Mediastinal atlas creation from 3-D chest computed tomography images: Application to automated detection and station mapping of lymph nodes
Abstract
One important aspect of lung cancer staging is the assessment of mediastinal lymph nodes in 3-D chest computed tomography (CT) images. In the current clinical routine this is done manually by analyzing the 3-D CT image slice by slice to find nodes, evaluate them quantitatively, and assign labels to them for describing the clinical and pathologic extent of metastases. In this paper we present a method to automate the process of lymph node detection and labeling by creation of a mediastinal average image and a novel lymph node atlas containing probability maps for mediastinal, aortic, and N1 nodes. Utilizing a fast deformable registration approach to match the atlas with CT images of new patients, our method can maintain an acceptable runtime. In comparison to previously published methods for mediastinal lymph node detection and labeling it also shows a good sensitivity and positive predictive value.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Cites
New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1)
Eisenhauer, E. A., Therasse, P., Bogaerts, J., Schwartz, L. H., Sargent, D., Ford, R., Dancey, J., Arbuck, S., Gwyther, S., Mooney, M., et al. (2009) · European Journal of Cancer · doi:10.1016/j.ejca.2008.10.026 · Cited by 29,534 · see citing works
New Guidelines to Evaluate the Response to Treatment in Solid Tumors
Therasse, P., Arbuck, S. G., Eisenhauer, E. A., Wanders, J., Kaplan, R. S., Rubinstein, L., Verweij, J., Van Glabbeke, M., van Oosterom, A. T., Christian, M. C., et al. (2000) · JNCI: Journal of the National Cancer Institute · doi:10.1093/jnci/92.3.205 · Cited by 15,660 · see citing works
Nonrigid registration using free-form deformations: application to breast mr images
Rueckert, D., Sonoda, L. I., Hayes, C., Hill, D. L. G., Leach, M. O., & Hawkes, D. J. (1999) · IEEE Transactions on Medical Imaging · doi:10.1109/42.796284 · Cited by 5,265 · see citing works
Multimodality image registration by maximization of mutual information
Maes, F., Collignon, A., Vandermeulen, D., Marchal, G., & Suetens, P. (1997) · IEEE Transactions on Medical Imaging · doi:10.1109/42.563664 · Cited by 4,547 · see citing works
Medical image analysis: progress over two decades and the challenges ahead
Duncan, J. S., & Ayache, N. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.824822 · Cited by 4,167 · see citing works
Multiscale vessel enhancement filtering
Frangi, A. F., Niessen, W. J., Vincken, K. L., & Viergever, M. A. (1998) · Lecture notes in computer science · doi:10.1007/bfb0056195 · Cited by 3,916 · see citing works
Mutual-information-based registration of medical images: a survey
Pluim, J. P. W., Maintz, J. B. A., & Viergever, M. A. (2003) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2003.815867 · Cited by 2,882 · see citing works
Alignment by Maximization of Mutual Information
Viola, P. A., & Wells, W. M. (1997) · International Journal of Computer Vision · doi:10.1023/a:1007958904918 · Cited by 2,610 · see citing works
Medical Image Computing and Computer-Assisted Intervention
Yang, G., Hawkes, D. J., Rueckert, D., Noble, J. A., & Taylor, C. (2009) · Research Explorer (The University of Manchester) · Cited by 1,591 · see citing works
Medical Image Computing and Computer-Assisted Intervention — MICCAI’98
Roche, A., Malandain, G., Pennec, X., & Ayache, N. (1998) · Lecture notes in computer science · doi:10.1007/bfb0056181 · Cited by 1,578 · see citing works
Rusch, V. W., Asamura, H., Watanabe, H., Giroux, D. J., Rami–Porta, R., & Goldstraw, P. G. (2009) · Journal of Thoracic Oncology · doi:10.1097/jto.0b013e3181a0d82e · Cited by 1,207 · see citing works
