1932

Abstract

Sparsity is a powerful concept to exploit for high-dimensional machine learning and associated representational and computational efficiency. Sparsity is well suited for medical image segmentation. We present a selection of techniques that incorporate sparsity, including strategies based on dictionary learning and deep learning, that are aimed at medical image segmentation and related quantification.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-bioeng-060418-052147
2020-06-04
2024-06-24
Loading full text...

Full text loading...

/deliver/fulltext/bioeng/22/1/annurev-bioeng-060418-052147.html?itemId=/content/journals/10.1146/annurev-bioeng-060418-052147&mimeType=html&fmt=ahah

Literature Cited

  1. 1. 
    Zhang Z, Xu Y, Yang J, Li X, Zhang D 2015. A survey of sparse representation: algorithms and applications. IEEE Access 3:490–530
    [Google Scholar]
  2. 2. 
    Li S, Yin H, Fang L 2012. Group-sparse representation with dictionary learning for medical image denoising and fusion. IEEE Trans. Biomed. Eng. 59:3450–59
    [Google Scholar]
  3. 3. 
    Ma L, Moisan L, Yu J, Zeng T 2013. A dictionary learning approach for Poisson image deblurring. IEEE Trans. Med. Imaging 32:1277–89
    [Google Scholar]
  4. 4. 
    Nayak N, Chang H, Borowsky A, Spellman P, Parvin B 2013. Classification of tumor histopathology via sparse feature learning. Proceedings of the 10th IEEE International Symposium on Biomedical Imaging410–13 Piscataway, NJ: IEEE
    [Google Scholar]
  5. 5. 
    Onofrey JA, Oksuz I, Sarkar S, Venkataraman R, Staib LH, Papademetris X 2016. MRI-TRUS image synthesis with application to image-guided prostate intervention. Proceedings of the International Workshop on Simulation and Synthesis in Medical Imaging157–66 Berlin: Springer
    [Google Scholar]
  6. 6. 
    Huang X, Dione DP, Compas CB, Papademetris X, Lin BA et al. 2014. Contour tracking in echocardiographic sequences via sparse representation and dictionary learning. Med. Image Anal. 18:253–71
    [Google Scholar]
  7. 7. 
    Fang R, Chen T, Metaxas D, Sanelli P, Zhang S 2015. Sparsity techniques in medical imaging. Comput. Med. Imaging Graph. 46:1
    [Google Scholar]
  8. 8. 
    Brill AB, Price RR, McClain WJ, Landay MW, eds 1977.Proceedings of the 5th International Conference on Information Processing in Medical Imaging. Oak Ridge, TN: Oak Ridge Natl. Lab.
  9. 9. 
    Sklansky J 1976. Boundary detection in medical radiography. Digital Processing of Biomedical Images K Preston, M Onoe 307–22 New York: Plenum
    [Google Scholar]
  10. 10. 
    Duda RO, Hart PE, Stork DG 2012.Pattern Classification. New York: Wiley
  11. 11. 
    Ballard DH, Brown CM 1982.Computer Vision. Englewood Cliffs, NJ: Prentice Hall
  12. 12. 
    Pham D, Xu C, Prince J 2000. Current methods in medical image segmentation. Annu. Rev. Biomed. Eng. 2:315–37
    [Google Scholar]
  13. 13. 
    Ashburner J, Friston KJ 2005. Unified segmentation. NeuroImage 26:839–51
    [Google Scholar]
  14. 14. 
    Zhang Y, Brady M, Smith S 2001. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans. Med. Imaging 20:45–57
    [Google Scholar]
  15. 15. 
    Van Leemput K, Maes F, Vandermeulen D, Suetens P 1999. Automated model-based tissue classification of MR images of the brain. IEEE Trans. Med. Imaging 18:897–908
    [Google Scholar]
  16. 16. 
    Roy S, Carass A, Bazin PL, Prince JL 2009. A Rician mixture model classification algorithm for magnetic resonance images. Proceedings of the 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro406–9 Piscataway, NJ: IEEE
    [Google Scholar]
  17. 17. 
    Pham DL 2001. Spatial models for fuzzy clustering. Comput. Vis. Image Underst. 84:285–97
    [Google Scholar]
  18. 18. 
