Segmentation of mediastinal lymph nodes in CT with anatomical priors

• Published in: International journal for computer assisted radiology and surgery Vol. 19; no. 8; pp. 1537 - 1544
• Main Authors: Mathai, Tejas Sudharshan, Liu, Bohan, Summers, Ronald M.
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 2024; Springer Nature B.V
• Subjects: Computed tomography; Computer Imaging; Computer Science; Datasets; Health Informatics; Imaging; Knowledge representation; Lymphatic system; Mediastinum; Medicine; Medicine & Public Health; Metric space; Original; Original Article; Pattern Recognition and Graphics; Performance evaluation; Radiology; Segmentation; Segments; Surgery; Trachea; Vision; Deep learning; CT; Lymph node; Segmentation; Mediastinum; Anatomical priors
• ISSN: 1861-6429; 1861-6410; 1861-6429
• DOI: 10.1007/s11548-024-03165-4

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.