Unbiased diffeomorphic atlas construction for computational anatomy
Joshi, S. C., Davis, B. C., Jomier, M., & Gerig, G. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.07.068 · Cited by 850 · see citing works
Computer analysis of computed tomography scans of the lung: a survey
Sluimer, I. C., Schilham, A. M. R., Prokop, M., & van Ginneken, B. (2006) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2005.862753 · Cited by 559 · see citing works
Average Brain Models: A Convergence Study
Guimond, A., Meunier, J., & Thirion, J. (2000) · Computer Vision and Image Understanding · doi:10.1006/cviu.1999.0815 · Cited by 431 · see citing works
Dense image registration through MRFs and efficient linear programming
Glocker, B., Nikos Komodakis, A., Tziritas, G., Navab, N., & Nikos Paragios, A. (2008) · Medical Image Analysis · doi:10.1016/j.media.2008.03.006 · Cited by 428 · see citing works
Self-citationRueckert, D., Frangi, A. F., & Schnabel, J. A. (2003) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2003.815865 · Cited by 414 · see citing works
Invasive Staging of Non-small Cell Lung Cancer*
Toloza, E. M., Harpole, L. H., Detterbeck, F. C., & McCrory, D. C. (2003) · CHEST Journal · doi:10.1378/chest.123.1_suppl.157s · Cited by 397 · see citing works
Tissue classification based on 3D local intensity structures for volume rendering
Sato, Y., Westin, C., Bhalerao, A. H., Nakajima, S.-I., Shiraga, N., Tamura, S., & Kikinis, R. (2000) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/2945.856997 · Cited by 339 · see citing works
Extraction of Airways From CT (EXACT'09)
Lo, P., van Ginneken, B., Reinhardt, J. M., Yavarna, T., de Jong, P. A., Irving, B., Fetita, C. I., Ortner, M., Pinho, R., Sijbers, J., et al. (2012) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2012.2209674 · Cited by 293 · see citing works
Self-citationBrain Functional Localization: A Survey of Image Registration Techniques
Gholipour, A., Kehtarnavaz, N., Briggs, R. W., Devous, M. D., & Gopinath, K. (2007) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2007.892508 · Cited by 270 · 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
RECIST revised: implications for the radiologist. A review article on the modified RECIST guideline
van Meerten, E. L. V. P., Gelderblom, H. A. J., & Bloem, J. L. (2009) · European Radiology · doi:10.1007/s00330-009-1685-y · Cited by 191 · see citing works
Quo Vadis, Atlas-Based Segmentation?
Torsten Rohlfing, B., Brandt, R., Menzel, R., Russakoff, D. B., & Maurer, C. R. (2005) · Springer eBooks · doi:10.1007/0-306-48608-3_11 · Cited by 188 · see citing works
Lymph Node Size and Metastatic Infiltration in Non-small Cell Lung Cancer*
Prenzel, K., ̈nig, S. P. M., Sinning, J. M., Baldus, S. E., Brochhagen, H.-G., Schneider, P. M., & lscher, A. H. H. (2003) · CHEST Journal · doi:10.1378/chest.123.2.463 · Cited by 170 · see citing works
CT-based definition of thoracic lymph node stations: An atlas from the University of Michigan
Chapet, O., Kong, F., Quint, L. E., Chang, A. C., Haken, R. K. T., Eisbruch, A., & Hayman, J. A. (2005) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2004.12.060 · Cited by 142 · see citing works
Establishing a Normative Atlas of the Human Lung
Li, B., Christensen, G. E., Hoffman, E. A., McLennan, G., & Reinhardt, J. M. (2003) · Academic Radiology · doi:10.1016/s1076-6332(03)80099-5 · Cited by 137 · see citing works
Martínez‐Monge, R., Fernandes, P. S., Gupta, N., & Gahbauer, R. A. (1999) · Radiology · doi:10.1148/radiology.211.3.r99jn40815 · Cited by 130 · see citing works
Is it Possible to Differentiate Malignant Mediastinal Nodes from Benign Nodes by Size?
