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

arXiv: https://arxiv.org/abs/2109.09271 · HTML: https://mlsanity.com/paper/2109.09271/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 23 references that are in the map.
- First posted on arXiv 20 Sep 2021.

## Builds on (most cited first, 7 of 7)

- [DARTS: Differentiable Architecture Search](https://mlsanity.com/paper/1806.09055/) (2018)
- [Organ at Risk Segmentation for Head and Neck Cancer using Stratified Learning and Neural Architecture Search](https://mlsanity.com/paper/2004.08426/) (2020)
- [Lymph Node Gross Tumor Volume Detection and Segmentation via Distance-based Gating using 3D CT/PET Imaging in Radiotherapy](https://mlsanity.com/paper/2008.11870/) (2020)
- [Lymph Node Gross Tumor Volume Detection in Oncology Imaging via Relationship Learning Using Graph Neural Network](https://mlsanity.com/paper/2008.13013/) (2020)
- [Robust Pancreatic Ductal Adenocarcinoma Segmentation with Multi-Institutional Multi-Phase Partially-Annotated CT Scans](https://mlsanity.com/paper/2008.10652/) (2020)
- [Accurate Esophageal Gross Tumor Volume Segmentation in PET/CT using Two-Stream Chained 3D Deep Network Fusion](https://mlsanity.com/paper/1909.01524/) (2019)
- [Deep Esophageal Clinical Target Volume Delineation using Encoded 3D Spatial Context of Tumors, Lymph Nodes, and Organs At Risk](https://mlsanity.com/paper/1909.01526/) (2019)

## Built on by (most cited first, 9 of 9)

- [SAME: Deformable Image Registration based on Self-supervised Anatomical Embeddings](https://mlsanity.com/paper/2109.11572/) (2021)
- [Continual Segment: Towards a Single, Unified and Accessible Continual Segmentation Model of 143 Whole-body Organs in CT Scans](https://mlsanity.com/paper/2302.00162/) (2023)
- [Towards a Comprehensive, Efficient and Promptable Anatomic Structure Segmentation Model using 3D Whole-body CT Scans](https://mlsanity.com/paper/2403.15063/) (2024)
- [SAME++: A Self-supervised Anatomical eMbeddings Enhanced medical image registration framework using stable sampling and regularized transformation](https://mlsanity.com/paper/2311.14986/) (2023)
- [Anatomy-Aware Lymph Node Detection in Chest CT using Implicit Station Stratification](https://mlsanity.com/paper/2307.15271/) (2023)
- [Multi-institutional Validation of Two-Streamed Deep Learning Method for Automated Delineation of Esophageal Gross Tumor Volume using planning-CT and FDG-PETCT](https://mlsanity.com/paper/2110.05280/) (2021)
- [A deep local attention network for pre-operative lymph node metastasis prediction in pancreatic cancer via multiphase CT imaging](https://mlsanity.com/paper/2301.01448/) (2023)
- [Integrating features from lymph node stations for metastatic lymph node detection](https://mlsanity.com/paper/2301.03202/) (2023)
- [From Slices to Sequences: Autoregressive Tracking Transformer for Cohesive and Consistent 3D Lymph Node Detection in CT Scans](https://mlsanity.com/paper/2503.07933/) (2025)

## Abstract (arXiv)

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.