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Radiation therapy has emerged as one of the preferred techniques to treatbrain cancer patients. During treatment, a very high dose of radiation isdelivered to a very narrow area. Prescribed radiation therapy for brain cancerrequires precisely defining the target treatment area, as well as delineatingvital brain structures which must be spared from radiotoxicity. Nevertheless,delineation task is usually still manually performed, which is inefficient andoperator-dependent. Several attempts of automatizing this process havereported. however, marginal results when analyzing organs in the optic region.In this work we present a deep learning classification scheme based onaugmented-enhanced features to automatically segment organs at risk (OARs) inthe optic region -optic nerves, optic chiasm, pituitary gland and pituitarystalk-. Fifteen MR images with various types of brain tumors wereretrospectively collected to undergo manual and automatic segmentation. MeanDice Similarity coefficients around 0.80 were reported. Incorporation ofproposed features yielded to improvements on the segmentation. Compared withsupport vector machines, our method achieved better performance with lessvariation on the results, as well as a considerably reduction on theclassification time. Performance of the proposed approach was also evaluatedwith respect to manual contours. In this case, results obtained from theautomatic contours mostly lie on the variability of the observers, showing nosignificant differences with respect to them. These results suggest thereforethat the proposed system is more accurate than other presented approaches, upto date, to segment these structures. The speed, reproducibility, androbustness of the process make the proposed deep learning-based classificationsystem a valuable tool for assisting in the delineation task of small OARs inbrain cancer.

A deep learning classification scheme based on augmented-enhanced features to segment organs at risk on the optic region in brain cancer patients