    Bai W, Shi W, Ledig C, Rueckert D 2015. Multi-atlas segmentation with augmented features for cardiac MR images. Med. Image Anal. 19:98–109
    [Google Scholar]
  19. 19. 
    Couprie C, Grady L, Najman L, Talbot H 2011. Power watershed: a unifying graph-based optimization framework. IEEE Trans. Pattern Anal. Mach. Intell. 33:1384–99
    [Google Scholar]
  20. 20. 
    Roy S, Carass A, Prince J 2011. A compressed sensing approach for MR tissue contrast synthesis. Inf. Process. Med. Imaging 22:371–83
    [Google Scholar]
  21. 21. 
    Wang Z, Donoghue C, Rueckert D 2013. Patch-based segmentation without registration: application to knee MRI. Proceedings of the International Workshop on Machine Learning in Medical Imaging98–105 Berlin: Springer
    [Google Scholar]
  22. 22. 
    Staib LH, Duncan JS 1992. Boundary finding with parametrically deformable models. IEEE Trans. Pattern Anal. Mach. Intell. 14:1061–75
    [Google Scholar]
  23. 23. 
    Chakraborty A, Staib LH, Duncan JS 1996. Deformable boundary finding in medical images by integrating gradient and region information. IEEE Trans. Med. Imaging 15:859–70
    [Google Scholar]
  24. 24. 
    Cootes TF, Taylor CJ, Cooper DH, Graham J 1995. Active shape models—their training and application. Comput. Vis. Image Underst. 61:38–59
    [Google Scholar]
  25. 25. 
    Cootes TF, Edwards G, Taylor C 2001. Active appearance models. IEEE Trans. Pattern Anal. Mach. Intell. 23:681–85
    [Google Scholar]
  26. 26. 
    Cortes C, Vapnik V 1995. Support-vector networks. Mach. Learn. 20:273–97
    [Google Scholar]
  27. 27. 
    Breiman L 2001. Random forests. Mach. Learn. 45:5–32
    [Google Scholar]
  28. 28. 
    Shen D, Wu G, Suk HI 2017. Deep learning in medical image analysis. Annu. Rev. Biomed. Eng. 19:221–48
    [Google Scholar]
  29. 29. 
    Marimont RB, Shapiro MB 1979. Nearest neighbour searches and the curse of dimensionality. IMA J. Appl. Math. 24:59–70
    [Google Scholar]
  30. 30. 
    Chen C, Ozolek J, Wang W, Rohde G 2011. A pixel classification system for segmenting biomedical images using intensity neighborhoods and dimension reduction. Proceedings of the IEEE International Symposium on Biomedical Imaging: From Nano to Macro1649–52 Piscataway, NJ: IEEE
    [Google Scholar]
  31. 31. 
    Baraniuk RG, Candès E, Elad M, Ma Y 2010. Applications of sparse representation and compressive sensing. Proc. IEEE 98:906–9
    [Google Scholar]
  32. 32. 
    Wright J, Yang A, Ganesh A, Sastry S, Ma Y 2009. Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31:210–27
    [Google Scholar]
  33. 33. 
    Huang K, Aviyente S 2006. Sparse representation for signal classification. Proceedings of Advances in Neural Information Processing Systems 19 (NIPS 2006) B. Schölkopf, J Platt, T Hofmann 609–16 Cambridge, MA: MIT Press
    [Google Scholar]
  34. 34. 
    Mairal J, Leordeanu M, Bach F, Hebert M, Ponce J 2008. Discriminative sparse image models for class-specific edge detection and image interpretation. Proceedings of the 2008 European Conference on Computer Vision43–56 Berlin: Springer
    [Google Scholar]
  35. 35. 
    Peyré G 2009. Sparse modeling of textures. J. Math. Imaging Vis. 34:17–31
    [Google Scholar]
  36. 36. 
    Skretting K, Husøy JH 2006. Texture classification using sparse frame-based representations. EURASIP J. Appl. Signal Process. 2006:052561
    [Google Scholar]
  37. 37. 
    Wright J, Ma Y, Mairal J, Sapiro G, Huang T, Yan S 2010. Sparse representation for computer vision and pattern recognition. Proc. IEEE 98:1031–44
    [Google Scholar]
  38. 38. 
