous

wound beds.– D. Sheet et al., (2013) – Cutaneous

wounds

• Challenges– Identify co-located tissue heterogeneity– Identify and discriminate rete-peg

architecture and inter-digitated structures

Tissue Photon Interaction

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Incident radiation

Regularreflection Diffuse

reflection

Scattering

Absorption OCT

B. Saleh, Introduction to Subsurface Imaging, Cambridge, 2011.

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Optical Coherence Tomography

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Low time-coherence light source

Depth scan mirror

Sample

Detector

Source beam

Reference beam

Sample beam

Detector beam

xz

z

OCT Image

Michelson interferometer

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Stochastic of TPI in SS-OCT

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Source

Ballistic backscattering

Non-ballisticbackscattering

Reference

Detector

A. F. Fercher, et al, Optical coherence tomography — principles and applications, Rep. Prog. Phys. 66 (2003) 239–303

EpitheliumSub-epithelium

Speckle intensity

Probability density

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S

S

SS

IIp

exp

1

Framework

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Learn TPI Model(i) Multiscale estimated speckle statistics(ii) Attenuation coefficient

Training Image Ground Truth Labels

Test Image

Learn TPI Model

Characterized tissue

train;,| II, xH

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Experiment Design• Data Collection:

– Multimodal Imaging and Computing for Theranostics, School of Medical Science and Technology, Indian Institute of Technology Kharagpur

– Imaging: Swept Source OCT System

– OCS 1300 SS, ThorLabs, NJ, USA– In vitro preserved Biopsy

• HE stained

• Cross validation:– 4 fold cross validation

• Samples– Normal # 1– Oral Sub-mucous Fibrosis # 1– Oral Leukoplakia # 1– Oral Lichen-planus # 1

• Learning:– Source task:

• {μ,σ} at 10 scales • Attenuation coefficient (van Soest

et al., (2010))

– Target task: • Random forest with 50 binary

decision trees

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In vitro validation towards In vivo translation

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Area under the ROC Curve

Epithelium = 0.9611Sub-epithelium = 0.9367

Take Home Message

• Photons interact characteristically with different tissues.– This is manifested through the stochastic convergence of OCT speckle

intensity. – Also manifested in the form of optical intensity attenuation.

• The stochastic nature of TPI accounts for uncertainties in observations.– Learning of TPI statistical physics overcomes these uncertainties.

• Transfer learning is a good framework for solving stochastic convergent signal decomposition problems– Speckle imaging application viz. OCT tissue characterization– Learn (weak) local uncertainty of signals– Learn (strong) the uncertainty associated with tissue types

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