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Using deep learning techniques in medical imaging: a systematic review of applications on CT and PET

  • Inês Domingues
  • Gisèle Pereira
  • Pedro Martins
  • Hugo Duarte
  • João Santos
  • Pedro Henriques AbreuEmail author
Article
  • 103 Downloads

Abstract

Medical imaging is a rich source of invaluable information necessary for clinical judgements. However, the analysis of those exams is not a trivial assignment. In recent times, the use of deep learning (DL) techniques, supervised or unsupervised, has been empowered and it is one of the current research key areas in medical image analysis. This paper presents a survey of the use of DL architectures in computer-assisted imaging contexts, attending two different image modalities: the actively studied computed tomography and the under-studied positron emission tomography, as well as the combination of both modalities, which has been an important landmark in several decisions related to numerous diseases. In the making of this review, we analysed over 180 relevant studies, published between 2014 and 2019, that are sectioned by the purpose of the research and the imaging modality type. We conclude by addressing research issues and suggesting future directions for further improvement. To our best knowledge, there is no previous work making a review of this issue.

Keywords

Deep learning Computed tomography Positron emission tomography Medical imaging 

Notes

Acknowledgements

This article is a result of the project NORTE-01-0145-FEDER-000027, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF).

References

  1. Afshari S, BenTaieb A, Hamarneh G (2018) Automatic localization of normal active organs in 3D PET scans. Comput Med Imaging Graph 70:111–118.  https://doi.org/10.1016/j.compmedimag.2018.09.008 CrossRefGoogle Scholar
  2. Ait Skourt B, El Hassani A, Majda A (2018) Lung CT image segmentation using deep neural networks. Procedia Comput Sci 127:109–113.  https://doi.org/10.1016/j.procs.2018.01.104 CrossRefGoogle Scholar
  3. Ali M, Son DH, Kang SH, Nam SR (2017) An accurate CT saturation classification using a deep learning approach based on unsupervised feature extraction and supervised fine-tuning strategy. Energies 10(11):1830.  https://doi.org/10.3390/en10111830 CrossRefGoogle Scholar
  4. Antonio CB, Bautista LGC, Labao AB, Naval PC (2018) Vertebra fracture classification from 3D CT lumbar spine segmentation masks using a convolutional neural network. In: Asian conference on intelligent information and database systems. Lecture notes in artificial intelligence, vol 10752. Springer, pp 449–458. https://doi.org/10.1007/978-3-319-75420-8_43
  5. Asadi S, Abdullah R, Yah Y, Nazir S (2019) Understanding institutional repository in higher learning institutions: a systematic literature review and directions for future research. IEEE Access 7:35242–35263.  https://doi.org/10.1109/ACCESS.2019.2897729 CrossRefGoogle Scholar
  6. Barbu A, Lu L, Roth H, Seff A, Summers RM (2018) An analysis of robust cost functions for CNN in computer-aided diagnosis. Comput Methods Biomech Biomed Eng Imaging Vis 6(3):253–258.  https://doi.org/10.1080/21681163.2016.1138240 CrossRefGoogle Scholar
  7. Belharbi S, Chatelain C, Hérault R, Adam S, Thureau S, Chastan M, Modzelewski R (2017) Spotting L3 slice in CT scans using deep convolutional network and transfer learning. Comput Biol Med 87(May):95–103.  https://doi.org/10.1016/j.compbiomed.2017.05.018 CrossRefGoogle Scholar
  8. Ben-Cohen A, Klang E, Raskin SP, Amitai MM, Greenspan H (2017) Virtual PET images from CT data using deep convolutional networks: initial results. In: Lecture notes in computer science, vol 10557. Springer, pp 49–57. https://doi.org/10.1007/978-3-319-68127-6_6
