Deep Learning Algorithm for Auto-Delineation of High-Risk Oropharyngeal Clinical Target Volumes with Built-in Dice Similarity Coefficient Parameter Optimization Function

Automating and standardizing the contouring of clinical target volumes (CTVs) can reduce inter-physician variability which is one of the largest sources of uncertainty in head and neck radiotherapy. Besides using uniform margin expansions to auto-delineate high-risk CTVs, very little work has been done to provide patient and disease specific high-risk CTVs. The aim of this study is to develop a deep neural network for the auto-delineation of high-risk CTVs.

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