    Candes E, Romberg J, Tao T 2006. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans. Inf. Theory 52:489–509
    [Google Scholar]
  39. 39. 
    Rodriguez F, Sapiro G 2007.Sparse representations for image classification: learning discriminative and reconstructive non-parametric dictionaries Tech. Rep., Univ. Minn., Minneapolis
  40. 40. 
    Zhang S, Zhan Y, Dewan M, Huang J, Metaxas DN, Zhou XS 2012. Towards robust and effective shape modeling: sparse shape composition. . Med. Image Anal. 16:265–77
    [Google Scholar]
  41. 41. 
    Zhang S, Zhan Y, Metaxas DN 2012. Deformable segmentation via sparse representation and dictionary learning. Med. Image Anal. 16:1385–96
    [Google Scholar]
  42. 42. 
    Zhang S, Zhan Y, Zhou Y, Uzunbas MG, Metaxas DN 2012. Shape prior modeling using sparse representation and online dictionary learning. Proceedings of the 15th Annual Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2012)435–42 Berlin: Springer
    [Google Scholar]
  43. 43. 
    Shi W, Zhuang X, Pizarro L, Bai W, Wang Het al 2012. Registration using sparse free-form deformations. Proceedings of the 15th Annual Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2012)659–66 Berlin: Springer
    [Google Scholar]
  44. 44. 
    Wee CY, Yap PT, Zhang D, Wang L, Shen D 2012. Constrained sparse functional connectivity networks for MCI classification. Proceedings of the 15th Annual Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2012)212–19 Berlin: Springer
    [Google Scholar]
  45. 45. 
    Davis G, Mallat S, Avellaneda M 1997. Adaptive greedy approximations. Constr. Approx. 13:57–98
    [Google Scholar]
  46. 46. 
    Mallat S, Zhang Z 1993. Matching pursuits with time-frequency dictionaries. IEEE Trans. Signal Process. 41:3397–415
    [Google Scholar]
  47. 47. 
    Pati YC, Rezaiifar R, Krishnaprasad PS 1993. Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition. Proceedings of the 27th Annual Asilomar Conference on Signals, Systems, and Computers40–44 Piscataway, NJ: IEEE
    [Google Scholar]
  48. 48. 
    Davis GM, Mallat SG, Zhang Z 1994. Adaptive time-frequency decompositions. Opt. Eng. 33:2183–91
    [Google Scholar]
  49. 49. 
    Tropp J 2004. Greed is good: algorithmic results for sparse approximation. IEEE Trans. Inf. Theory 50:2231–42
    [Google Scholar]
  50. 50. 
    Starck J, Elad M, Donoho D 2005. Image decomposition via the combination of sparse representations and a variational approach. IEEE Trans. Image Proc. 14:1570–82
    [Google Scholar]
  51. 51. 
    Donoho DL 2006. For most large underdetermined systems of linear equations the minimal ℓ1-norm solution is also the sparsest solution. Commun. Pure Appl. Math. 59:797–829
    [Google Scholar]
  52. 52. 
    Liao S, Gao Y, Lian J, Shen D 2013. Sparse patch-based label propagation for accurate prostate localization in CT images. IEEE Trans. Med. Imaging 32:419–34
    [Google Scholar]
  53. 53. 
    Tibshirani R 1996. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. B 58:267–88
    [Google Scholar]
  54. 54. 
    Zou H, Hastie T 2005. Regularization and variable selection via the elastic net. J. R. Stat. Soc. B 67:301–20
    [Google Scholar]
  55. 55. 
    Aharon M, Elad M, Bruckstein A 2006. K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Signal Process. 54:4311–22
    [Google Scholar]
  56. 56. 
    Engan K, Aase S, Husoy J 1999. Frame based signal compression using method of optimal directions (MOD). Proceedings of the 1999 IEEE International Symposium on Circuits and Systems (ISCAS) 41–4 Piscataway, NJ: IEEE
    [Google Scholar]
  57. 57. 
    Mairal J, Bach F, Ponce J, Sapiro G 2009. Online dictionary learning for sparse coding. Proceedings of the 26th International Conference on Machine Learning689–96 New York: ACM
    [Google Scholar]
  58. 58. 