  9. Ben-Cohen A, Klang E, Raskin SP, Soffer S, Ben-Haim S, Konen E, Amitai MM, Greenspan H (2019) Cross-modality synthesis from CT to PET using FCN and GAN networks for improved automated lesion detection. Eng Appl Artif Intell 78:186–194.  https://doi.org/10.1016/j.engappai.2018.11.013 CrossRefGoogle Scholar
  10. Bengio Y, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35(8):1798–1828.  https://doi.org/10.1109/TPAMI.2013.50 CrossRefGoogle Scholar
  11. Berg E, Cherry SR (2018) Using convolutional neural networks to estimate time-of-flight from PET detector waveforms. Phys Med Biol 63(2):02LT01.  https://doi.org/10.1088/1361-6560/aa9dc5 CrossRefGoogle Scholar
  12. Bessa S, Domingues I, Cardosos JS, Passarinho P, Cardoso P, Rodrigues V, Lage F (2014) Normal breast identification in screening mammography: a study on 18000 images. In: IEEE international conference on bioinformatics and biomedicine (BIBM), pp 325–330Google Scholar
  13. Bhatt J, Joshi M, Sharma M (2018) Early detection of lung cancer from CT images: nodule segmentation and classification using deep learning. In: Tenth international conference on machine vision (ICMV). SPIE, p 29. https://doi.org/10.1117/12.2309530
  14. Bi L, Kim J, Kumar A, Feng D, Fulham M (2017a) Synthesis of positron emission tomography (PET) images via multi-channel generative adversarial networks (GANs). In: Lecture notes in computer science, vol 10555. Springer, Cham, pp 43–51. https://doi.org/10.1007/978-3-319-67564-0_5
  15. Bi L, Kim J, Kumar A, Wen L, Feng D, Fulham M (2017b) Automatic detection and classification of regions of FDG uptake in whole-body PET-CT lymphoma studies. Comput Med Imaging Graph 60:3–10.  https://doi.org/10.1016/j.compmedimag.2016.11.008 CrossRefGoogle Scholar
  16. Bibault JE, Giraud P, Housset M, Durdux C, Taieb J, Berger A, Coriat R, Chaussade S, Dousset B, Nordlinger B, Burgun A (2018) Deep learning and radiomics predict complete response after neo-adjuvant chemoradiation for locally advanced rectal cancer. Sci Rep 8(1):12611.  https://doi.org/10.1038/s41598-018-30657-6 CrossRefGoogle Scholar
  17. Blanc-Durand P, Van Der Gucht A, Schaefer N, Itti E, Prior JO (2018) Automatic lesion detection and segmentation of 18F-FET PET in gliomas: a full 3D U-Net convolutional neural network study. PLoS ONE 13(4):e0195798.  https://doi.org/10.1371/journal.pone.0195798 CrossRefGoogle Scholar
  18. Blau N, Klang E, Kiryati N, Amitai M, Portnoy O, Mayer A (2018) Fully automatic detection of renal cysts in abdominal CT scans. Int J Comput Assist Radiol Surg 13(7):957–966.  https://doi.org/10.1007/s11548-018-1726-6 CrossRefGoogle Scholar
  19. Boas FE, Fleischmann D (2012) CT artifacts: causes and reduction techniques. Imaging Med 4(2):229–240.  https://doi.org/10.2217/iim.12.13 CrossRefGoogle Scholar
  20. Brenner DJ, Hricak H (2010) Radiation exposure from medical imaging. JAMA 304(2):208.  https://doi.org/10.1001/jama.2010.973 CrossRefGoogle Scholar
  21. Bushberg JT, Boone JM (2011) The essential physics of medical imaging. Lippincott Williams & Wilkins, PhiladelphiaGoogle Scholar
  22. Cha KH, Hadjiiski L, Samala RK, Chan HP, Caoili EM, Cohan RH (2016) Urinary bladder segmentation in CT urography using deep-learning convolutional neural network and level sets. Med Phys 43(4):1882–1896.  https://doi.org/10.1118/1.4944498 CrossRefGoogle Scholar
  23. Cha KH, Hadjiiski L, Chan HP, Weizer AZ, Alva A, Cohan RH, Caoili EM, Paramagul C, Samala RK (2017a) Bladder cancer treatment response assessment in CT using radiomics with deep-learning. Sci Rep 7(1):8738.  https://doi.org/10.1038/s41598-017-09315-w CrossRefGoogle Scholar
  24. Cha KH, Hadjiiski LM, Chan HP, Caoili EM, Cohan RH, Weizer A, Samala RK (2017b) Computer-aided detection of bladder masses in CT urography (CTU). In: Medical imaging: computer-aided diagnosis, SPIE, vol 10134, p 1013403. https://doi.org/10.1117/12.2255668