Arita, T., Matsumoto, T., Kuramitsu, T., Kawamura, M., Matsunaga, N., Sugi, K., & Esato, K. (1996) · CHEST Journal · doi:10.1378/chest.110.4.1004 · Cited by 109 · see citing works
Sihoe, A. D. L., & Yim, A. P. C. (2004) · Journal of Surgical Research · doi:10.1016/j.jss.2003.11.006 · Cited by 107 · see citing works
An Atlas of the Pelvic Lymph Node Regions to Aid Radiotherapy Target Volume Definition
Taylor, A., Rockall, A., & Powell, M. E. B. (2007) · Clinical Oncology · doi:10.1016/j.clon.2007.05.002 · Cited by 107 · see citing works
Correlation methods for the centering, rotation, and alignment of functional brain images.
Junck, L., Moen, J. G., Hutchins, G. D., Brown, M. B., & Kuhl, D. E. (1990) · PubMed · Cited by 105 · 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
Handbook of Biomedical Image Analysis
Suri, J. S., Wilson, D. L., & Laxminarayan, S. (2005) · Springer eBooks · doi:10.1007/b104806 · Cited by 93 · see citing works
Marker‐controlled watershed for lymphoma segmentation in sequential CT images
Yan, J., Zhao, B., Wang, L., Zelenetz, A. D., & Schwartz, L. H. (2006) · Medical Physics · doi:10.1118/1.2207133 · Cited by 70 · see citing works
Patz, E. F., Erasmus, J. J., McAdams, H. P., Connolly, J. E., Marom, E. M., Goodman, P. C., Leder, R. A., Keogan, M. T., & Herndon, J. E. (1999) · Radiology · doi:10.1148/radiology.212.1.r99jl1956 · Cited by 66 · 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
Three‐dimensional atlas of lymph node topography based on the visible human data set
Qatarneh, S. M., Kiricuţă, I., Brahme, A., Tiede, U., & Lind, B. K. (2006) · The Anatomical Record Part B The New Anatomist · doi:10.1002/ar.b.20102 · Cited by 49 · see citing works
Kitasaka, T., Tsujimura, Y., Nakamura, Y., Mori, K., Suenaga, Y., Ito, M., & Nawano, S. (2007) · Springer eBooks · doi:10.1007/978-3-540-75759-7_41 · Cited by 46 · see citing works
Self-citationGeneralizing vesselness with respect to dimensionality and shape
Antiga, L. (2007) · The Insight Journal · doi:10.54294/urgadx · Cited by 45 · see citing works
Medical Imaging 2007: Computer-aided Diagnosis
Giger, M. L., & Karssemeijer, N. (2007) · Cited by 44 · see citing works
A population-based atlas and clinical target volume for the head-and-neck lymph nodes
Poon, I., Fischbein, N. J., Lee, N. Y., Akazawa, P. F., Xia, P., Quivey, J. M., & Phillips, T. L. (2004) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2004.01.038 · Cited by 42 · see citing works
CT Depiction of Regional Nodal Stations for Lung Cancer Staging
Ko, J. P., Drucker, E. A., Shepard, J.-A. O., Mountain, C. F., Dresler, C. M., Sabloff, B. S., & McLoud, T. C. (2000) · American Journal of Roentgenology · doi:10.2214/ajr.174.3.1740775 · Cited by 42 · 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
3D general lesion segmentation in CT
Jolly, M., & Grady, L. J. (2008) · IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI) · doi:10.1109/isbi.2008.4541116 · Cited by 40 · see citing works
CT Demonstration of the 1996 AJCC-UICC Regional Lymph Node Classification for Lung Cancer Staging
Cymbalista, M., Waysberg, A., Zacharias, C. E., Ajavon, Y., Riquet, M., Rebibo, G., & Greniér, P. (1999) · Radiographics · doi:10.1148/radiographics.19.4.g99jl09899 · Cited by 40 · see citing works
Cuadra, M. B., Duay, V., & Thiran, J. (2015) · Springer eBooks · doi:10.1007/978-0-387-09749-7_12 · Cited by 39 · see citing works
Automatic mediastinal lymph node detection in chest CT
Feuerstein, M., Deguchi, D., Kitasaka, T., Iwano, S., Imaizumi, K., Hasegawa, Y., Suenaga, Y., & Mori, K. (2009) · Medical Imaging · doi:10.1117/12.811101 · Cited by 37 · see citing works
