Segmentation of organs‐at‐risks in head and neck CT images using convolutional neural networks
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PURPOSE: Accurate segmentation of organs-at-risks (OARs) is the key step for efficient planning of radiation therapy for head and neck (HaN) cancer treatment. In the work, we proposed the first deep learning-based algorithm, for segmentation of OARs in HaN CT images, and compared its performance against state-of-the-art automated segmentation algorithms, commercial software, and interobserver variability. METHODS: Convolutional neural networks (CNNs)-a concept from the field of deep learning-were used to study consistent intensity patterns of OARs from training CT images and to segment the OAR in a previously unseen test CT image. For CNN training, we extracted a representative number of positive intensity patches around voxels that belong to the OAR of interest in training CT images, and negative intensity patches around voxels that belong to the surrounding structures. These patches then passed through a sequence of CNN layers that captured local image features such as corners, end-points, and edges, and combined them into more complex high-order features that can efficiently describe the OAR. The trained network was applied to classify voxels in a region of interest in the test image where the corresponding OAR is expected to be located. We then smoothed the obtained classification results by using Markov random fields algorithm. We finally extracted the largest connected component of the smoothed voxels classified as the OAR by CNN, performed dilate-erode operations to remove cavities of the component, which resulted in segmentation of the OAR in the test image. RESULTS: The performance of CNNs was validated on segmentation of spinal cord, mandible, parotid glands, submandibular glands, larynx, pharynx, eye globes, optic nerves, and optic chiasm using 50 CT images. The obtained segmentation results varied from 37.4% Dice coefficient (DSC) for chiasm to 89.5% DSC for mandible. We also analyzed the performance of state-of-the-art algorithms and commercial software reported in the literature, and observed that CNNs demonstrate similar or superior performance on segmentation of spinal cord, mandible, parotid glands, larynx, pharynx, eye globes, and optic nerves, but inferior performance on segmentation of submandibular glands and optic chiasm. CONCLUSION: We concluded that convolution neural networks can accurately segment most of OARs using a representative database of 50 HaN CT images. At the same time, inclusion of additional information, for example, MR images, may be beneficial to some OARs with poorly visible boundaries.
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MR image segmentation using deep learning approaches, typically CNNs, are now penetrating the whole field of applications.
“MR image segmentation using deep learning approaches, typically CNNs, are now penetrating the whole field of applications.”
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Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
(2014) · Cited by 33,419 · see citing works
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818 · see citing works
Global cancer statistics, 2012
Torre, L. A., Bray, F., Siegel, R. L., Ferlay, J., Lortet‐Tieulent, J., & Jemal, A. (2015) · CA A Cancer Journal for Clinicians · doi:10.3322/caac.21262 · Cited by 26,858 · see citing works
Radiation Dose–Volume Effects of Optic Nerves and Chiasm
Mayo, C. S., Martel, M. K., Marks, L. B., et al. (2010) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2009.07.1753 · Cited by 518 · see citing works
A benchmark for comparison of dental radiography analysis algorithms
Wang, C., Huang, C.-T., Lee, J., et al. (2016) · Medical Image Analysis · doi:10.1016/j.media.2016.02.004 · Cited by 404 · see citing works
Vision 20/20: Perspectives on automated image segmentation for radiotherapy
Sharp, G. C., Fritscher, K. D., Pekar, V., Peroni, M., Shusharina, N., Veeraraghavan, H., & Yang, J. (2014) · Medical Physics · doi:10.1118/1.4871620 · Cited by 377 · see citing works
An Introduction to Computational Networks and the Computational Network Toolkit