  25. Chen CM, Chou YH, Tagawa N, Do Y (2013) Computer-aided detection and diagnosis in medical imaging. Comput Math Methods Med 2013:1–2.  https://doi.org/10.1155/2013/790608 CrossRefGoogle Scholar
  26. Chen YJ, Hua KL, Hsu CH, Cheng WH, Hidayati SC (2015) Computer-aided classification of lung nodules on computed tomography images via deep learning technique. Onco Targets Therapy.  https://doi.org/10.2147/OTT.S80733 CrossRefGoogle Scholar
  27. Chen H, Zhang Y, Kalra MK, Lin F, Chen Y, Liao P, Zhou J, Wang G (2017a) Low-dose CT with a residual encoder–decoder convolutional neural network. IEEE Trans Med Imaging 36(12):2524–2535.  https://doi.org/10.1109/TMI.2017.2715284 CrossRefGoogle Scholar
  28. Chen H, Zhang Y, Zhang W, Liao P, Li K, Zhou J, Wang G (2017b) Low-dose CT via convolutional neural network. Biomed Opt Express 8(2):679.  https://doi.org/10.1364/BOE.8.000679 CrossRefGoogle Scholar
  29. Chen Y, Ren Y, Fu L, Xiong J, Larsson R, Xu X, Sun J, Zhao J (2018) A 3D convolutional neural network framework for polyp candidates detection on the limited dataset of CT colonography. In: 40th Annual international conference of the IEEE engineering in medicine and biology society (EMBC), pp 678–681. https://doi.org/10.1109/EMBC.2018.8512305
  30. Cheng D, Liu M (2017) Combining convolutional and recurrent neural networks for Alzheimer’s disease diagnosis using PET images. In: IEEE international conference on imaging systems and techniques (IST), pp 1–5. https://doi.org/10.1109/IST.2017.8261461
  31. Cheng JZ, Ni D, Chou YH, Qin J, Tiu CM, Chang YC, Huang CS, Shen D, Chen CM (2016) Computer-aided diagnosis with deep learning architecture: applications to breast lesions in US images and pulmonary nodules in CT scans. Sci Rep 6(1):24454.  https://doi.org/10.1038/srep24454 CrossRefGoogle Scholar
  32. Chmelik J, Jakubicek R, Walek P, Jan J, Ourednicek P, Lambert L, Amadori E, Gavelli G (2018) Deep convolutional neural network-based segmentation and classification of difficult to define metastatic spinal lesions in 3D CT data. Med Image Anal 49:76–88.  https://doi.org/10.1016/j.media.2018.07.008 CrossRefGoogle Scholar
  33. Chmelik J, Jakubicek R, Jan J, Ourednicek P, Lambert L, Amadori E, Gavelli G (2019) Fully automatic CAD system for segmentation and classification of spinal metastatic lesions in CT data. In: World congress on medical physics and biomedical engineering 2018, vol 68. Springer, pp 155–158. https://doi.org/10.1007/978-981-10-9035-6_28
  34. Choi H, Jin KH (2018) Predicting cognitive decline with deep learning of brain metabolism and amyloid imaging. Behav Brain Res 344:103–109.  https://doi.org/10.1016/j.bbr.2018.02.017 CrossRefGoogle Scholar
  35. Christ PF, Elshaer MEA, Ettlinger F, Tatavarty S, Bickel M, Bilic P, Rempfler M, Armbruster M, Hofmann F, D’Anastasi M, Sommer WH, Ahmadi SA, Menze BH (2016) Automatic liver and lesion segmentation in ct using cascaded fully convolutional neural networks and 3D conditional random fields. In: International conference on medical image computing and computer-assisted intervention (MICCAI). Springer, pp 415–423Google Scholar
  36. Çiçek Ö, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O (2016) 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: International conference on medical image computing and computer-assisted intervention (MICCAI). Springer, pp 424–432. https://doi.org/10.1007/978-3-319-46723-8_49
  37. Ciompi F, de Hoop B, van Riel SJ, Chung K, Scholten ET, Oudkerk M, de Jong PA, Prokop M, van Ginneken B (2015) Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box. Med Image Anal 26(1):195–202.  https://doi.org/10.1016/j.media.2015.08.001 CrossRefGoogle Scholar
