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

Abstract

Radiation therapy has emerged as one of the preferred techniques to treat brain cancer patients. During treatment, a very high dose of radiation is delivered to a very narrow area. Prescribed radiation therapy for brain cancer requires precisely defining the target treatment area, as well as delineating vital brain structures which must be spared from radiotoxicity. Nevertheless, delineation task is usually still manually performed, which is inefficient and operator-dependent. Several attempts of automatizing this process have reported, however, marginal results when analyzing organs in the optic region. In this work we present a deep learning classification scheme based on augmented-enhanced features to automatically segment organs at risk in the optic region -optic nerves, optic chiasm, pituitary gland and pituitary stalk-. Fifteen MR images with various types of brain tumors were retrospectively collected to undergo manual and automatic segmentation. Mean Dice Similarity coefficients of 0.79, 0.83, 0.76 and 0.77, respectively, were reported in this study. Incorporation of proposed features yielded to improvements on the segmentation with respect to classical features. Compared with support vector machines, our method achieved better performance with less variation on the results, as well as a considerably reduction on the classification time. Performance of the proposed approach was also evaluated with respect to manual contours. In this case, results obtained from the automatic contours mostly lie on the variability of the observers. Additionally, in cases where our method was under performing with respect to manual raters, statistical analysis showed that there were not significant differences between them. These results suggest therefore that the proposed system is more accurate than other presented approaches, up to date, to segment these structures. The speed, reproducibility, and robustness of the process make the proposed deep learning-based classification system a valuable tool for assisting in the delineation task of small OARs in brain cancer.

A recursive ensemble organ segmentation (REOS) framework: application in brain radiotherapy

“…Dolz et al (2015) used a support vector machine (SVM) algorithm to segment optical nerves on magnetic resonance images (MRI) and a mean DSC of 76% was reported across 15 patients. Their later study (Dolz et al 2017) showed improvements on the optical nerves (DSC 79%) and the chiasm (DSC 83%) via a deep-learning framework combined with hand-crafted features augmentation, though the model was only validated on a small patient cohort (15 patients). Similarly, Ren et al (2018) developed an interleaved 3D-CNNs to segment small-volume structures on H&N CT images, where multi-scale image patches were fed into multi-channel CNNs to yield channel-wise probabilities, which were then interleaved and jointly used to classify voxels.…”