  Lymph node station (LNS) delineation from computed tomography (CT) scans is
an indispensable step in radiation oncology workflow. High inter-user
variabilities across oncologists and prohibitive laboring costs motivated the
automated approach. Previous works exploit anatomical priors to infer LNS based
on predefined ad-hoc margins. However, without voxel-level supervision, the
performance is severely limited. LNS is highly context-dependent - LNS
boundaries are constrained by anatomical organs - we formulate it as a deep
spatial and contextual parsing problem via encoded anatomical organs. This
permits the deep network to better learn from both CT appearance and organ
context. We develop a stratified referencing organ segmentation protocol that
divides the organs into anchor and non-anchor categories and uses the former's
predictions to guide the later segmentation. We further develop an auto-search
module to identify the key organs that opt for the optimal LNS parsing
performance. Extensive four-fold cross-validation experiments on a dataset of
98 esophageal cancer patients (with the most comprehensive set of 12 LNSs + 22
organs in thoracic region to date) are conducted. Our LNS parsing model
produces significant performance improvements, with an average Dice score of
81.1% +/- 6.1%, which is 5.0% and 19.2% higher over the pure CT-based deep
model and the previous representative approach, respectively.