  38. Costello JE, Cecava ND, Tucker JE, Bau JL (2013) CT radiation dose: current controversies and dose reduction strategies. Am J Roentgenol 201(6):1283–1290.  https://doi.org/10.2214/AJR.12.9720 CrossRefGoogle Scholar
  39. Cui J, Liu X, Wang Y, Liu H (2017) Deep reconstruction model for dynamic PET images. PLoS ONE 12(9):e0184667.  https://doi.org/10.1371/journal.pone.0184667 CrossRefGoogle Scholar
  40. Czakon J, Drapejkowski F, Zurek G, Giedziun P, Zebrowski J, Dyrka W (2016) Machine learning methods for accurate delineation of tumors in PET images. CoRR arXiv:1610.09493
  41. Da C, Zhang H, Sang Y (2015) Brain CT image classification with deep neural networks. In: 18th Asia pacific symposium on intelligent and evolutionary systems, vol 1, pp 653–662. https://doi.org/10.1007/978-3-319-13359-1_50
  42. Das A, Acharya UR, Panda SS, Sabut S (2019) Deep learning based liver cancer detection using watershed transform and Gaussian mixture model techniques. Cognit Syst Res 54:165–175.  https://doi.org/10.1016/j.cogsys.2018.12.009 CrossRefGoogle Scholar
  43. de Carvalho Filho AO, Silva AC, de Paiva AC, Nunes RA, Gattass M (2018) Classification of patterns of benignity and malignancy based on CT using topology-based phylogenetic diversity index and convolutional neural network. Pattern Recognit 81:200–212.  https://doi.org/10.1016/j.patcog.2018.03.032 CrossRefGoogle Scholar
  44. de Vos BD, Wolterink JM, de Jong PA, Leiner T, Viergever MA, Isgum I (2017) ConvNet-based localization of anatomical structures in 3-D medical images. IEEE Trans Med Imaging 36(7):1470–1481.  https://doi.org/10.1109/TMI.2017.2673121 CrossRefGoogle Scholar
  45. Deng CL, Jiang HY, Li HM (2017) Automated high uptake regions recognition and lymphoma detection based on fully convolutional networks on chest and abdomen PET image. In: International conference on medicine sciences and bioengineering (ICMSB), pp 331–336. https://doi.org/10.12783/dtbh/icmsb2017/17985
  46. Diniz JOB, Diniz PHB, Valente TLA, Silva AC, Paiva AC (2019) Spinal cord detection in planning CT for radiotherapy through adaptive template matching, IMSLIC and convolutional neural networks. Comput Methods Programs Biomed 170:53–67.  https://doi.org/10.1016/j.cmpb.2019.01.005 CrossRefGoogle Scholar
  47. Doersch C (2016) Tutorial on variational autoencoders. CoRR arXiv:1606.05908
  48. Domingues III, Cardoso JS (2014) Max-ordinal learning. IEEE Trans Neural Netw Learn Syst 25(7):1384–1389.  https://doi.org/10.1109/TNNLS.2013.2287381 CrossRefGoogle Scholar
  49. Domingues I, Abreu PH, Santos J (2018) Bi-rads classification of breast cancer: a new pre-processing pipeline for deep models training. In: 25th IEEE international conference on image processing (ICIP), pp 1378–1382. https://doi.org/10.1109/ICIP.2018.8451510
  50. Domingues I, Sampaio I, Duarte H, dos Santos JAM, Abreu PH (2019) Computer vision in esophageal cancer: a literature review. IEEE Access.  https://doi.org/10.1109/ACCESS.2019.2930891 CrossRefGoogle Scholar
  51. Domingues I, Cardoso JS (2013) Mass detection on mammogram images: a first assessment of deep learning techniques. In: 19th Portuguese conference on pattern recognitionGoogle Scholar
  52. Dormer JD, Halicek M, Ma L, Reilly CM, Fei B, Schreibmann E (2018) Convolutional neural networks for the detection of diseased hearts using CT images and left atrium patches. In: Medical imaging: computer-aided diagnosis, SPIE, p 107. https://doi.org/10.1117/12.2293548
  53. Dou Q, Chen H, Jin Y, Yu L, Qin J, Heng PA (2016) 3D deeply supervised network for automatic liver segmentation from CT volumes. In: International conference on medical image computing and computer-assisted intervention (MICCAI). Springer, pp 149–157Google Scholar
