ArticleInternational journal of computer assisted radiology and surgery2024
Segmentation of mediastinal lymph nodes in CT with anatomical priors.
Article in International journal of computer assisted radiology and surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Analysis of deep learning-based segmentation of lymph nodes on full-dose and reduced-dose body CT.Abdominal radiology (New York) · 2026Article
- Deep Learning-Based Detection of Malignant and Equivocal Cervical Lymph Nodes on CT Imaging Using a 2D U-Net++ Model.Maedica · 2026Article
- Progress in fully automated abdominal CT interpretation-an update over the past decade.Abdominal radiology (New York) · 2026Review
- Annotation-efficient deep learning detection and measurement of mediastinal lymph nodes in CT.International journal of computer assisted radiology and surgery · 2026Article
- Deep learning for malignant lymph node segmentation and detection: a review.Frontiers in immunology · 2025Review
- Subcutaneous edema segmentation on abdominal CT using multi-class labels and iterative annotation.International journal of computer assisted radiology and surgery · 2025Article
- Weakly supervised detection of pheochromocytomas and paragangliomas in CT using noisy data.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2024Article
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Authors and funding
3 authors.
Funding
Abstract
purposeLymph 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.
methodsWe 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.
resultsFor LNs with short axis diameter
conclusionTo 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.
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