Automatic Detection and Segmentation of Lymph Nodes From CT Data
Abstract
Lymph nodes are assessed routinely in clinical practice and their size is followed throughout radiation or chemotherapy to monitor the effectiveness of cancer treatment. This paper presents a robust learning-based method for automatic detection and segmentation of solid lymph nodes from CT data, with the following contributions. First, it presents a learning based approach to solid lymph node detection that relies on marginal space learning to achieve great speedup with virtually no loss in accuracy. Second, it presents a computationally efficient segmentation method for solid lymph nodes (LN). Third, it introduces two new sets of features that are effective for LN detection, one that self-aligns to high gradients and another set obtained from the segmentation result. The method is evaluated for axillary LN detection on 131 volumes containing 371 LN, yielding a 83.0% detection rate with 1.0 false positive per volume. It is further evaluated for pelvic and abdominal LN detection on 54 volumes containing 569 LN, yielding a 80.0% detection rate with 3.2 false positives per volume. The running time is 5-20 s per volume for axillary areas and 15-40 s for pelvic. An added benefit of the method is the capability to detect and segment conglomerated lymph nodes.
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Shin, H.-C., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2528162 · Cited by 5,759 · see citing works
“Previous studies have analyzed three-dimensional patch creation for LN detection [23], [24], atlas creation from chest CT [25] and the extraction of multi-level image features [26], [27].”
BackgroundImproving Computer-Aided Detection UsingConvolutional Neural Networks and Random View Aggregation
Roth, H. R., Lu, L., Liu, J., Yao, J., Seff, A., Cherry, K., Kim, L., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2482920 · Cited by 608 · see citing works
“We use a soft-tissue window level of [-100, 200 HU] as in [44].”
MethodLeveraging Mid-Level Semantic Boundary Cues for Automated Lymph Node Detection
Seff, A., Lu, L., Barbu, A., Roth, H., Shin, H.-C., & Summers, R. M. (2015) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-24571-3_7 · Cited by 36 · see citing works
Self-citationChao, C.-H., Zhu, Z., Guo, D., Yan, K., Ho, T.-Y., Cai, J., Harrison, A. P., Ye, X., Xiao, J., Yuille, A., et al. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-59728-3_75 · Cited by 29 · see citing works
Median Filter Helps Lymph Node Segmentation in Deep Learning via PET/CT
Zhang, X., Liao, W., & Xu, G. (2021) · 2021 4th International Conference on Digital Medicine and Image Processing · doi:10.1145/3506651.3506662 · Cited by 1 · see citing works
- Authors
- Barbu, A., Suehling, M., Xu, X., Liu, D., Zhou, S. K., & Comaniciu, D.
- Affiliations
- Florida State University (US)
- Siemens (United States) (US)
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2012-02
- Volume
- 31
- Issue
- 2
- Pages
- 240-250
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W2008120479
- Cited by
- 94
- References
- 23
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 5 · 2023: 5 · 2022: 11 · 2021: 11 · 2020: 15 · 2019: 2 · 2018: 5 · 2017: 5 · 2016: 9 · 2015: 6 · 2014: 5 · 2013: 7 · 2012: 2
- FWCI
- 5.16
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Medical Imaging (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- lymph node detection, lymph node segmentation, computed tomography, learning-based detection, axillary lymph nodes, pelvic lymph nodes, abdominal lymph nodes, cancer treatment monitoring
- MeSH terms
- Algorithms, Humans, Lymph Nodes, Lymphoma, Pattern Recognition, Automated, Radiographic Image Enhancement, Radiographic Image Interpretation, Computer-Assisted, Sensitivity and Specificity, Tomography, X-Ray Computed, Reproducibility of Results
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