  54. Dou Q, Yu L, Chen H, Jin Y, Yang X, Qin J, Heng PA (2017) 3D deeply supervised network for automated segmentation of volumetric medical images. Med Image Anal 41:40–54.  https://doi.org/10.1016/j.media.2017.05.001 CrossRefGoogle Scholar
  55. Erhan D, Manzagol PA, Bengio Y, Bengio S, Vincent P (2009) The difficulty of training deep architectures and the effect of unsupervised pre-training. In: Proceedings of the international conference on artificial intelligence and statistics, vol 5, pp 153–160Google Scholar
  56. Erhan D, Bengio Y, Courville A, Manzagol PA, Vincent P, Bengio S (2010) Why does unsupervised pre-training help deep learning? J Mach Learn Res 11:625–660MathSciNetzbMATHGoogle Scholar
  57. Erickson ZA, Galimzianova A, Hoogi A, Rubin DL, Erickson BJ (2017) Deep learning for brain MRI segmentation: state of the art and future directions. J Digit Imaging 30(4):449–459.  https://doi.org/10.1007/s10278-017-9983-4 CrossRefGoogle Scholar
  58. Farag A, Lu L, Roth HR, Liu J, Turkbey E, Summers RM (2017) A bottom-up approach for pancreas segmentation using cascaded superpixels and (deep) image patch labeling. IEEE Trans Image Process 26(1):386–399.  https://doi.org/10.1109/TIP.2016.2624198 MathSciNetCrossRefzbMATHGoogle Scholar
  59. Fechter T, Adebahr S, Baltas D, Ben Ayed I, Desrosiers C, Dolz J (2017) Esophagus segmentation in CT via 3D fully convolutional neural network and random walk. Med Phys 44(12):6341–6352.  https://doi.org/10.1002/mp.12593 CrossRefGoogle Scholar
  60. Feng A, Chen Z, Wu X, Ma Z (2017) From convolutional to recurrent: case study in Nasopharyngeal Carcinoma segmentation. In: International conference on the frontiers and advances in data science (FADS). IEEE, pp 18–22. https://doi.org/10.1109/FADS.2017.8253187
  61. Fischer A, Igel C (2014) Training restricted Boltzmann machines: an introduction. Pattern Recognit 47(1):25–39.  https://doi.org/10.1016/j.patcog.2013.05.025 CrossRefzbMATHGoogle Scholar
  62. Fukushima K, Miyake S (1982) Neocognitron: a self-organizing neural network model for a mechanism of visual pattern recognition. In: Competition and cooperation in neural nets. Springer, pp 267–285. https://doi.org/10.1007/978-3-642-46466-9_18
  63. Gao XW, Hui R (2016) A deep learning based approach to classification of CT brain images. In: IEEE SAI computing conference (SAI), pp 28–31. https://doi.org/10.1109/SAI.2016.7555958
  64. Gao M, Xu Z, Lu L, Wu A, Nogues I, Summers RM, Mollura DJ (2016) Segmentation label propagation using deep convolutional neural networks and dense conditional random field. In: IEEE 13th international symposium on biomedical imaging (ISBI), pp 1265–1268. https://doi.org/10.1109/ISBI.2016.7493497
  65. Gao XW, Hui R, Tian Z (2017) Classification of CT brain images based on deep learning networks. Comput Methods Programs Biomed 138:49–56.  https://doi.org/10.1016/j.cmpb.2016.10.007 CrossRefGoogle Scholar
  66. Gao M, Bagci U, Lu L, Wu A, Buty M, Shin HC, Roth H, Papadakis GZ, Depeursinge A, Summers RM, Xu Z, Mollura DJ (2018) Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks. Comput Methods Biomech Biomed Eng Imaging Vis 6(1):1–6.  https://doi.org/10.1080/21681163.2015.1124249 CrossRefGoogle Scholar
  67. Gerard SE, Patton TJ, Christensen GE, Bayouth JE, Reinhardt JM (2019) FissureNet: a deep learning approach for pulmonary fissure detection in CT images. IEEE Trans Med Imaging 38(1):156–166.  https://doi.org/10.1109/TMI.2018.2858202 CrossRefGoogle Scholar
  68. Ghesu FC, Georgescu B, Zheng Y, Grbic S, Maier A, Hornegger J, Comaniciu D (2019) Multi-scale deep reinforcement learning for real-time 3D-landmark detection in CT scans. IEEE Trans Pattern Anal Mach Intell 41(1):176–189.  https://doi.org/10.1109/TPAMI.2017.2782687 CrossRefGoogle Scholar