    Tropp JA, Gilbert AC 2007. Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans. Inf. Theory 53:4655–66
    [Google Scholar]
  59. 59. 
    Mairal J, Bach F, Ponce J, Sapiro G, Zisserman A 2008. Discriminative learned dictionaries for local image analysis. Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition1–8 Piscataway, NJ: IEEE
    [Google Scholar]
  60. 60. 
    Glorot X, Bordes A, Bengio Y 2011. Deep sparse rectifier neural networks. J. Mach. Learn. Res. 15:315–23
    [Google Scholar]
  61. 61. 
    Narang S, Diamos GF, Sengupta S, Elsen E 2017. Exploring sparsity in recurrent neural networks. arXiv:1704.05119 [cs.LG]
    [Google Scholar]
  62. 62. 
    Shi S, Chu X 2017. Speeding up convolutional neural networks by exploiting the sparsity of rectifier units. arXiv:1704.07724 [cs.CV]
    [Google Scholar]
  63. 63. 
    Ng A 2011.Sparse autoencoder. CS294A lecture notes, Stanford Univ., Stanford, CA. https://web.stanford.edu/class/cs294a/sparseAutoencoder_2011new.pdf
  64. 64. 
    Le QV, Coates A, Prochnow B, Ng AY 2011. On optimization methods for deep learning. J. Mach. Learn. Res. 15:265–72
    [Google Scholar]
  65. 65. 
    Scardapane S, Comminiello D, Hussain A, Uncini A 2017. Group sparse regularization for deep neural networks. Neurocomputing 241:81–89
    [Google Scholar]
  66. 66. 
    Xu J, Xiang L, Liu Q, Gilmore H, Wu J et al. 2015. Stacked sparse autoencoder (SSAE) for nuclei detection on breast cancer histopathology images. IEEE Trans. Med. Imaging 35:119–30
    [Google Scholar]
  67. 67. 
    Liu B, Wang M, Foroosh H, Tappen M, Penksy M 2015. Sparse convolutional neural networks. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition806–14 Piscataway, NJ: IEEE
    [Google Scholar]
  68. 68. 
    Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R 2014. Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15:1929–58
    [Google Scholar]
  69. 69. 
    Papyan V, Romano Y, Elad M 2017. Convolutional neural networks analyzed via convolutional sparse coding. J. Mach. Learn. Res. 18:1–52
    [Google Scholar]
  70. 70. 
    Poultney C, Chopra S, LeCun Y 2007. Efficient learning of sparse representations with an energy-based model. Proceedings of Advances in Neural Information Processing Systems 20 (NIPS 2007) JC Platt, D Koller, Y Singer, ST Roweis 1137–44 Cambridge, MA: MIT Press
    [Google Scholar]
  71. 71. 
    Ben-Cohen A, Klang E, Kerpel A, Konen E, Amitai MM, Greenspan H 2018. Fully convolutional network and sparsity-based dictionary learning for liver lesion detection in CT examinations. Neurocomputing 275:1585–94
    [Google Scholar]
  72. 72. 
    Huang X, Lin BA, Compas CB, Sinusas AJ, Staib LH, Duncan JS 2012. Segmentation of left ventricles from echocardiographic sequences via sparse appearance representation. Proceedings of the 2012 IEEE Workshop on Mathematical Methods in Biomedical Image Analysis305–12 Piscataway, NJ: IEEE
    [Google Scholar]
  73. 73. 
    Huang X, Dione DP, Compas CB, Papademetris X, Lin BAet al 2012. A dynamical appearance model based on multiscale sparse representation: segmentation of the left ventricle from 4D echocardiography. Proceedings of the 15th Annual Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2012)58–65 Berlin: Springer
    [Google Scholar]
  74. 74. 
    Huang X, Dione DP, Lin BA, Bregasi A, Sinusas AJ, Duncan JS 2013. Segmentation of 4D echocardiography using stochastic online dictionary learning. Proceedings of the 16th Annual Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2013)57–65 Berlin: Springer
    [Google Scholar]
  75. 75. 
