Segmented OCT data set for depth resolved brain tumor detection validated by histological analysis

The aim of this work is the creation of segmented data set consisting of optical coherence tomography (OCT) scans, which were taken of brain tumor tissue with different tumor infiltration rates. In an ongoing clinical study more than 140 human brain samples with different infiltration grades were recorded ex vivo with two OCT systems, a spectral domain OCT system and a swept-source OCT system that uses a 1310 nm Fourier domain mode locked laser. The histological analysis of the recorded samples builds the ground truth for labeling the corresponding OCT B-Scans. The segmented data set gained from this process will be used to train a classification algorithm, taking into account structural and optical properties such as the attenuation coefficient. In the future the classification algorithm together with a microscope integrated OCT system will be used for the in vivo identification of brain tumors as a guidance tool for the surgeon to increase tumor resection efficiency.