  69. Gibson E, Giganti F, Hu Y, Bonmati E, Bandula S, Gurusamy K, Davidson B, Pereira SP, Clarkson MJ, Barratt DC (2018) Automatic multi-organ segmentation on abdominal CT with dense V-networks. IEEE Trans Med Imaging 37(8):1822–1834.  https://doi.org/10.1109/TMI.2018.2806309 CrossRefGoogle Scholar
  70. Golan R, Jacob C, Denzinger J (2016) Lung nodule detection in CT images using deep convolutional neural networks. In: IEEE international joint conference on neural networks (IJCNN), pp 243–250. https://doi.org/10.1109/IJCNN.2016.7727205
  71. Gong K, Catana C, Qi J, Li Q (2018) PET image reconstruction using deep image prior. IEEE Trans Med Imaging.  https://doi.org/10.1109/TMI.2018.2888491 CrossRefGoogle Scholar
  72. Gong K, Guan J, Kim K, Zhang X, Yang J, Seo Y, El Fakhri G, Qi J, Li Q (2019) Iterative PET image reconstruction using convolutional neural network representation. IEEE Trans Med Imaging 38(3):675–685.  https://doi.org/10.1109/TMI.2018.2869871 CrossRefGoogle Scholar
  73. Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: Advances in neural information processing systems 27. Curran Associates Inc., pp 2672–2680Google Scholar
  74. Gordon M, Hadjiiski L, Cha K, Chan HP, Samala R, Cohan RH, Caoili EM (2017) Segmentation of inner and outer bladder wall using deep-learning convolutional neural network in CT urography. In: Medical imaging: computer-aided diagnosis, SPIE, vol 10134. https://doi.org/10.1117/12.2255528
  75. Greenspan H, van Ginneken B, Summers RM (2016) Guest editorial deep learning in medical imaging: overview and future promise of an exciting new technique. IEEE Trans Med Imaging 35(5):1153–1159.  https://doi.org/10.1109/TMI.2016.2553401 CrossRefGoogle Scholar
  76. Grewal M, Srivastava MM, Kumar P, Varadarajan S (2018) RADnet: radiologist level accuracy using deep learning for hemorrhage detection in CT scans. In: IEEE 15th international symposium on biomedical imaging (ISBI), pp 281–284. https://doi.org/10.1109/ISBI.2018.8363574
  77. Gu J, Wang Z, Kuen J, Ma L, Shahroudy A, Shuai B, Liu T, Wang X, Wang G, Cai J, Chen T (2018) Recent advances in convolutional neural networks. Pattern Recognit 77:354–377.  https://doi.org/10.1016/j.patcog.2017.10.013 CrossRefGoogle Scholar
  78. Guo Z, Li X, Huang H, Guo N, Li Q (2018) Medical image segmentation based on multi-modal convolutional neural network: study on image fusion schemes. In: IEEE 15th international symposium on biomedical imaging (ISBI), pp 903–907. https://doi.org/10.1109/ISBI.2018.8363717
  79. Hamidian S, Sahiner B, Petrick N, Pezeshk A (2017) 3D convolutional neural network for automatic detection of lung nodules in chest CT. In: Medical imaging: computer-aided diagnosis, SPIE, vol 10134, p 1013409. https://doi.org/10.1117/12.2255795
  80. Hashimoto N, Suzuki K, Liu J, Hirano Y, MacMahon H, Kido S (2018) Deep neural network convolution (NNC) for three-class classification of diffuse lung disease opacities in high-resolution CT (HRCT): consolidation, ground-glass opacity (GGO), and normal opacity. In: Medical imaging: computer-aided diagnosis, SPIE, p 113. https://doi.org/10.1117/12.2293550
  81. Hastie T, Friedman J, Tibshirani R (2001) The elements of statistical learning, 2nd edn. Springer, New York.  https://doi.org/10.1007/978-0-387-21606-5 CrossRefzbMATHGoogle Scholar
  82. Hatt M, Laurent B, Ouahabi A, Fayad H, Tan S, Li L, Lu W, Jaouen V, Tauber C, Czakon J, Drapejkowski F, Dyrka W, Camarasu-Pop S, Cervenansky F, Girard P, Glatard T, Kain M, Yao Y, Barillot C, Kirov A, Visvikis D (2018) The first MICCAI challenge on PET tumor segmentation. Med Image Anal 44:177–195.  https://doi.org/10.1016/j.media.2017.12.007 CrossRefGoogle Scholar
  83. He J, Yang Y, Wang Y, Zeng D, Bian Z, Zhang H, Sun J, Xu Z, Ma J (2019) Optimizing a parameterized plug-and-play ADMM for iterative low-dose CT reconstruction. IEEE Trans Med Imaging 38(2):371–382.  https://doi.org/10.1109/TMI.2018.2865202 CrossRefGoogle Scholar
