The aim of this work is automatic and efficient detection of medically-relevant features from oral and dental hyperspectral images by applying up-to-date deep learning convolutional neural network techniques. This will help dentists to identify and classify unhealthy areas automatically and to prevent the progression of diseases. Hyperspectral imaging approach allows one to do so without exposing the patient to ionizing X-ray radiation. Spectral imaging provides information in the visible and near-infrared wavelength ranges. The dataset used in this paper contains 116 hyperspectral images from 18 patients taken from different viewing angles. Image annotation (ground truth) includes 38 classes in six different sub-groups assessed by dental experts. Mask region-based convolutional neural network (Mask R-CNN) is used as a deep learning model, for instance segmentation of areas. Preliminary results show high potential and accuracy for classification and segmentation of different classes.
Oleksandr Boiko, Joni Hyttinen, Pauli Fält, Heli Jäsberg, Arash Mirhashemi, Arja Kullaa, Markku Hauta-Kasari, "Deep Learning for Dental Hyperspectral Image Analysis" in Proc. IS&T 27th Color and Imaging Conf., 2019, pp 295 - 299, https://doi.org/10.2352/issn.2169-2629.2019.27.53