Deepâ learning convolutional neural network: Inner and outer bladder wall segmentation in CT urography
dc.contributor.author | Gordon, Marshall N. | |
dc.contributor.author | Hadjiiski, Lubomir M. | |
dc.contributor.author | Cha, Kenny H. | |
dc.contributor.author | Samala, Ravi K. | |
dc.contributor.author | Chan, Heang‐ping | |
dc.contributor.author | Cohan, Richard H. | |
dc.contributor.author | Caoili, Elaine M. | |
dc.date.accessioned | 2019-02-12T20:24:43Z | |
dc.date.available | 2020-04-01T15:06:24Z | en |
dc.date.issued | 2019-02 | |
dc.identifier.citation | Gordon, Marshall N.; Hadjiiski, Lubomir M.; Cha, Kenny H.; Samala, Ravi K.; Chan, Heang‐ping ; Cohan, Richard H.; Caoili, Elaine M. (2019). "Deepâ learning convolutional neural network: Inner and outer bladder wall segmentation in CT urography." Medical Physics 46(2): 634-648. | |
dc.identifier.issn | 0094-2405 | |
dc.identifier.issn | 2473-4209 | |
dc.identifier.uri | https://hdl.handle.net/2027.42/147844 | |
dc.publisher | Wiley Periodicals, Inc. | |
dc.publisher | American Cancer Society Inc. | |
dc.subject.other | segmentation | |
dc.subject.other | bladder | |
dc.subject.other | bladder wall | |
dc.subject.other | computerâ aided diagnosis | |
dc.subject.other | CT urography | |
dc.subject.other | deep learning | |
dc.title | Deepâ learning convolutional neural network: Inner and outer bladder wall segmentation in CT urography | |
dc.type | Article | en_US |
dc.rights.robots | IndexNoFollow | |
dc.subject.hlbsecondlevel | Medicine (General) | |
dc.subject.hlbtoplevel | Health Sciences | |
dc.description.peerreviewed | Peer Reviewed | |
dc.description.bitstreamurl | https://deepblue.lib.umich.edu/bitstream/2027.42/147844/1/mp13326.pdf | |
dc.description.bitstreamurl | https://deepblue.lib.umich.edu/bitstream/2027.42/147844/2/mp13326_am.pdf | |
dc.identifier.doi | 10.1002/mp.13326 | |
dc.identifier.source | Medical Physics | |
dc.identifier.citedreference | Gurcan MN, Chan Hâ P, Sahiner B, Hadjiiski L, Petrick N, Helvie MA. Optimal neural network architecture selection: improvement in computerized detection of microcalcifications. Acad Rad. 2002; 9: 420 â 429. | |
dc.identifier.citedreference | Brady SM, Wenjun Chi J, Moore NR, Schnabel JA. Segmentation of the bladder wall using coupled level set methods, Chicago, IL, USA; 2011. | |
dc.identifier.citedreference | Hadjiiski L, Chan Hâ P, Law Y, et al. Segmentation of urinary bladder in CT urography (CTU) using CLASS. Proc SPIE. 2012; 8315: 83150J1 â 83150J7. | |
dc.identifier.citedreference | Hadjiiski LM, Sahiner B, Chan Hâ P, Caoili EM, Cohan RH, Zhou C. Automated segmentation of urinary bladder and detection of bladder lesions in multiâ detector row CT urography. Proc SPIE. 2009; 7260: 72603R1 â 72603R7. | |
dc.identifier.citedreference | Hadjiiski L, Chan HP, Cohan RH, et al. Urinary bladder segmentation in CT urography (CTU) using CLASS. Med Phys. 2013; 40: 111906. | |
dc.identifier.citedreference | Cha KH, Hadjiiski LM, Chan Hâ P, Caoili EM, Cohan RH, Zhou C. CT urography: segmentation of urinary bladder using CLASS with local contour refinement. Phys Med Biol. 2014; 59: 2767 â 2785. | |
dc.identifier.citedreference | Fukushima K, Miyake S, Ito T. Neocognitron: a neural network model for a mechanism of visual pattern recognition. IEEE Trans Syst Man Cybern. 1983; SMCâ 13: 826 â 834. | |
dc.identifier.citedreference | Lo SCB, Lin JS, Freedman MT, Mun SK. Computerâ assisted diagnosis of lung nodule detection using artificial convolution neural network. Proc SPIE. 1993; 1898: 859 â 869. | |
