  Clinical target volume (CTV) delineation from radiotherapy computed
tomography (RTCT) images is used to define the treatment areas containing the
gross tumor volume (GTV) and/or sub-clinical malignant disease for radiotherapy
(RT). High intra- and inter-user variability makes this a particularly
difficult task for esophageal cancer. This motivates automated solutions, which
is the aim of our work. Because CTV delineation is highly context-dependent--it
must encompass the GTV and regional lymph nodes (LNs) while also avoiding
excessive exposure to the organs at risk (OARs)--we formulate it as a deep
contextual appearance-based problem using encoded spatial contexts of these
anatomical structures. This allows the deep network to better learn from and
emulate the margin- and appearance-based delineation performed by human
physicians. Additionally, we develop domain-specific data augmentation to
inject robustness to our system. Finally, we show that a simple 3D progressive
holistically nested network (PHNN), which avoids computationally heavy decoding
paths while still aggregating features at different levels of context, can
outperform more complicated networks. Cross-validated experiments on a dataset
of 135 esophageal cancer patients demonstrate that our encoded spatial context
approach can produce concrete performance improvements, with an average Dice
score of 83.9% and an average surface distance of 4.2 mm, representing
improvements of 3.8% and 2.4 mm, respectively, over the state-of-the-art
approach.