    Onofrey JA, Staib LH, Papademetris X 2018. Segmenting the brain surface from CT images with artifacts using locally-oriented appearance and dictionary learning. IEEE Trans. Med. Imaging 38:596–607
    [Google Scholar]
  76. 76. 
    Tong T, Wolz R, Coupe P, Hijnal J, Rueckert D et al. 2013. Segmentation of MR images via discriminative dictionary learning and sparse coding: application to hippocampus labeling. NeuroImage 76:11–23
    [Google Scholar]
  77. 77. 
    Roy S, He Q, Sweeney E, Carass A, Reich D et al. 2015. Subject-specific sparse dictionary learning for atlas-based brain MRI segmentation. IEEE J. Biomed. Health Inform. 19:1598–609
    [Google Scholar]
  78. 78. 
    Zhang F, Yang J, Nezami N, Laage-gaupp F, Chapiro Jet al 2018. Liver tissue classification using an auto-context-based deep neural network with a multi-phase training framework. Proceedings of the 4th International Workshop on Patch-Based Techniques in Medical Imaging59–66 Berlin: Springer
    [Google Scholar]
  79. 79. 
    Amini A, Duncan J 1991. Pointwise tracking of left-ventricular motion. Proc. IEEE Workshop Vis. Motion 1:294–99
    [Google Scholar]
  80. 80. 
    McEachen JC, Nehorai A, Duncan JS 2000. Multiframe temporal estimation of cardiac nonrigid motion. IEEE Trans. Image Proc. 9:651–65
    [Google Scholar]
  81. 81. 
    Compas C, Wong E, Huang X, Sampath S, Pal P et al. 2014. Radial basis functions for combining shape and speckle tracking in 4D echocardiography. IEEE Trans. Med. Imaging 33:1275–89
    [Google Scholar]
  82. 82. 
    Mairal J, Sapiro G, Elad M 2008. Learning multiscale sparse representations for image and video restoration. Multiscale Model. Simul. 7:214–41
    [Google Scholar]
  83. 83. 
    Freund Y, Schapire R 1997. A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55:119–39
    [Google Scholar]
  84. 84. 
    Sarti A, Corsi C, Mazzini E, Lamberti C 2005. Maximum likelihood segmentation of ultrasound images with Rayleigh distribution. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 52:947–60
    [Google Scholar]
  85. 85. 
    Zhu Y, Papademetris X, Sinusas A, Duncan J 2010. Segmentation of the left ventricle from cardiac MR images using a subject-specific dynamical model. IEEE Trans. Med. Imaging 29:669–87
    [Google Scholar]
  86. 86. 
    Studholme C, Novotny E, Zubal I, Duncan J 2001. Estimating tissue deformation between functional images induced by intracranial electrode implantation using anatomical MRI. NeuroImage 13:561–76
    [Google Scholar]
  87. 87. 
    krinjar O, Spencer D, Duncan J 1998. Brain shift modeling for use in neurosurgery. Proceedings of the 1st International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 1998)641–49 Berlin: Springer
    [Google Scholar]
  88. 88. 
    krinjar O, Duncan J 1999. Real time 3D brain shift compensation. Proceedings of the Biennial International Conference on Information Processing in Medical Imaging42–55 Berlin: Springer
    [Google Scholar]
  89. 89. 
    krinjar O, Nabavi A, Duncan J 2002. Model-driven brain shift compensation. Med. Image Anal. 6:361–73
    [Google Scholar]
  90. 90. 
    Chui H, Rangarajan A 2003. A new point matching algorithm for non-rigid registration. Comput. Vis. Image Underst. 89:114–41
    [Google Scholar]
  91. 91. 
    Myronenko A, Song X 2010. Point set registration: coherent point drift. IEEE Trans. Pattern Anal. Mach. Intell. 32:2262–75
    [Google Scholar]
  92. 92. 
    Smith SM 2002. Fast robust automated brain extraction. Hum. Brain Mapp. 17:143–55
    [Google Scholar]
  93. 93. 
    Zhang Q, Li B 2010. Discriminative K-SVD for dictionary learning in face recognition. Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition2691–98 Piscataway, NJ: IEEE
    [Google Scholar]
  94. 94. 