dc.identifier.citedreference | Chan Hâ P, Lo SCB, Helvie MA, Goodsitt MM, Cheng SNC, Adler DD. Recognition of mammographic microcalcifications with artificial neural network. Radiology. 1993; 189: 318. | |
dc.identifier.citedreference | Chan Hâ P, Sahiner B, Lo SCB, et al. Computerâ aided diagnosis in mammography: detection of masses by artificial neural network. Med Phys. 1994; 21: 875 â 876. | |
dc.identifier.citedreference | Zhang W, Doi K, Giger ML, Wu Y, Nishikawa RM, Schmidt RA. Computerized detection of clustered microcalcifications in digital mammograms using a shiftâ invariant artificial neural network. Med Phys. 1994; 21: 517 â 524. | |
dc.identifier.citedreference | Chan Hâ P, Lo SCB, Sahiner B, Lam KL, Helvie MA. Computerâ aided detection of mammographic microcalcifications: pattern recognition with an artificial neural network. Med Phys. 1995; 22: 1555 â 1567. | |
dc.identifier.citedreference | Lo SCB, Chan Hâ P, Lin JS, Li H, Freedman M, Mun SK. Artificial convolution neural network for medical image pattern recognition. Neural Networks. 1995; 8: 1201 â 1214. | |
dc.identifier.citedreference | Sahiner B, Chan Hâ P, Petrick N, et al. Classification of mass and normal breast tissue: a convolution neural network classifier with spatial domain and texture images. IEEE Trans Med Imaging. 1996; 15: 598 â 610. | |
dc.identifier.citedreference | Gurcan MN, Sahiner B, Chan Hâ P, Hadjiiski LM, Petrick N. Selection of an optimal neural network architecture for computerâ aided detection of microcalcifications â Comparison of automated optimization techniques. Med Phys. 2001; 28: 1937 â 1948. | |
dc.identifier.citedreference | Samala RK, Chan Hâ P, Lu Y, Hadjiiski LM, Wei J, Helvie MA. Digital breast tomosynthesis: computerâ aided detection of clustered microcalcifications on planar projection images. Phys Med Biol. 2014; 59: 7457 â 7477. | |
dc.identifier.citedreference | Krizhevsky A. cudaâ convnet. http://code.google.com/p/cuda-convnet/ | |
dc.identifier.citedreference | Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Proceedings 25th International Conference on Neural Information Processing Systems, 2012; 1: 1097 â 1105. | |
dc.identifier.citedreference | Russakovsky O, Deng J, Su H, et al. Imagenet large scale visual recognition challenge. Int J Comput Vision. 2015; 115: 211 â 252. | |
dc.identifier.citedreference | Krizhevsky A. Learning Multiple Layers of Features from Tiny Images. Master’s thesis, Department of Computer Science, University of Toronto; 2009. | |
dc.identifier.citedreference | Litjens G, Kooi T, Bejnordi BE, et al. A survey on deep learning in medical image analysis. Med Image Anal. 2017; 42: 60 â 88. | |
dc.identifier.citedreference | Cha KH, Hadjiiski L, Samala RK, Chan HP, Caoili EM, Cohan RH. Urinary bladder segmentation in CT urography using deepâ learning convolutional neural network and level sets. Med Phys. 2016; 43: 1882 â 1896. | |
dc.identifier.citedreference | Gordon M, Hadjiiski L, Cha K, et al. Segmentation of inner and outer bladder wall using deepâ learning convolutional neural networks in CT urography. Proc SPIE. 2017; 10134: 1013402 â 1013402â 7. | |
dc.identifier.citedreference | Nair V, Hinton GE. Rectified linear units improve restricted Boltzmann machines. Proc International Conference on Machine Learning (ICML). 2010: 807 â 814. | |
dc.identifier.citedreference | Ioffe S, Szegedy C. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift; 2015. | |
