Segmentation of mediastinal lymph nodes in CT with anatomical priors
Document Type
Journal Article
Publication Date
5-13-2024
Journal
International journal of computer assisted radiology and surgery
DOI
10.1007/s11548-024-03165-4
Keywords
Anatomical priors; CT; Deep learning; Lymph node; Mediastinum; Segmentation
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 off-the-shelf 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 LNs with short axis diameter 8 mm, the 3D cascade nnUNet model obtained the highest Dice score of 67.9 ± 23.4 and lowest Hausdorff distance error of 22.8 ± 20.2. For LNs of all sizes, the Dice score was 58.7 ± 21.3 and this represented a 10% improvement over a recently published approach 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 the potential for improved patient outcomes through the identification of enlarged nodes in initial staging CT scans.
APA Citation
Mathai, Tejas Sudharshan; Liu, Bohan; and Summers, Ronald M., "Segmentation of mediastinal lymph nodes in CT with anatomical priors" (2024). GW Authored Works. Paper 4938.
https://hsrc.himmelfarb.gwu.edu/gwhpubs/4938
Department
School of Medicine and Health Sciences Resident Works