Deep learning of tissue specific speckle representations in optical coherence tomography and deeper exploration for in situ histology

Optical coherence tomography (OCT) relies on speckle image formation by coherent sensing of photons diffracted from a broadband laser source incident on tissues. Its non-ionizing nature and tissue specific speckle appearance has leveraged rapid clinical translation for non-invasive high-resolution in situ imaging of critical organs and tissue viz. coronary vessels, healing wounds, retina and choroid. However the stochastic nature of speckles introduces inter- and intra-observer reporting variability challenges. This paper proposes a deep neural network (DNN) based architecture for unsupervised learning of speckle representations in swept-source OCT using denoising auto-encoders (DAE) and supervised learning of tissue specifics using stacked DAEs for histologically characterizing healthy skin and healing wounds with the aim of reducing clinical reporting variability. Performance of our deep learning based tissue characterization method in comparison with conventional histology of healthy and wounded mice skin strongly advocates its use for in situ histology of live tissues.