Self-citationThree-dimensional active surface approach to lymph node segmentation
Honea, D. M., & Snyder, W. E. (1999) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.348493 · Cited by 36 · see citing works
Rogowska, J., Batchelder, K. F., Gazelle, G. S., Halpern, E. F., CONNOR, W., & Wolf, G. L. (1996) · Investigative Radiology · doi:10.1097/00004424-199603000-00004 · Cited by 35 · see citing works
Improving Deriche-style Recursive Gaussian Filters
Farnebäck, G., & Westin, C. (2006) · Journal of Mathematical Imaging and Vision · doi:10.1007/s10851-006-8464-z · Cited by 34 · see citing works
Kitasaka, T., Mori, K., Hasegawa, J., & Jun-ichiro, T. (2002) · Forma · Cited by 32 · see citing works
Self-citationLymph node segmentation on CT images by a shape model guided deformable surface methodh
Maleike, D., Fabel, M., Tetzlaff, R., von Tengg‐Kobligk, H., Heimann, T., Meinzer, H.-P., & Ivo Wolf, A. (2008) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.770352 · Cited by 27 · see citing works
Adaptive Branch Tracing and Image Sharpening for Airway Tree Extraction in 3-D Chest CT
Feuerstein, M. ;. (n.d.) · mediaTUM (Technical University of Munich) · Cited by 21 · see citing works
Self-citationMediastinal Staging 2005: Pictures, Scopes, and Scalpels
Pass, H. I. (2005) · Seminars in Oncology · doi:10.1053/j.seminoncol.2005.02.014 · Cited by 17 · see citing works
Detection of mediastinal and hilar lymph nodes by 16-row MDCT: Is contrast material needed?
Takahashi, M., Nitta, N., Takazakura, R., Nagatani, Y., Ushio, N., & Murata, K. (2007) · European Journal of Radiology · doi:10.1016/j.ejrad.2007.05.028 · Cited by 16 · see citing works
Automatische Detektion von Lymphknoten in CT-Datensätzen des Halses
Dornheim, L., & Dornheim, J. (2008) · Informatik aktuell · doi:10.1007/978-3-540-78640-5_62 · Cited by 14 · see citing works
Head and Neck Lymph Node Region Delineation Using a Hybrid Image Registration Method
Teng, C.-C., Shapiro, L. G., & Kalet, I. J. (2006) · doi:10.1109/isbi.2006.1624953 · Cited by 13 · see citing works
Extraction and visualization of the central chest lymph-node stations
Lu, K., Merritt, S. A., & Higgins, W. E. (2008) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.768691 · Cited by 12 · see citing works
Novel method and applications for labeling and identifying lymph nodes
Kiraly, A. P., Naidich, D. P., Guendel, L., Zhang, L., & Novak, C. L. (2007) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.709413 · Cited by 9 · see citing works
Adaptive model based pulmonary artery segmentation in 3D chest CT
Feuerstein, M., Kitasaka, T., & Mori, K. (2010) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.843750 · Cited by 7 · see citing works
Self-citationSystem for definition of the central-chest vasculature
Taeprasartsit, P., & Higgins, W. E. (2009) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.811143 · Cited by 4 · see citing works
Medical Imaging 2006: Physiology, Function, and Structure from Medical Images
Manduca, A., & Hu, X. (2006) · Cited by 4 · see citing works
A Generalized Squared Euclidean Distance Transform with Voronoi Maps
King, B., Döker, R., Meier, S., Shin, H., & Galanski, M. (2006) · The Insight Journal · doi:10.54294/c46wom · Cited by 3 · see citing works
Lymphatic atlas-based target volume definition for intensity-modulated radiation therapy planning
Qatarneh, S. M., Kiricuţă, I., Brahme, A., Noz, M. E., Ferreira, B. C., Kim, W. C., & Lind, B. K. (2007) · Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment · doi:10.1016/j.nima.2007.06.105 · Cited by 2 · see citing works
