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Lymph node station (LNS) delineation from computed tomography (CT) scans isan indispensable step in radiation oncology workflow. High inter-uservariabilities across oncologists and prohibitive laboring costs motivated theautomated approach. Previous works exploit anatomical priors to infer LNS basedon predefined ad-hoc margins. However, without voxel-level supervision, theperformance is severely limited. LNS is highly context-dependent - LNSboundaries are constrained by anatomical organs - we formulate it as a deepspatial and contextual parsing problem via encoded anatomical organs. Thispermits the deep network to better learn from both CT appearance and organcontext. We develop a stratified referencing organ segmentation protocol thatdivides the organs into anchor and non-anchor categories and uses the former'spredictions to guide the later segmentation. We further develop an auto-searchmodule to identify the key organs that opt for the optimal LNS parsingperformance. Extensive four-fold cross-validation experiments on a dataset of98 esophageal cancer patients (with the most comprehensive set of 12 LNSs + 22organs in thoracic region to date) are conducted. Our LNS parsing modelproduces significant performance improvements, with an average Dice score of81.1% +/- 6.1%, which is 5.0% and 19.2% higher over the pure CT-based deepmodel and the previous representative approach, respectively.

DeepStationing: Thoracic Lymph Node Station Parsing in CT Scans using Anatomical Context Encoding and Key Organ Auto-Search