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
ISBI 2014 - FrB03.1 [Debdoot Sheet] 2
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?
ISBI 2014 - FrB03.1 [Debdoot Sheet] 3
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
ISBI 2014 - FrB03.1 [Debdoot Sheet] 4
State of the Art
2 May 2014
• 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
ISBI 2014 - FrB03.1 [Debdoot Sheet] 5
Incident radiation
Regularreflection Diffuse
reflection
Scattering
Absorption OCT
B. Saleh, Introduction to Subsurface Imaging, Cambridge, 2011.
2 May 2014
Optical Coherence Tomography
ISBI 2014 - FrB03.1 [Debdoot Sheet] 6
Low time-coherence light source
Depth scan mirror
Sample
Detector
Source beam
Reference beam
Sample beam
Detector beam
xz
z
OCT Image
Michelson interferometer
2 May 2014
Stochastic of TPI in SS-OCT
ISBI 2014 - FrB03.1 [Debdoot Sheet] 7
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
2 May 2014
S
S
SS
IIp
exp
1
Framework
2 May 2014 ISBI 2014 - FrB03.1 [Debdoot Sheet] 8
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
ISBI 2014 - FrB03.1 [Debdoot Sheet] 9
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
2 May 2014
ISBI 2014 - FrB03.1 [Debdoot Sheet] 10
In vitro validation towards In vivo translation
2 May 2014
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
2 May 2014 ISBI 2014 - FrB03.1 [Debdoot Sheet] 11
Top Related