Monte Carlo investigation of deep learning tissue classification performance in OCT-based smart laser bone surgery (Conference Presentation)

Automatic tissue classification using optical coherence tomography (OCT) explores the possibility to control laser ablation in prevention for collateral damage of critical tissues. During ablation, tissue experience thermal dissipation which induces mechanical expansion and optical properties alteration. We reconstructed OCT images of bone, fat, and muscle tissues for pre and post ablation temperatures condition using Monte Carlo simulation. We trained a deep neural network to recognize tissue type based on reconstructed OCT images with pre-ablation temperature condition and tested it on post-ablation temprature condition. The reconstructed images show small changes in the tissue structure but do not significantly affect the performance of the classifier.