    Jack CR, Bernstein MA, Fox NC, Thompson P, Alexander G et al. 2008. The Alzheimer's Disease Neuroimaging Initiative (ADNI): MRI methods. J. Magn. Reson. Imaging 27:685–91
    [Google Scholar]
  95. 95. 
    Mazziotta J, Toga A, Evans A, Fox P, Lancaster J et al. 2001. A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM). Philos. Trans. R. Soc. B 356:1293–322
    [Google Scholar]
  96. 96. 
    Coupe P, Manjn J, Fonov V, Pruessner J, Robles M, Collins D 2011. Patch-based segmentation using expert priors: application to hippocampus and ventricle segmentation. NeuroImage 54:940–54
    [Google Scholar]
  97. 97. 
    Nyul LG, Udupa JK 1999. On standardizing the MR image intensity scale. Magn. Reson. Med. 42:1072–81
    [Google Scholar]
  98. 98. 
    Zhang S, Zhan Y, Dewan M, Huang J, Metaxas D 2012. Towards robust and effective shape modeling: sparse shape composition. Med. Image Anal. 16:265–77
    [Google Scholar]
  99. 99. 
    Wang G, Zhang S, Xie H, Metaxas D, Gu L 2015. A homotopy-based sparse representation for fast and accurate shape prior modeling in liver surgical planning. Med. Image Anal. 19:176–86
    [Google Scholar]
  100. 100. 
    Shen D, Davatzikos C 2000. An adaptive-focus deformable model using statistical and geometric information. IEEE Trans. Pattern Anal. Mach. Intell. 22:906–13
    [Google Scholar]
  101. 101. 
    Zhan Y, Shen D 2006. Deformable segmentation of 3D ultrasound prostate images using statistical texture matching method. IEEE Trans. Med. Imaging 25:256–72
    [Google Scholar]
  102. 102. 
    Bookstein F 1989. Principal warps: thin-plate splines and the decomposition of deformations. IEEE Trans. Pattern Anal. Mach. Intell. 11:567–85
    [Google Scholar]
  103. 103. 
    Ronneberger O, Fischer P, Brox T 2015. U-net: convolutional networks for biomedical image segmentation. Proceedings of the 18th Annual Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2015)234–41 Berlin: Springer
    [Google Scholar]
  104. 104. 
    Raza A, Sood GK 2014. Hepatocellular carcinoma review: current treatment, and evidence-based medicine. World J. Gastroenterol. 20:4115–27
    [Google Scholar]
  105. 105. 
    Raoul JL, Sangro B, Forner A, Mazzaferro V, Piscaglia F et al. 2011. Evolving strategies for the management of intermediate-stage hepatocellular carcinoma: available evidence and expert opinion on the use of transarterial chemoembolization. Cancer Treat. Rev. 37:212–20
    [Google Scholar]
  106. 106. 
    Tu Z, Bai X 2010. Auto-context and its application to high-level vision tasks and 3D brain image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 32:1744–57
    [Google Scholar]
  107. 107. 
    Treilhard J, Smolka S, Staib L, Chapiro J, Lin Met al 2017. Liver tissue classification in patients with hepatocellular carcinoma by fusing structured and rotationally invariant context representation. Proceedings of the 20th Annual Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2017)81–88 Berlin: Springer
    [Google Scholar]
  108. 108. 
    Christ PF, Elshaer MEA, Ettlinger F, Tatavarty S, Bickel Met al 2016. Automatic liver and lesion segmentation in CT using cascaded fully convolutional neural networks and 3D conditional random fields. Proceedings of the 19th Annual Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2016)415–23 Berlin: Springer
    [Google Scholar]
  109. 109. 
    Li W, Jia F, Hu Q 2015. Automatic segmentation of liver tumor in CT images with deep convolutional neural networks. J. Comput. Commun. 3:146–51
    [Google Scholar]
  110. 110. 
    Papademetris X, Jackowski MP, Rajeevan N, DiStasio M, Okuda H et al. 2006. BioImage Suite: an integrated medical image analysis suite: an update. Insight J. 2006:209
    [Google Scholar]
/content/journals/10.1146/annurev-bioeng-060418-052147
Loading
/content/journals/10.1146/annurev-bioeng-060418-052147
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error