Deep Learning of Tissue Specific Speckle Representations in Optical Coherence Tomography and Deeper Exploration for In situ Histology
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"Deep Learning of Tissue Specific Speckle Representations in Optical Coherence Tomography and Deeper Exploration for In situ Histology" presented at the Deep Learning for Biomedical Imaging session at the 2015 IEEE International Symposium on Biomedical Imaging (ISBI 2015) at Brooklyn, New York, USA.
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Deep Learning of Tissue Specific Speckle Representations in Optical Coherence Tomography and Deeper Exploration for In situ Histology
- 1. Deep Learning of Tissue Specific Speckle Representations in Optical Coherence Tomography and Deeper Exploration for In situ Histology Debdoot Sheet @ Department of Electrical Engineering, Indian Institute of Technology Kharagpur, India. Sri Phani Krishna Karri, Jyotirmoy Chatterjee @ School of Medical Science and Technology, Indian Institute of Technology Kharagpur, India Amin Katouzian, Nassir Navab @ Chair for Computer Aided Medical Procedures, TU Munich, Germany Ajoy K. Ray @ Electronics and Electrical Comm. Engg., Indian Institute of Technology Kharagpur, India. 1ISBI 2015 / FrDT3.5 - Deep Learning of Tissue Specific Speckle... - Debdoot Sheet
- 2. Motivation • Soft tissues – e.g. skin – Epithelial – Connective – Muscular – Adipose • Pathological markers – Extracellular matrix deposition – Cellular atypia and dysplasia – Loss of histo-architecture – Proliferative changes • Conventional histology – Patient discomfort – 48-72 hours delay in processing • Alternatives – Subsurface imaging • Optical coherence tomography (OCT) • Challenges with the alternative – Hard to interpret – Stochastic uncertainty of speckles ISBI 2015 / FrDT3.5 - Deep Learning of Tissue Specific Speckle... - Debdoot Sheet 2 Epithelium, Papillary dermis, Dermis, Adipose
- 3. Where do we stand now? ISBI 2015 / FrDT3.5 - Deep Learning of Tissue Specific Speckle... - Debdoot Sheet 3 This Paper Text books R. K. Das (2012), PhD Thesis A. Barui (2011), PhD Thesis D. Sheet et.al., ISBI 2014
- 4. State of the Art • In situ Histology with OCT – G. van Soest et al., (2010), G. J. Ughi et al., (2013) – Cardiovascular OCT – D. Sheet et al., (2013, 2014) – Cutaneous wounds, oral • Challenges – Heuristic features • Texture • Intensity statistics – Heuristic computational models • Transfer learning of speckle occurrence models – Incomplete representation dictionary ISBI 2015 / FrDT3.5 - Deep Learning of Tissue Specific Speckle... - Debdoot Sheet 4 Multi-scale modeling of OCT speckles Training image set Ground truth Random forest learning Multi-scale modeling of OCT speckles Test image Labeled tissue
- 5. Heuristics in State of Art ISBI 2015 / FrDT3.5 - Deep Learning of Tissue Specific Speckle... - Debdoot Sheet 5
- 6. The Solution ISBI 2015 / FrDT3.5 - Deep Learning of Tissue Specific Speckle... - Debdoot Sheet 6 DenoisingAutoEncoder DenoisingAutoEncoder LogisticReg.
- 7. Unfurling the Deep Network ISBI 2015 / FrDT3.5 - Deep Learning of Tissue Specific Speckle... - Debdoot Sheet 7
- 8. Learning of Representations ISBI 2015 / FrDT3.5 - Deep Learning of Tissue Specific Speckle... - Debdoot Sheet 8 Representation of speckle appearance models learned by DAE1
- 9. Learning of Representations ISBI 2015 / FrDT3.5 - Deep Learning of Tissue Specific Speckle... - Debdoot Sheet 9 Sparsity of representations learned by DAE2
- 10. Experiment Design • Data Collection – School of Medical Science and Technology, Indian Institute of Technology Kharagpur – 1300 nm (HPBW 100 nm) Swept Source OCT System • OCS 1300 SS, ThorLabs, NJ, USA • 8 bit bitmap images – Histology for ground truth • HE stained • Samples – Mus musculus (small mice) – 16 healthy skin – 2 wounds on skin • DNN architecture – Patch size – 36 × 36 px – DAE1 – 400 nodes – DAE2 – 100 nodes – Target – Logistic Reg. • 5 outputs – Sparsity – 20% – Mini-batch training • In situ Histology Performance – Epithelium – 96% – Papillary dermis – 93% – Dermis – 99% – Adipose tissue – 98% ISBI 2015 / FrDT3.5 - Deep Learning of Tissue Specific Speckle... - Debdoot Sheet 10
- 11. Results in Wounds ISBI 2015 / FrDT3.5 - Deep Learning of Tissue Specific Speckle... - Debdoot Sheet 11 (a) OCT image of wound (b) Ground truth (c) In situ histology Epithelium, Papillary dermis, Dermis, Adipose Epithelium, Papillary dermis, Dermis, Adipose
- 12. Take Home Message • Photons interact characteristically with different tissues. – Stochastic similarity exists in speckle appearance. – Such representations are hard to heuristically encode. • Deep learning and auto-encoders for computational imaging – Speckle imaging application viz. OCT tissue characterization – Hierarchical learning • Locally embedded representations. • Sparsity is in learned (auto-encoded) representations. ISBI 2015 / FrDT3.5 - Deep Learning of Tissue Specific Speckle... - Debdoot Sheet 12 Queries: Debdoot Sheet (debdoot@ee.iitkgp.ernet.in)