Development of an automated detection algorithm for mediastinal lymph nodes on chest x-ray CT images
Muraoka, Y., Tian, Q. C., Tomita, Y., & Honda, S. (1998) · Cited by 2 · see citing works
Marco Feuerstein, A., Kitasaka, T., & Mori, K. (2009) · Cited by 2 · see citing works
Self-citationLee, J. H., Lee, K. S., Kim, T. S., Yi, C. A., Cho, J. M., & Lee, M. H. (2003) · Journal of the Korean Radiological Society · doi:10.3348/jkrs.2003.49.4.285 · Cited by 1 · see citing works
Flagged first, then by citations
4 of the 73 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
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
Roth, H. R., Lu, L., Seff, A., Cherry, K. M., Hoffman, J., Wang, S.-J., Liu, J., Turkbey, E., & Summers, R. (2014) · International Conference on Medical Image Computing and Computer-Assisted Intervention · doi:10.1007/978-3-319-10404-1_65 · Cited by 593 · see citing works
“…not directly comparable with the other studies since Barbu et al. (2012) reports on axillary, pelvic, and only some parts of the abdominal regions, while others evaluate only on me-diastinum (Feuerstein et al., 2012, Feulner et al., 2013, Feuerstein et al., 2009) or abdomen (Nakamura et al., 2013).”
ResultA Multistage Discriminative Model for Tumor and Lymph Node Detection in Thoracic Images
Song, Y., Cai, W., Kim, J., & Feng, D. D. (2012) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2012.2185057 · Cited by 57 · see citing works
Liu, J., Hoffman, J., Zhao, J., Yao, J., Lü, L., Kim, L., Turkbey, E. B., & Summers, R. M. (2016) · Medical Physics · doi:10.1118/1.4954009 · Cited by 56 · see citing works
2D View Aggregation for Lymph Node Detection Using a Shallow Hierarchy of Linear Classifiers
Seff, A., Lü, L., Cherry, K. M., Roth, H. R., Liu, J., Wang, S., Hoffman, J., Turkbey, E. B., & Summers, R. M. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10404-1_68 · Cited by 53 · see citing works
Luó, X., & Mori, K. (2014) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2307052 · Cited by 43 · see citing works
Self-citationContinuous rotation invariant features for gradient-based texture classification
Hanbay, K., Alpaslan, N., Talu, M. F., Hanbay, D., Karcı, A., & Kocamaz, A. F. (2014) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2014.10.004 · Cited by 33 · see citing works
“In many state-of-theart studies, the object edges, corners, and shape information are obtained by the Hessian matrix [65,66].”
BackgroundJin, D., Guo, D.-Z., Ge, J., Ye, X.-H., & Lu, L. (2022) · Journal of the National Cancer Center · doi:10.1016/j.jncc.2022.09.003 · Cited by 31 · see citing works
“76 Very few previous works 62 , 77–79 have explored the problem of automated LN station segmentation, and limited performances have been reported due to the complexity and ambiguity of the definition of the boundary of LN stations.”
BackgroundLiu, J., Zhao, J., Hoffman, J., Yao, J.-H., Zhang, W., Turkbey, E., Wang, S.-J., Kim, C. F., & Summers, R. (2014) · Medical Imaging · doi:10.1117/12.2043737 · Cited by 33 · see citing works
Mercieca, S., Belderbos, J. S. A., Jaeger, K. D., Schinagl, D. A. X., van Zijp, N. V. D. V., Pomp, J., Theuws, J. C. M., Khalifa, J., van de Vaart, P., & van Herk, M. (2017) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2017.11.020 · Cited by 25 · see citing works
Abdominal lymphadenopathy detection using random forest
Cherry, K. M., Wang, S.-J., Turkbey, E., & Summers, R. (2014) · Medical Imaging · doi:10.1117/12.2043837 · Cited by 25 · see citing works
“Feuerstein et al.5 created a mediastinal atlas on chest CT images for automatic detection and station mapping of lymph nodes.”
