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Urinary bladder segmentation in CT urography using deepâ learning convolutional neural network and level sets
dc.contributor.authorCha, Kenny H.
dc.contributor.authorHadjiiski, Lubomir
dc.contributor.authorSamala, Ravi K.
dc.contributor.authorChan, Heang‐ping
dc.contributor.authorCaoili, Elaine M.
dc.contributor.authorCohan, Richard H.
dc.date.accessioned2017-01-06T20:47:57Z
dc.date.available2017-06-01T16:55:23Zen
dc.date.issued2016-04
dc.identifier.citationCha, Kenny H.; Hadjiiski, Lubomir; Samala, Ravi K.; Chan, Heang‐ping ; Caoili, Elaine M.; Cohan, Richard H. (2016). "Urinary bladder segmentation in CT urography using deepâ learning convolutional neural network and level sets." Medical Physics 43(4): 1882-1896.
dc.identifier.issn0094-2405
dc.identifier.issn2473-4209
dc.identifier.urihttps://hdl.handle.net/2027.42/134923
dc.publisherAmerican Association of Physicists in Medicine
dc.publisherWiley Periodicals, Inc.
dc.subject.otherbiological organs
dc.subject.othercancer
dc.subject.othercomputerised tomography
dc.subject.otherimage segmentation
dc.subject.othermedical image processing
dc.subject.otherneural nets
dc.subject.otherpattern classification
dc.subject.otherComputed tomography
dc.subject.otherComputerised tomographs
dc.subject.otherBiological material, e.g. blood, urine; Haemocytometers
dc.subject.otherDigital computing or data processing equipment or methods, specially adapted for specific applications
dc.subject.otherImage data processing or generation, in general
dc.subject.othercomputerâ aided detection
dc.subject.otherdeepâ learning
dc.subject.othersegmentation
dc.subject.otherCT urography
dc.subject.otherbladder
dc.subject.otherlevel set
dc.subject.otherCancer
dc.subject.otherMedical image segmentation
dc.subject.otherRadiologists
dc.subject.otherComputed tomography
dc.subject.otherMedical image contrast
dc.subject.otherArtificial neural networks
dc.subject.otherImage detection systems
dc.subject.otherMedical magnetic resonance imaging
dc.subject.otherAnatomy
dc.titleUrinary bladder segmentation in CT urography using deepâ learning convolutional neural network and level sets
dc.typeArticleen_US
dc.rights.robotsIndexNoFollow
dc.subject.hlbsecondlevelMedicine (General)
dc.subject.hlbtoplevelHealth Sciences
dc.description.peerreviewedPeer Reviewed
dc.contributor.affiliationumDepartment of Radiology, The University of Michigan, Ann Arbor, Michigan 48109â 0904
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/134923/1/mp4498.pdf
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/134923/2/mp4498_am.pdf
dc.identifier.doi10.1118/1.4944498
dc.identifier.sourceMedical Physics
dc.identifier.citedreferenceW. C. Liu, K. J. Mortele, and S. G. Silverman, â Incidental extraurinary findings at MDCT urography in patients with hematuria: Prevalence and impact on imaging costs,â Am. J. Roentgenol. 185, 1051 â 1056 ( 2005 ). 10.2214/AJR.04.0218
dc.identifier.citedreferenceM. Noroozian, R. H. Cohan, E. M. Caoili, N. C. Cowan, and J. H. Ellis, â Multislice CT urography: State of the art,â Br. J. Radiol. 77, S74 â S86 ( 2004 ). 10.1259/bjr/13478281
dc.identifier.citedreferenceS. B. Park, J. K. Kim, H. J. Lee, H. J. Choi, and K.â S. Cho, â Hematuria: Portal venous phase multi detector row CT of the bladderâ a prospective study,â Radiology 245, 798 â 805 ( 2007 ). 10.1148/radiol.2452061060
dc.identifier.citedreferenceG. S. Sudakoff, D. P. Dunn, M. L. Guralnick, R. S. Hellman, D. Eastwood, and W. A. See, â Multidetector computerized tomography urography as the primary imaging modality for detecting urinary tract neoplasms in patients with asymptomatic hematuria,â J. Urol. 179, 862 â 867 ( 2008 ). 10.1016/j.juro.2007.10.061
dc.identifier.citedreferenceK. Cha, L. Hadjiiski, H.â P. Chan, R. H. Cohan, E. M. Caoili, and C. Zhou, â Detection of urinary bladder mass in CT urography with SPAN,â Med. Phys. 42, 4271 â 4284 ( 2015 ). 10.1118/1.4922503
dc.identifier.citedreferenceL. Li, Z. Wang, X. Li, X. Wei, H. L. Adler, W. Huang, S. Rizvi, H. Meng, D. P. Harrington, and Z. Liang, â A new partial volume segmentation approach to extract bladder wall for computer aided detection in virtual cystoscopy,â Proc. SPIE 5369, 199 â 206 ( 2004 ). 10.1117/12.535913
dc.identifier.citedreferenceC. Duan, Z. Liang, S. Bao, H. Zhu, S. Wang, G. Zhang, J. J. Chen, and H. Lu, â A coupled level set framework for bladder wall segmentation with application to MR cystography,â IEEE Trans. Med. Imaging 29, 903 â 915 ( 2010 ). 10.1109/tmi.2009.2039756
