Segmentation of Mediastinal Lymph Nodes in CT with Anatomical Priors
Tejas Sudharshan Mathai Bohan Liu Ronald M. Summers
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.