BackgroundMediastinal Lymph Node Detection and Segmentation Using Deep Learning
Nayan, A.-A., Kijsirikul, B., & Iwahori, Y. (2022) · IEEE Access · doi:10.1109/access.2022.3198996 · Cited by 19 · see citing works
“As a result, manually counting and quantifying LNs in images from human observers is time-consuming and error-prone [5].”
BackgroundGuo, D., Ye, X., Ge, J., Di, X., Lü, L., Huang, L., Xie, G., Xiao, J. Z., Lu, Z., Peng, L., et al. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87240-3_1 · Cited by 18 · see citing works
Zeineldine, S. M., Akl, I. B., Mohamad, M., Chmaisse, A., Chahwan, S., Asmar, K., El‐Merhi, F., & Saade, C. (2018) · British Journal of Radiology · doi:10.1259/bjr.20180509 · Cited by 15 · see citing works
Xu, G., Udupa, J. K., Tong, Y., Odhner, D., Cao, H., & Torigian, D. A. (2020) · Medical Physics · doi:10.1002/mp.14240 · Cited by 13 · see citing works
Iuga, A.-I., Lossau, T., Caldeira, L., Rinneburger, M., Lennartz, S., Hokamp, N. G., Püsken, M., Carolus, H., Maintz, D. C., Klinder, T., et al. (2021) · European Journal of Radiology · doi:10.1016/j.ejrad.2021.109718 · Cited by 11 · see citing works
Yu, Q., Wang, Y., Yan, K., Li, H., Guo, D., Zhang, L., Shen, N., Wang, Q., Ding, X., Lü, L., et al. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72946-1_11 · Cited by 11 · see citing works
Oda, H., Bhatia, K. K., Oda, M., Kitasaka, T., Iwano, S., Homma, H., Takabatake, H., Mori, M., Natori, H., Schnabel, J. A., et al. (2017) · Journal of medical imaging · doi:10.1117/1.jmi.4.4.044502 · Cited by 10 · see citing works
“Feuerstein et al. 20 proposed a mediastinal lymph node detection algorithm using this.”
BackgroundSelf-citationTemplate Creation for High-Resolution Computed Tomography Scans of the Lung in R Software
Ryan, S., Vestal, B. E., Maier, L. A., Carlson, N. E., & Muschelli, J. (2019) · Academic Radiology · doi:10.1016/j.acra.2019.10.030 · Cited by 9 · see citing works
“Examples include lungs 2 , lymph nodes 3 , liver 4 , and spleen 5 , which are often imaged with computed tomography (CT).”
BackgroundThree Aspects on Using Convolutional Neural Networks for Computer-Aided Detection in Medical Imaging
Shin, H.-C., Roth, H. R., Gao, M., Lü, L., Xu, Z., Nogues, I., Yao, J., Mollura, D. J., & Summers, R. M. (2017) · Advances in computer vision and pattern recognition · doi:10.1007/978-3-319-42999-1_8 · Cited by 9 · see citing works
Cao, Y., Feng, J., Wang, C., Yang, F., Wang, X., Xu, J., Huang, C., Zhang, S., Li, Z., Mao, L., et al. (2023) · La radiologia medica · doi:10.1007/s11547-023-01747-x · Cited by 8 · see citing works
“These studies were based on traditional CAD systems using machine learning and feature engineering methods, requiring prior knowledge or manual operations [15, 19, 20].”
Thoracic Abnormality Detection with Data Adaptive Structure Estimation
Song, Y., Cai, W., Zhou, Y., & Feng, D. D. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33415-3_10 · Cited by 7 · see citing works
“They avoid handling the influence from the other type of abnormality by assuming its non-existence [2, 4] or with user-defined region of interest [1, 3].”