dc.identifier.citedreferenceC. J. Duan, K. H. Yuan, F. H. Liu, P. Xiao, G. Q. Lv, and Z. R. Liang, â An adaptive windowâ setting scheme for segmentation of bladder tumor surface via MR cystography,â IEEE Trans. Inf. Technol. Biomed. 16, 720 â 729 ( 2012 ). 10.1109/titb.2012.2200496
dc.identifier.citedreferenceH. Han, L. Li, C. Duan, H. Zhang, Y. Zhao, and Z. Liang, â A unified EM approach to bladder wall segmentation with coupled levelâ set constraints,â Med. Image Anal. 17, 1192 â 1205 ( 2013 ). 10.1016/j.media.2013.08.002
dc.identifier.citedreferenceX. F. Chai, M. van Herk, A. Betgen, M. Hulshof, and A. Bel, â Automatic bladder segmentation on CBCT for multiple plan ART of bladder cancer using a patientâ specific bladder model,â Phys. Med. Biol. 57, 3945 â 3962 ( 2012 ). 10.1088/0031â 9155/57/12/3945
dc.identifier.citedreferenceL. Hadjiiski, H. P. Chan, Y. Law, R. H. Cohan, E. M. Caoili, H. C. Cho, C. Zhou, and J. Wei, â Segmentation of urinary bladder in CT urography (CTU) using class,â Proc. SPIE 8315, 83150Jâ 83151 â 83150Jâ 83157 ( 2012 ). 10.1117/12.912847
dc.identifier.citedreferenceL. M. Hadjiiski, B. Sahiner, H. P. Chan, E. M. Caoili, R. H. Cohan, and C. Zhou, â Automated segmentation of urinary bladder and detection of bladder lesions in multiâ detector row CT urography,â Proc. SPIE 7260, 72603Râ 72601 â 72603Râ 72607 ( 2009 ). 10.1117/12.813864
dc.identifier.citedreferenceL. Hadjiiski, H. P. Chan, R. H. Cohan, E. M. Caoili, Y. Law, K. Cha, C. Zhou, and J. Wei, â Urinary bladder segmentation in CT urography (CTU) using CLASS,â Med. Phys. 40, 111906 (10pp.) ( 2013 ). 10.1118/1.4823792
dc.identifier.citedreferenceK. Cha, L. M. Hadjiiski, H.â P. Chan, E. M. Caoili, R. H. Cohan, and C. Zhou, â CT urography: Segmentation of urinary bladder using CLASS with local contour refinement,â Phys. Med. Biol. 59, 2767 â 2785 ( 2014 ). 10.1088/0031â 9155/59/11/2767
dc.identifier.citedreferenceH. P. Chan, S. C. B. Lo, B. Sahiner, K. L. Lam, and M. A. Helvie, â Computerâ aided detection of mammographic microcalcifications: Pattern recognition with an artificial neural network,â Med. Phys. 22, 1555 â 1567 ( 1995 ). 10.1118/1.597428
dc.identifier.citedreferenceR. K. Samala, H. P. Chan, Y. Lu, L. M. Hadjiiski, J. Wei, and M. A. Helvie, â Digital breast tomosynthesis: Computerâ aided detection of clustered microcalcifications on planar projection images,â Phys. Med. Biol. 59, 7457 â 7477 ( 2014 ). 10.1088/0031â 9155/59/23/7457
dc.identifier.citedreferenceM. N. Gurcan, B. Sahiner, H. P. Chan, L. M. Hadjiiski, and N. Petrick, â Selection of an optimal neural network architecture for computerâ aided diagnosisâ Comparison of automated optimization techniques,â Radiology 217, 436 ( 2000 ). 10.1148/radiology.217.2.r00nv09436
dc.identifier.citedreferenceM. N. Gurcan, B. Sahiner, H. P. Chan, L. M. Hadjiiski, and N. Petrick, â Selection of an optimal neural network architecture for computerâ aided detection of microcalcificationsâ Comparison of automated optimization techniques,â Med. Phys. 28, 1937 â 1948 ( 2001 ). 10.1118/1.1395036
dc.identifier.citedreferenceJ. Ge, B. Sahiner, L. M. Hadjiiski, H.â P. Chan, J. Wei, M. A. Helvie, and C. Zhou, â Computer aided detection of clusters of microcalcifications on full field digital mammograms,â Med. Phys. 33, 2975 â 2988 ( 2006 ). 10.1118/1.2211710
dc.identifier.citedreferenceJ. Ge, L. M. Hadjiiski, B. Sahiner, J. Wei, M. A. Helvie, C. Zhou, and H.â P. Chan, â Computerâ aided detection system for clustered microcalcifications: Comparison of performance on fullâ field digital mammograms and digitized screenâ film mammograms,â Phys. Med. Biol. 52, 981 â 1000 ( 2007 ). 10.1088/0031â 9155/52/4/008
dc.identifier.citedreferenceP. Filev, L. Hadjiiski, H.â P. Chan, B. Sahiner, J. Ge, M. A. Helvie, M. Roubidoux, and C. A. Zhou, â Automated regional registration and characterization of corresponding microcalcification clusters on temporal pairs of mammograms for interval change analysis,â Med. Phys. 35, 5340 â 5350 ( 2008 ). 10.1118/1.3002311
dc.identifier.citedreferenceR. K. Samala, H. P. Chan, Y. Lu, L. M. Hadjiiski, J. Wei, and M. A. Helvie, â Computerâ aided detection system for clustered microcalcifications in digital breast tomosynthesis using joint information from volumetric and planar projection images,â Phys. Med. Biol. 60, 8457 â 8479 ( 2015 ). 10.1088/0031â 9155/60/21/8457
dc.identifier.citedreferenceA. Krizhevsky, cudaâ convnet, 2012, see https://code.google.com/p/cudaâ convnet/.
