Segmentation of Mediastinal Lymph Nodes in CT with Anatomical Priors

TL;DR

This paper introduces a novel method for segmenting mediastinal lymph nodes in CT scans by utilizing anatomical priors from 28 structures, achieving improved accuracy over previous approaches and aiding clinical assessment.

Contribution

The study is the first to leverage 28 anatomical priors for mediastinal lymph node segmentation, enhancing accuracy and potential clinical utility.

Findings

Achieved Dice score of 72.2 for enlarged LNs

Improved segmentation accuracy by 10 points over previous methods

Demonstrated potential for better disease staging and monitoring

Abstract

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 $\geq$ 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.