Yu, D., Eversole, A., Seltzer, M., Yao, K., Kuchaiev, O., Zhang, Y., Seide, F. T. B., Huang, Z., Guenter, B. K., Wang, H., et al. (2014) · Cited by 334 · see citing works
Variations in the Contouring of Organs at Risk: Test Case From a Patient With Oropharyngeal Cancer
Nelms, B. E., Tomé, W. A., Robinson, G. J., & Wheeler, J. A. (2010) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2010.10.019 · Cited by 236 · see citing works
Wang, C.-W., Huang, C.-T., Hsieh, M.-C., Li, C.-H., Chang, S.-W., Li, W.-C., Vandaele, R., Maree, R., Jodogne, S., Geurts, P., et al. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2412951 · Cited by 235 · see citing works
Self-citationApproximate Labeling via Graph Cuts Based on Linear Programming
Nikos Komodakis, A., & Tziritas, G. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1061 · Cited by 223 · see citing works
3D Variation in delineation of head and neck organs at risk
Brouwer, C. L., Steenbakkers, R. J. H. M., van den Heuvel, E. R., Duppen, J. C., Navran, A., Bijl, H. P., Chouvalova, O., Burlage, F. R., Meertens, H., Langendijk, J. A., et al. (2012) · Radiation Oncology · doi:10.1186/1748-717x-7-32 · Cited by 212 · see citing works
Atlas-Based Auto-segmentation of Head and Neck CT Images
Han, X., Hoogeman, M. S., Levendag, P. C., Hibbard, L. S., Teguh, D. N., Voet, P. W. J., Cowen, A. C., & Wolf, T. K. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-85990-1_52 · Cited by 200 · see citing works
Teguh, D. N., Levendag, P. C., Voet, P. W. J., Al‐Mamgani, A., Han, X., Wolf, T. K., Hibbard, L. S., Nowak, P. J. C. M., Akhiat, H., Dirkx, M. L. P., et al. (2010) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2010.07.009 · Cited by 193 · see citing works
Geets, X., Daisne, J., Arcangeli, S., et al. (2005) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2005.04.010 · Cited by 183 · see citing works
Zhang, T., Chi, Y., Meldolesi, E., et al. (2007) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2007.01.038 · Cited by 179 · see citing works
Macchia, M. L., Fellin, F., Amichetti, M., et al. (2012) · Radiation Oncology · doi:10.1186/1748-717x-7-160 · Cited by 172 · see citing works
Isambert, A., Dhermain, F. G., Bidault, F., et al. (2008) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2007.11.030 · Cited by 156 · see citing works
Daisne, J., & Blumhofer, A. (2013) · Radiation Oncology · doi:10.1186/1748-717x-8-154 · Cited by 149 · see citing works
Evidence-based organ-sparing radiotherapy in head and neck cancer
Dirix, P. R., & Nuyts, S. (2010) · The Lancet Oncology · doi:10.1016/s1470-2045(09)70231-1 · Cited by 145 · see citing works
Deeley, M. A., Chen, A., Datteri, R. D., Noble, J. H., Cmelak, A. J., Donnelly, E. F., Malcolm, A. W., Moretti, L., Jaboin, J. J., Niermann, K. J., et al. (2011) · Physics in Medicine and Biology · doi:10.1088/0031-9155/56/14/021 · Cited by 144 · see citing works
A multi-center milestone study of clinical vertebral CT segmentation
Yao, J., Burns, J. E., Forsberg, D., et al. (2016) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2015.12.006 · Cited by 137 · see citing works
Evaluation of Automatic Atlas-Based Lymph Node Segmentation for Head-and-Neck Cancer
Stapleford, L. J., Lawson, J. D., Perkins, C., et al. (2010) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2009.09.023 · Cited by 135 · see citing works
Korez, R., Ibragimov, B., Likar, B., Pernuš, F., & Vrtovec, T. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2389334 · Cited by 128 · see citing works
Self-citationFritscher, K. D., Peroni, M., Zaffino, P., et al. (2014) · Medical Physics · doi:10.1118/1.4871623 · Cited by 124 · see citing works
Qazi, A. A., Pekar, V., Kim, J., Xie, J., Breen, S. L., & Jaffray, D. A. (2011) · Medical Physics · doi:10.1118/1.3654160 · Cited by 122 · see citing works
Voet, P. W. J., Dirkx, M. L. P., Teguh, D. N., Hoogeman, M. S., Levendag, P. C., & Heijmen, B. J. M. (2011) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2010.11.017 · Cited by 114 · see citing works
Yang, X., Wu, N., Cheng, G., et al. (2014) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2014.08.350 · Cited by 113 · see citing works
Shape Representation for Efficient Landmark-Based Segmentation in 3-D
Ibragimov, B., Likar, B., Pernuš, F., & Vrtovec, T. (2014) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2013.2296976 · Cited by 109 · see citing works