BackgroundMethodInfluentialGuo, D.-Z., Ge, J., Yan, K., Wang, P., Zhu, Z.-T., Zheng, D., Hua, X.-S., Lu, L., Ho, T., Ye, X.-H., et al. (2022) · International Conference on Medical Image Computing and Computer-Assisted Intervention · doi:10.1007/978-3-031-16443-9_6 · Cited by 7 · see citing works
“Feuerstein et al. built a statistical atlas for lymph node detection in chest CT [7].”
BackgroundMethodLearning directional relative positions between mediastinal lymph node stations and organs
Sarrut, D., Rit, S., Claude, L., Pinho, R., Pitson, G., Bouilhol, G., & Lynch, R. (2014) · Medical Physics · doi:10.1118/1.4873677 · Cited by 7 · see citing works
Automated abdominal lymph node segmentation based on RST analysis and SVM
Nimura, Y., Hayashi, Y., Kitasaka, T., Furukawa, K., Misawa, K., & Mori, K. (2014) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2043349 · Cited by 6 · see citing works
“Table 2 and Figure 1 show detection results and comparison with previous methods.(1,2) The sensitivity of the proposed method was 82.”
BackgroundResultSelf-citationXu, G., Cao, H., & Jiang, G. (2020) · International Conference on Bioinformatics and Biomedical Science (ICBBS) · doi:10.1145/3431943.3432285 · Cited by 6 · see citing works
“Hence, it is time consuming and error prone bymanually counting andmeasuring LNs in images from human observes [3].”
BackgroundLiu, J., Zhao, J., Hoffman, J., Yao, J., Lü, L., Turkbey, E. B., Kim, C., & Summers, R. M. (2014) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2014.6868068 · Cited by 5 · see citing works
Hoffman, J., Liu, J., Turkbey, E. B., Kim, L., & Summers, R. M. (2015) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2082190 · Cited by 5 · see citing works
Engelson, S., Ehrhardt, J., Kepp, T., Niemeijer, J., Schierholz, S., Berkel, L., Elser, Y., Sieren, M. M., & Heinz Handels, A. (2024) · SPIE Medical Imaging: Computer-Aided Diagnosis · doi:10.1117/12.3006158 · Cited by 4 · see citing works
“Feuerstein et al. 2 transfer the use of atlases for segmentation purposes from the field of brain imaging to lymphatic tissue in the chest.”
BackgroundZheng, Z., Fang, X., Yao, J., Zhu, M., Lu, L., Shi, Y., Lu, H., Lu, J., Zhang, L., Shao, C., et al. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-024-02314-1 · Cited by 3 · see citing works
DiffAtlas: GenAI-Fying Atlas Segmentation via Image-Mask Diffusion
Zhang, H., Liu, Y., Yang, J., Guo, W., Wang, X., & Fua, P. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-05325-1_16 · Cited by 3 · see citing works
Nakamura, Y., Nimura, Y., Kitasaka, T., Mizuno, S., Furukawa, K., Goto, H., Fujiwara, M., Misawa, K., Ito, M., Nawano, S., et al. (2015) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2082500 · Cited by 3 · see citing works
“Feuerstein et al.(3) proposed a method based on the probabilistic atlas showing the existences of mediastinal lymph nodes.”
MethodInfluentialSelf-citationSpatio‐temporal context based recurrent visual attention model for lymph node detection
Peng, H., & Peng, Y. (2020) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22430 · Cited by 3 · see citing works
“5 To address this gap, many relevant research works 6-9 have devoted efforts in directly using 3D spatial information from volumetric CT images for computer-aided detection systems.”
