# Segmentation of Mediastinal Lymph Nodes in CT with Anatomical Priors

arXiv: https://arxiv.org/abs/2401.06272 · HTML: https://mlsanity.com/paper/2401.06272/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 32 references that are in the map.
- First posted on arXiv 11 Jan 2024.

## Builds on (most cited first, 8 of 8)

- [The Medical Segmentation Decathlon](https://mlsanity.com/paper/2106.05735/) (2021)
- [TotalSegmentator: robust segmentation of 104 anatomical structures in CT images](https://mlsanity.com/paper/2208.05868/) (2022)
- [nnFormer: Interleaved Transformer for Volumetric Segmentation](https://mlsanity.com/paper/2109.03201/) (2021)
- [A New 2.5D Representation for Lymph Node Detection using Random Sets of Deep Convolutional Neural Network Observations](https://mlsanity.com/paper/1406.2639/) (2014)
- [Mediastinal lymph nodes segmentation using 3D convolutional neural network ensembles and anatomical priors guiding](https://mlsanity.com/paper/2102.06515/) (2021)
- [Lymph Node Detection in T2 MRI with Transformers](https://mlsanity.com/paper/2111.04885/) (2021)
- [Universal Lymph Node Detection in T2 MRI using Neural Networks](https://mlsanity.com/paper/2204.00622/) (2022)
- [Enhanced Muscle and Fat Segmentation for CT-Based Body Composition Analysis: A Comparative Study](https://mlsanity.com/paper/2401.05294/) (2024)

## Built on by (most cited first, 3 of 3)

- [Leveraging Anatomical Priors for Automated Pancreas Segmentation on Abdominal CT](https://mlsanity.com/paper/2504.06921/) (2025)
- [Longitudinal Assessment of Lung Lesion Burden in CT](https://mlsanity.com/paper/2504.06924/) (2025)
- [A Study of Anatomical Priors for Deep Learning-Based Segmentation of Pheochromocytoma in Abdominal CT](https://mlsanity.com/paper/2507.15193/) (2025)

## Abstract (arXiv)

Purpose: Lymph nodes (LNs) in the chest have a tendency to enlarge due to various pathologies, such as lung cancer or pneumonia. Clinicians routinely measure nodal size to monitor disease progression, confirm metastatic cancer, and assess treatment response. However, variations in their shapes and appearances make it cumbersome to identify LNs, which reside outside of most organs. Methods: We propose to segment LNs in the mediastinum by leveraging the anatomical priors of 28 different structures (e.g., lung, trachea etc.) generated by the public TotalSegmentator tool. The CT volumes from 89 patients available in the public NIH CT Lymph Node dataset were used to train three 3D nnUNet models to segment LNs. The public St. Olavs dataset containing 15 patients (out-of-training-distribution) was used to evaluate the segmentation performance. Results: For the 15 test patients, the 3D cascade nnUNet model obtained the highest Dice score of 72.2 +- 22.3 for mediastinal LNs with short axis diameter ≥ 8mm and 54.8 +- 23.8 for all LNs respectively. These results represent an improvement of 10 points over a current approach that was evaluated on the same test dataset. Conclusion: To our knowledge, we are the first to harness 28 distinct anatomical priors to segment mediastinal LNs, and our work can be extended to other nodal zones in the body. The proposed method has immense potential for improved patient outcomes through the identification of enlarged nodes in initial staging CT scans.