Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.
Transfer Learning of Tissue Photon
Interaction in Optical Coherence
Tomography towards In vivo Histology of
the Oral Mucos...
Motivation
• Mucosa forms the general internal
lining of the oral cavity protecting it
from harsh external influences.
• S...
Where do we stand now?
ISBI 2014 - FrB03.1 [Debdoot Sheet] 3
This Paper
2 May 2014
Text books
R. K. Das (2012), PhD Thesis...
State of the Art
2 May 2014 ISBI 2014 - FrB03.1 [Debdoot Sheet] 4
• In situ investigation
– Optical Coherence Tomography (...
Tissue Photon Interaction
ISBI 2014 - FrB03.1 [Debdoot Sheet] 5
Incident
radiation
Regular
reflection Diffuse
reflection
S...
Optical Coherence Tomography
ISBI 2014 - FrB03.1 [Debdoot Sheet] 6
Low time-coherence
light source
Depth scan mirror
Sampl...
Stochastic of TPI in SS-OCT
ISBI 2014 - FrB03.1 [Debdoot Sheet] 7
Source
Ballistic
backscattering
Non-ballistic
backscatte...
Framework
2 May 2014 ISBI 2014 - FrB03.1 [Debdoot Sheet] 8
Learn TPI Model
(i) Multiscale estimated speckle statistics
(ii...
Experiment Design
• Data Collection:
– Multimodal Imaging and
Computing for Theranostics,
School of Medical Science and
Te...
In vitro
validation
towards
In vivo
translation
2 May 2014 ISBI 2014 - FrB03.1 [Debdoot Sheet] 10
Area under the ROC Curve...
Take Home Message
• Photons interact characteristically with different tissues.
– This is manifested through the stochasti...
Upcoming SlideShare
Loading in …5
×

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

1,493 views

Published on

Published in: Technology, Health & Medicine
  • Be the first to comment

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

  1. 1. 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, India Amita 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
  2. 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 challenge 2 May 2014 ISBI 2014 - FrB03.1 [Debdoot Sheet] 2
  3. 3. Where do we stand now? ISBI 2014 - FrB03.1 [Debdoot Sheet] 3 This Paper 2 May 2014 Text books R. K. Das (2012), PhD Thesis A. Barui (2011), PhD Thesis
  4. 4. State of the Art 2 May 2014 ISBI 2014 - FrB03.1 [Debdoot Sheet] 4 • 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
  5. 5. Tissue Photon Interaction ISBI 2014 - FrB03.1 [Debdoot Sheet] 5 Incident radiation Regular reflection Diffuse reflection Scattering Absorption OCT B. Saleh, Introduction to Subsurface Imaging, Cambridge, 2011. 2 May 2014
  6. 6. 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 x z z OCT Image Michelson interferometer 2 May 2014
  7. 7. Stochastic of TPI in SS-OCT ISBI 2014 - FrB03.1 [Debdoot Sheet] 7 Source Ballistic backscattering Non-ballistic backscattering Reference Detector A. F. Fercher, et al, Optical coherence tomography — principles and applications, Rep. Prog. Phys. 66 (2003) 239–303 Epithelium Sub-epithelium Speckle intensity Probability density 2 May 2014          S S S S I Ip  exp 1
  8. 8. 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, xH
  9. 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] 9
  10. 10. In vitro validation towards In vivo translation 2 May 2014 ISBI 2014 - FrB03.1 [Debdoot Sheet] 10 Area under the ROC Curve Epithelium = 0.9611 Sub-epithelium = 0.9367
  11. 11. 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

×