Dual wavelength analysis and classification of brain tumor tissue with optical coherence tomography

Abstract

The ill-defined tumor borders of glioblastoma multiforme pose a major challenge for the surgeon during tumor resection, since the goal of the tumor resection is the complete removal, while saving as much healthy brain tissue as possible. In recent years, optical coherence tomography (OCT) was successfully used to classify white matter from tumor infiltrated white matter by several research groups. Motivated by these results, a dataset was created, which consisted of sets of corresponding ex vivo OCT images, which were acquired by two OCT-systems with different properties (e.g. wavelength and resolution). Each image was annotated with semantic labels. The labels differentiate between white and gray matter and three different stages of tumor infiltration. The data from both systems not only allowed a comparison of the ability of a system to identify the different tissue types present during the tumor resection, but also enable a multimodal tissue analysis evaluating corresponding OCT images of the two systems simultaneously. A convolutional neural network with dirichlet prior was trained, which allowed to capture the uncertainty of a prediction. The approach increased the sensitivity of identifying tumor infiltration from 58 % to 78 % for data with a low prediction uncertainty compared to a previous monomodal approach.