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Clinical target volume (CTV) delineation from radiotherapy computedtomography (RTCT) images is used to define the treatment areas containing thegross tumor volume (GTV) and/or sub-clinical malignant disease for radiotherapy(RT). High intra- and inter-user variability makes this a particularlydifficult task for esophageal cancer. This motivates automated solutions, whichis the aim of our work. Because CTV delineation is highly context-dependent--itmust encompass the GTV and regional lymph nodes (LNs) while also avoidingexcessive exposure to the organs at risk (OARs)--we formulate it as a deepcontextual appearance-based problem using encoded spatial contexts of theseanatomical structures. This allows the deep network to better learn from andemulate the margin- and appearance-based delineation performed by humanphysicians. Additionally, we develop domain-specific data augmentation toinject robustness to our system. Finally, we show that a simple 3D progressiveholistically nested network (PHNN), which avoids computationally heavy decodingpaths while still aggregating features at different levels of context, canoutperform more complicated networks. Cross-validated experiments on a datasetof 135 esophageal cancer patients demonstrate that our encoded spatial contextapproach can produce concrete performance improvements, with an average Dicescore of 83.9% and an average surface distance of 4.2 mm, representingimprovements of 3.8% and 2.4 mm, respectively, over the state-of-the-artapproach.

Deep Esophageal Clinical Target Volume Delineation using Encoded 3D Spatial Context of Tumors, Lymph Nodes, and Organs At Risk