dc.identifier.citedreference | Street E, Hadjiiski L, Sahiner B, et al. Automated volume analysis of head and neck lesions on ct scans using 3D level set segmentation. Med Phys. 2007; 34: 4399 â 4408. | |
dc.identifier.citedreference | Way TW, Hadjiiski LM, Sahiner B, et al. Computerâ aided diagnosis of pulmonary nodules on CT scans: segmentation and classification using 3D active contours. Med Phys. 2006; 33: 2323 â 2337. | |
dc.identifier.citedreference | American Cancer Society. Cancer Facts & Figures 2017. Atlanta: American Cancer Society Inc.; 2017. | |
dc.identifier.citedreference | Akbar SA, Mortele KJ, Baeyens K, Kekelidze M, Silverman SG. Multidetector CT urography: techniques, clinical applications, and pitfalls. Semin Ultrasound CT MRI. 2004; 25: 41 â 54. | |
dc.identifier.citedreference | Caoili EM, Cohan RH, Korobkin M, et al. Urinary tract abnormalities: initial experience with multiâ detector row CT urography. Radiology. 2002; 222: 353 â 360. | |
dc.identifier.citedreference | Liu WC, Mortele KJ, Silverman SG. Incidental extraurinary findings at MDCT urography in patients with hematuria: prevalence and impact on imaging costs. Am J Roentgenol. 2005; 185: 1051 â 1056. | |
dc.identifier.citedreference | McCarthy CL, Cowan NC. Multidetector CT urography (MDâ CTU) for urothelial imaging. Radiology. 2002; 225: 237. | |
dc.identifier.citedreference | Noroozian M, Cohan RH, Caoili EM, Cowan NC, Ellis JH. Multislice CT urography: state of the art. Br J Radiol. 2004; 77: S74 â S86. | |
dc.identifier.citedreference | Park SB, Kim JK, Lee HJ, Choi HJ, Cho Kâ S. Hematuria: portal venous phase multi detector row CT of the bladder â a prospective study. Radiology. 2007; 245: 798 â 805. | |
dc.identifier.citedreference | Sudakoff GS, Dunn DP, Guralnick ML, Hellman RS, Eastwood D, See WA. Multidetector computerized tomography urography as the primary imaging modality for detecting urinary tract neoplasms in patients with asymptomatic hematuria. J Urol. 2008; 179: 862 â 867. | |
dc.identifier.citedreference | Li L, Wang Z, Li X, et al. A new partial volume segmentation approach to extract bladder wall for computer aided detection in virtual cystoscopy. Proc SPIE. 2004; 5369: 199 â 206. | |
dc.identifier.citedreference | Duan C, Liang Z, Bao S, et al. A coupled level set framework for bladder wall segmentation with application to MR cystography. IEEE Trans Med Imaging. 2010; 29: 903 â 915. | |
dc.identifier.citedreference | Duan CJ, Yuan KH, Liu FH, Xiao P, Lv GQ, Liang ZR. An adaptive windowâ setting scheme for segmentation of bladder tumor surface via MR cystography. IEEE Trans Inf Technol Biomed. 2012; 16: 720 â 729. | |
dc.identifier.citedreference | Han H, Li L, Duan C, Zhang H, Zhao Y, Liang Z. A unified EM approach to bladder wall segmentation with coupled levelâ set constraints. Med Image Anal. 2013; 17: 1192 â 1205. | |
dc.identifier.citedreference | Chai XF, van Herk M, Betgen A, Hulshof M, Bel A. Automatic bladder segmentation on CBCT for multiple plan ART of bladder cancer using a patientâ specific bladder model. Phys Med Biol. 2012; 57: 3945 â 3962. | |
dc.identifier.citedreference | Ma Z, Jorge RN, Mascarenhas T, Tavares JM. Novel approach to segment the inner and outer boundaries of the bladder wall in T2â weighted magnetic resonance images. Ann Biomed Eng. 2011; 39: 2287 â 2297. | |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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