BackgroundUnderstanding Medical Images Based on Computational Anatomy Models
Hanaoka, S., Kamiya, N., Sato, Y., Mori, K., Fukuda, H., Taki, Y., Sato, K., Wu, K., Masutani, Y., Hara, T., et al. (2017) · Springer eBooks · doi:10.1007/978-4-431-55976-4_3 · Cited by 2 · see citing works
Self-citationNakamura, Y., Nimura, Y., Oda, M., Kitasaka, T., Furukawa, K., Goto, H., Fujiwara, M., Misawa, K., & Mori, K. (2016) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2214925 · Cited by 2 · see citing works
Self-citationOda, H., Nimura, Y., Oda, M., Kitasaka, T., Iwano, S., Honma, H., Takabatake, H., Mori, M., Natori, H., & Mori, K. (2016) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2216663 · Cited by 1 · see citing works
Self-citationMori, K. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-816176-0.00009-0 · Cited by 1 · see citing works
Self-citationContext Enhanced Graphical Model for Object Localization in Medical Images
Song, Y., Cai, W., Huang, H., Wang, Y., & Feng, D. D. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-36620-8_19
“For such imaging problems, while lots of work have been reported [25, 16, 19, 5, 18, 4, 21, 15], they are mostly designed to be domain specific; and often rely on sophisticated feature sets, which can be computational-intensive and difficult to adapt to other imaging problems.”
BackgroundStation number assignment to abdominal lymph node for assisting gastric cancer surgery
Hayashi, Y., Misawa, K., & Mori, K. (2020) · Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization · doi:10.1080/21681163.2020.1835543
“In the lung cancer treatment assistance system, several research groups have introduced not only detection of the lymph nodes but also station mapping of the lymph nodes (Lu et al. 2011; Feuerstein et al. 2012; Liu et al. 2016).”
BackgroundSelf-citationAutomated Detection of Mediastinal Lymph Nodes for Assistance of Transbronchial Needle Aspiration
Kitasaka, T., Mitsuhiro, K., Oda, M., Iwano, S., & Mori, K. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-38085-3_16
Self-citationAutomatic estimation of mediastinal lymph node map in CT images
Kawamura, Y., Kikuchi, T., Oda, M., Wang, C., Hayashi, Y., Roth, H. R., Yang, D., & Mori, K. (2026) · SPIE Medical Imaging: Computer-Aided Diagnosis · doi:10.1117/12.3084458
“6 However, these methods did not evaluate the LNMs themselves, limiting explainability for clinicians.”
Self-citationUtility of artificial intelligence for identifying thoracic lymphadenopathy in lung cancer
Dervis, M., Burade, A., Lanuti, M. D., Dagogo‐Jack, I., & Digumarthy, S. R. (2026) · Lung Cancer · doi:10.1016/j.lungcan.2026.109596
- Authors
- Feuerstein, M., Glocker, B., Kitasaka, T., Nakamura, Y., Iwano, S., & Mori, K.
- Affiliations
- Microsoft Research (United Kingdom) (GB)
- Aichi Institute of Technology (JP)
- Nagoya University (JP)
- Venue
- Medical Image Analysis
- Publisher
- Elsevier BV
- Published
- 2012-01
- Volume
- 16
- Issue
- 1
- Pages
- 63-74
- ISSN
- 1361-8415 (ISSN-L)
- OpenAlex
- W2003644909
- Cited by
- 43
- References
- 73
- Citations by year
- 2026: 2 · 2025: 1 · 2024: 3 · 2023: 1 · 2022: 4 · 2021: 2 · 2020: 4 · 2019: 2 · 2018: 1 · 2017: 4 · 2016: 4 · 2015: 2 · 2014: 9 · 2013: 2 · 2012: 2
- FWCI
- 4.48
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Medical Image Analysis (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Ministry of Education, Culture, Sports, Science and Technology
- Japan Society for the Promotion of Science (21103006)
- 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
- mediastinal lymph nodes, lymph node detection, lung cancer staging, deformable image registration, probability maps, aortic lymph nodes
- MeSH terms
- Algorithms, Artificial Intelligence, Computer Simulation, Humans, Lymph Nodes, Mediastinum, Models, Anatomic, Pattern Recognition, Automated, Radiographic Image Enhancement, Radiographic Image Interpretation, Computer-Assisted, Sensitivity and Specificity, Radiography, Thoracic, Tomography, X-Ray Computed, Reproducibility of Results, Imaging, Three-Dimensional
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