Self-citationBondiau, P., Malandain, G., Chanalet, S., et al. (2004) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2004.08.055 · Cited by 109 · see citing works
Dosimetric Evaluation of Automatic Segmentation for Adaptive IMRT for Head-and-Neck Cancer
Tsuji, S. Y., Hwang, A., WEINBERG, V. K., et al. (2010) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2009.06.012 · Cited by 108 · see citing works
Tao, C., Yi, J., Chen, N., et al. (2015) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2015.05.012 · Cited by 103 · see citing works
Fortunati, V., Verhaart, R. F., van der Lijn, F., Niessen, W. J., Veenland, J. F., Paulides, M. M., & van Walsum, T. (2013) · Medical Physics · doi:10.1118/1.4810971 · Cited by 102 · see citing works
Atlas-based delineation of lymph node levels in head and neck computed tomography images
Commowick, O., Grégoire, V., & Malandain, G. (2008) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2008.01.018 · Cited by 100 · see citing works
Deep Learning for Medical Image SegmentationPreprint
Lai, M. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1505.02000 · Cited by 96 · see citing works
Emphasizing Conformal Avoidance Versus Target Definition for IMRT Planning in Head-and-Neck Cancer
Harari, P. M., Song, S., & Tomé, W. A. (2010) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2009.09.062 · Cited by 95 · see citing works
A pre-clinical assessment of an atlas-based automatic segmentation tool for the head and neck
Sims, R., Isambert, A., Grégoire, V., et al. (2009) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2009.08.013 · Cited by 92 · see citing works
Mukesh, M., Benson, R. J., Jena, R., et al. (2012) · British Journal of Radiology · doi:10.1259/bjr/66693547 · Cited by 78 · see citing works
Automatic Segmentation and Online virtualCT in Head-and-Neck Adaptive Radiation Therapy
Peroni, M., Ciardo, D., Spadea, M. F., et al. (2012) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2012.04.003 · Cited by 77 · see citing works
Evaluation of an automatic segmentation algorithm for definition of head and neck organs at risk
Thomson, D., Boylan, C., Liptrot, T., et al. (2014) · Radiation Oncology · doi:10.1186/1748-717x-9-173 · Cited by 76 · see citing works
Duc, A. K. H., Eminowicz, G., Mendes, R. L., et al. (2015) · Medical Physics · doi:10.1118/1.4927567 · Cited by 74 · see citing works
An efficient locally affine framework for the smooth registration of anatomical structures
Commowick, O., Arsigny, V., Isambert, A., COSTA, J., Dhermain, F. G., Bidault, F., Bondiau, P., Ayache, N., & Malandain, G. (2008) · Medical Image Analysis · doi:10.1016/j.media.2008.01.002 · Cited by 73 · see citing works
Faggiano, E., Fiorino, C., Scalco, E., Broggi, S., Cattaneo, M., Maggiulli, E., Dell’Oca, I., Muzio, N. G. D., Calandrino, R., & Rizzo, G. (2011) · Physics in Medicine and Biology · doi:10.1088/0031-9155/56/3/015 · Cited by 63 · see citing works
A Game-Theoretic Framework for Landmark-Based Image Segmentation
Ibragimov, B., Likar, B., Pernuš, F., & Vrtovec, T. (2012) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2012.2202915 · Cited by 61 · see citing works
Self-citationMattiucci, G. C., Boldrini, L., Chiloiro, G., et al. (2013) · Acta Oncologica · doi:10.3109/0284186x.2013.813069 · Cited by 59 · see citing works
Gorthi, S., Duay, V., Houhou, N., Cuadra, M. B., Schick, U., Becker, M., Allal, A. S., & Thiran, J. (2009) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2008.2011104 · Cited by 57 · see citing works
Lymph node segmentation from CT images using fast marching method
Yan, J., Zhuang, T.-G., Zhao, B., & Schwartz, L. H. (2003) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2003.09.003 · Cited by 54 · see citing works
Segmentation of Neck Lymph Nodes in CT Datasets with Stable 3D Mass-Spring Models
Dornheim, J., Seim, H., Preim, B., Hertel, I., & Strauß, G. (2007) · Academic Radiology · doi:10.1016/j.acra.2007.09.001 · Cited by 51 · see citing works
Verhaart, R. F., Fortunati, V., Verduijn, G. M., van Walsum, T., Veenland, J. F., & Paulides, M. M. (2014) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2014.01.027 · Cited by 47 · see citing works
Chen, A., Deeley, M. A., Niermann, K. J., et al. (2010) · Medical Physics · doi:10.1118/1.3515459 · Cited by 45 · see citing works
