Single-click, semi-automatic lung nodule contouring using hierarchical conditional random fields
Abstract
Lung cancer is the second most common cancer in the United States, regardless of gender. Lung cancer staging is a critical process for diagnosis and prognosis that is commonly done through the analysis of computed tomography of the chest. Analysis can be done by extracting quantitative metrics from clinician defined contours; however, defining contours manually can be a time consuming process which, in an environment where fast staging is necessary, is undesirable. Semi-automatic methods are desirable for minimizing user input while achieving similar contouring results. However, they can be hampered by image noise and complex shapes. In this paper, we present a single-click, semi-automatic contouring method for lung nodules that formulates the problem as a texture-based hierarchical conditional random field, making it robust to noise and capable of contouring complex shapes. Comparing against other semi-automatic contouring methods using clinician contours as ground truth, the proposed method achieves higher results in the sensitivity, Dice, and Jaccard metrics while achieving comparable results with the specificity and accuracy metrics.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Cites
Stochastic relaxation, gibbs distributions, and the bayesian restoration of images.
Geman, S., & Geman, D. (1984) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1984.4767596 · Cited by 17,949 · see citing works
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
Lafferty, J., McCallum, A., & Pereira, F. C. N. (2001) · ScholarlyCommons (University of Pennsylvania) · Cited by 12,953 · see citing works
Siegel, R., Ma, J., Zou, Z., & Jemal, A. (2014) · CA: A Cancer Journal for Clinicians · doi:10.3322/caac.21208 · Cited by 11,499 · see citing works
Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening
The National Lung Screening Trial Research Team (2011) · New England Journal of Medicine · doi:10.1056/nejmoa1102873 · Cited by 11,064 · see citing works
Edge, S. B., & Compton, C. C. (2010) · Annals of Surgical Oncology · doi:10.1245/s10434-010-0985-4 · Cited by 9,055 · see citing works
Fast approximate energy minimization via graph cuts
Boykov, Y., Veksler, O., & Zabih, R. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.969114 · Cited by 6,988 · see citing works
Haralock, R. M., & Shapiro, L. G. (1991) · Addison-Wesley Longman Publishing Co., Inc. eBooks · Cited by 3,942 · see citing works
Segmentation of medical images using adaptive region growing
Pohle-Fröhlich, R., & Toennies, K. D. (2001) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.431013 · Cited by 314 · see citing works
MDCT-based 3-D texture classification of emphysema and early smoking related lung pathologies
Xu, Y., Sonka, M., McLennan, G., et al. (2006) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2006.870889 · Cited by 235 · see citing works
Aromatase Expression Predicts Survival in Women with Early-Stage Non–Small Cell Lung Cancer
Mah, V. H., Seligson, D. B., Ai, L., Márquez, D. C., Wistuba, I. I., Elshimali, Y., Fishbein, M. C., Chia, D., Pietras, R. J., & Goodglick, L. (2007) · Cancer Research · doi:10.1158/0008-5472.can-07-2607 · Cited by 136 · see citing works
Ng, Q., Goh, V., Fichte, H., Klotz, E., Fernie, P., Saunders, M. I., Hoskin, P., & Padhani, A. R. (2006) · Radiology · doi:10.1148/radiol.2392050568 · Cited by 130 · see citing works
Interactive segmentation based on the live wire for 3D CT chest image analysis
Lu, K., & Higgins, W. E. (2007) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-007-0129-x · Cited by 41 · see citing works
Improved interactive medical image segmentation using Enhanced Intelligent Scissors (EIS)
Mishra, A. K., Wong, A. K. C., Zhang, W., Clausi, D. A., & Fieguth, P. (2008) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2008.4649855 · Cited by 30 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation
Wang, G., Zuluaga, M. A., Li, W., Pratt, R., Patel, P. A., Aertsen, M., Doel, T., David, A. L., Deprest, J., Ourselin, S., et al. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2840695 · Cited by 502 · see citing works
- Authors
- Haider, S. A., Shafiee, M. J., Chung, A., Khalvati, F., Oikonomou, A., Wong, A., & Haider, M. A.
- Affiliations
- University of Waterloo (CA)
- Sunnybrook Health Science Centre (CA)
- Venue
- 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI)
- Publisher
- IEEE
- Published
- 2015-04
- Volume
- 25
- Pages
- 1139-1142
- OpenAlex
- W1572251380
- Cited by
- 6
- References
- 13
- Citations by year
- 2021: 1 · 2020: 1 · 2019: 2 · 2018: 2
- FWCI
- 0.27
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Symposium on Biomedical Imaging (ISBI) (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 › Pulmonary and Respiratory Medicine › Lung Cancer Diagnosis and Treatment
- Keywords
- semi-automatic segmentation, texture segmentation, lung cancer staging, computed tomography, image noise robustness
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
