Neural network-based delineation of clinical target volumes for glioma patients

Outlining the clinical target volume (CTV) in radiotherapy can be time-consuming and error-prone. We propose a convolutional neural network (CNN)-assisted delineation of the CTV for glioma, aiming to reduce inter- and intra-observer variability and decrease treatment planning time. Microscopic disease spread in the brain is restricted by anatomical barriers that are impenetrable by tumor cells. These brain barrier structures were automatically segmented using a 3D CNN trained on 25 datasets of registered planning CT and diagnostic MR images. Satisfactory results were obtained for segmentation of skull, brainstem, corpus callosum, cerebellum, falx cerebri, brain sinuses, tentorium, and venticles. Segmentation quality was assessed by comparing CNN-derived and manually drawn structures using an independent dataset. The Dice score ranged from 73% to 96% and did not improve after more patients were added to the training dataset. After segmentation, the CTV was generated by expanding the gross tumor volume (GTV) by a fixed radius, excluding voxels contained in other segmented structures. We will compare CNN-derived CTV quality with manually delineated CTVs for a large set of 100 patients.