Geometrical model-based segmentation of the organs of sight on CT images
Bekes, G., Máté, E., Nyúl, L. G., et al. (2008) · Medical Physics · doi:10.1118/1.2826557 · Cited by 44 · see citing works
Image registration with auto‐mapped control volumes
Schreibmann, E., & Xing, L. (2006) · Medical Physics · doi:10.1118/1.2184440 · Cited by 43 · see citing works
Self-citationSegmentation of tongue muscles from super-resolution magnetic resonance images
Ibragimov, B., Prince, J. L., Murano, E. Z., et al. (2014) · Medical Image Analysis · doi:10.1016/j.media.2014.11.006 · Cited by 41 · see citing works
Self-citationContour-Driven Atlas-Based Segmentation
Wachinger, C., Fritscher, K. D., Sharp, G. C., et al. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2442753 · Cited by 41 · see citing works
Dentalmaps: Automatic Dental Delineation for Radiotherapy Planning in Head-and-Neck Cancer
Thariat, J., Ramus, L., Maingon, P., et al. (2011) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2011.03.035 · Cited by 39 · see citing works
Chen, A., Niermann, K. J., Deeley, M. A., et al. (2011) · Physics in Medicine and Biology · doi:10.1088/0031-9155/57/1/93 · Cited by 38 · see citing works
Image‐based modeling of tumor shrinkage in head and neck radiation therapya)
Chao, M., Xie, Y., Moros, E. G., et al. (2010) · Medical Physics · doi:10.1118/1.3399872 · Cited by 32 · see citing works
Automatic tissue segmentation of head and neck MR images for hyperthermia treatment planning
Fortunati, V., Verhaart, R. F., Niessen, W. J., et al. (2015) · Physics in Medicine and Biology · doi:10.1088/0031-9155/60/16/6547 · Cited by 27 · see citing works
Verhaart, R. F., Fortunati, V., Verduijn, G. M., et al. (2014) · Medical Physics · doi:10.1118/1.4901270 · Cited by 26 · see citing works
Head and neck lymph node region delineation with image registration
Teng, C.-C., Shapiro, L. G., & Kalet, I. J. (2010) · BioMedical Engineering OnLine · doi:10.1186/1475-925x-9-30 · Cited by 21 · see citing works
Head and Neck Auto-segmentation Challenge
Pekar, V., Allaire, S., Kim, J., et al. (2009) · doi:10.54294/263mqy · Cited by 19 · see citing works
Leavens, C., Vik, T., Schulz, H., et al. (2008) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.769710 · Cited by 13 · see citing works
Automated measurement of anterior and posterior acetabular sector angles
Ibragimov, B., Likar, B., Pernuš, F., et al. (2012) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.910873 · Cited by 5 · see citing works
Self-citationIntroduction to the Issue on Digital Image Processing Techniques for Oncology
Tang, J., Rangayyan, R. M., Yao, J., et al. (2009) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2009.2013533 · Cited by 4 · see citing works
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- Authors
- Ibragimov, B., & Xing, L.
- Affiliations
- Stanford Medicine (US)
- Corresponding author
- Ibragimov, Bulat
- Venue
- Medical Physics
- Publisher
- Wiley
- Published
- 2017-02
- Volume
- 44
- Issue
- 2
- Pages
- 547-557
- ISSN
- 0094-2405 (ISSN-L)
- 2473-4209
- PMC
- PMC5383420
- OpenAlex
- W2560725027
- Cited by
- 502
- References
- 64
- Citations by year
- 2026: 21 · 2025: 28 · 2024: 34 · 2023: 50 · 2022: 58 · 2021: 100 · 2020: 84 · 2019: 76 · 2018: 47 · 2017: 4
- FWCI
- 28.73
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Medical Physics (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (1R01CA176553, R01E0116777)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
- Physical Sciences › Physics and Astronomy › Radiation › Advanced Radiotherapy Techniques
- Keywords
- organ-at-risk segmentation, head and neck cancer, radiotherapy treatment planning, CT images, convolutional neural networks, spinal cord, mandible, parotid gland, submandibular gland, larynx, pharynx, optic chiasm, optic nerve, eye globe, Markov random fields, interobserver variability, deep learning segmentation, connected component analysis
- MeSH terms
- Head and Neck Neoplasms, Humans, Image Processing, Computer-Assisted, Markov Chains, Radiotherapy Planning, Computer-Assisted, Software, Tomography, X-Ray Computed, Observer Variation, Neural Networks, Computer, Radiotherapy, Intensity-Modulated, Organs at Risk
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