dc.identifier.citedreferenceA. Krizhevsky, I. Sutskever, and G. E. Hinton, â ImageNet classification with deep convolutional neural networks,â in NIPS 2012: Neural Information Processing Systems, Lake Tahoe, Nevada ( 2012 ).
dc.identifier.citedreferenceO. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, and M. Bernstein, â Imagenet large scale visual recognition challenge,â Int. J. Comput. Vis. 115, 211 â 252 ( 2015 ). 10.1007/s11263â 015â 0816â y
dc.identifier.citedreferenceA. Krizhevsky, â Learning Multiple Layers of Features from Tiny Images,â M.S. thesis, University of Toronto, Toronto, 2009, see http://www.cs.toronto.edu/~kriz/learningâ featuresâ 2009â TR.pdf.
dc.identifier.citedreferenceP. Viola and M. Jones, â Rapid object detection using a boosted cascade of simple features,â in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, edited by A. Jacobs and T. Baldwin ( IEEE Computer Society, Los Alamitos, CA, 2001 ), Vol. 1, pp. 511 â 518.
dc.identifier.citedreferenceR. Lienhart and J. Maydt, â An extended set of Haarâ like features for rapid object detection,â in Proceedings of IEEE International Conference on Image Processing ( IEEE, 2002 ), Vol. I, pp. 900 â 903. 10.1109/ICIP.2002.1038171
dc.identifier.citedreferenceT. W. Way, L. M. Hadjiiski, B. Sahiner, H.â P. Chan, P. N. Cascade, E. A. Kazerooni, N. Bogot, and C. Zhou, â Computerâ aided diagnosis of pulmonary nodules on CT scans: Segmentation and classification using 3D active contours,â Med. Phys. 33, 2323 â 2337 ( 2006 ). 10.1118/1.2207129
dc.identifier.citedreferenceP. Jaccard, â The distribution of the flora in the alpine zone,â New Phytol. 11, 37 â 50 ( 1912 ). 10.1111/j.1469â 8137.1912.tb05611.x
dc.identifier.citedreferenceE. Street, L. Hadjiiski, B. Sahiner, S. Gujar, M. Ibrahim, S. K. Mukherji, and H. P. Chan, â Automated volume analysis of head and neck lesions on CT scans using 3D level set segmentation,â Med. Phys. 34, 4399 â 4408 ( 2007 ). 10.1118/1.2794174
dc.identifier.citedreferenceV. Nair and G. E. Hinton, Presented at the Proceedings of the 27th International Conference on Machine Learning (ICMLâ 10) ( 2010 ).
dc.identifier.citedreferenceAmerican Cancer Society, What are the key statistics about bladder cancer?, 2015, available at www.cancer.org.
dc.identifier.citedreferenceS. A. Akbar, K. J. Mortele, K. Baeyens, M. Kekelidze, and S. G. Silverman, â Multidetector CT urography: Techniques, clinical applications, and pitfalls,â Semin. Ultrasound CT MRI 25, 41 â 54 ( 2004 ). 10.1053/j.sult.2003.11.002
dc.identifier.citedreferenceE. M. Caoili, R. H. Cohan, M. Korobkin, J. F. Platt, I. R. Francis, G. J. Faerber, J. E. Montie, and J. H. Ellis, â Urinary tract abnormalities: Initial experience with multiâ detector row CT urography,â Radiology 222, 353 â 360 ( 2002 ). 10.1148/radiol.2222010667
dc.identifier.citedreferenceC. L. McCarthy and N. C. Cowan, â Multidetector CT urography (MDâ CTU) for urothelial imaging,â Radiology 225, 237 ( 2002 ).
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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