  84. Hong HA, Sheikh UU (2016) Automatic detection, segmentation and classification of abdominal aortic aneurysm using deep learning. In: IEEE 12th international colloquium on signal processing and its applications (CSPA), pp 242–246. https://doi.org/10.1109/CSPA.2016.7515839
  85. Huang L, Xia W, Zhang B, Qiu B, Gao X (2017) MSFCN-multiple supervised fully convolutional networks for the osteosarcoma segmentation of CT images. Comput Methods Programs Biomed 143:67–74.  https://doi.org/10.1016/j.cmpb.2017.02.013 CrossRefGoogle Scholar
  86. Huang B, Chen Z, Wu PM, Ye Y, Feng ST, Wong CYO, Zheng L, Liu Y, Wang T, Li Q, Huang B (2018) Fully automated delineation of gross tumor volume for head and neck cancer on PET-CT using deep learning: a dual-center study. Contrast Media Mol Imaging.  https://doi.org/10.1155/2018/8923028 CrossRefGoogle Scholar
  87. Humpire Mamani GE, Setio AAA, van Ginneken B, Jacobs C (2017) Organ detection in thorax abdomen CT using multi-label convolutional neural networks. In: Medical imaging: computer-aided diagnosis, SPIE, vol 10134, p 1013416. https://doi.org/10.1117/12.2254349
  88. Hu Z, Muhammad A, Zhu M (2018) Pulmonary nodule detection in CT images via deep neural network. In: 2nd International conference on graphics and signal processing (ICGSP). ACM Press, pp 79–83. https://doi.org/10.1145/3282286.3282302
  89. Hussein S, Cao K, Song Q, Bagci U (2017) Risk stratification of lung nodules using 3D CNN-based multi-task learning. In: International conference on information processing in medical imaging, pp 249–260. https://doi.org/10.1007/978-3-319-59050-9_20
  90. Ibragimov B, Xing L (2017) Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks. Med Phys 44(2):547–557.  https://doi.org/10.1002/mp.12045 CrossRefGoogle Scholar
  91. Ibragimov B, Toesca D, Chang D, Koong A, Xing L (2017) Combining deep learning with anatomy analysis for segmentation of portal vein for liver SBRT planning. Phys Med Biol 62(23):8943–8958.  https://doi.org/10.1088/1361-6560/aa9262 CrossRefGoogle Scholar
  92. Išgum I, van Ginneken B, Lessmann N (2018) Iterative convolutional neural networks for automatic vertebra identification and segmentation in CT images. In: Medical imaging: image processing, SPIE, vol 10574, p 7. https://doi.org/10.1117/12.2292731
  93. Isola P, Zhu JY, Zhou T, Efros AA (2017) Image-to-image translation with conditional adversarial networks. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 5967–5976. https://doi.org/10.1109/CVPR.2017.632
  94. Jackson P, Hardcastle N, Dawe N, Kron T, Hofman MS, Hicks RJ (2018) Deep learning renal segmentation for fully automated radiation dose estimation in unsealed source therapy. Front Oncol.  https://doi.org/10.3389/fonc.2018.00215 CrossRefGoogle Scholar
  95. Jiao J, Ourselin S (2017) Fast PET reconstruction using multi-scale fully convolutional neural networks. CoRR arXiv:1704.07244
  96. Jin X, Ye H, Li L, Xia Q (2017) Image segmentation of liver CT based on fully convolutional network. In: IEEE 10th international symposium on computational intelligence and design (ISCID), pp 210–213. https://doi.org/10.1109/ISCID.2017.49
  97. Jnawali K, Arbabshirani MR, Rao N, Patel AA (2018) Deep 3D convolution neural network for CT brain hemorrhage classification. In: Medical imaging: computer-aided diagnosis, SPIE, p 47. https://doi.org/10.1117/12.2293725
  98. Jung Kh, Park H, Hwang W (2017) Deep learning for medical image analysis: applications to computed tomography and magnetic resonance imaging. Hanyang Med Rev 37(2):61.  https://doi.org/10.7599/hmr.2017.37.2.61 CrossRefGoogle Scholar
  99. Jung H, Kim B, Lee I, Lee J, Kang J (2018) Classification of lung nodules in CT scans using three-dimensional deep convolutional neural networks with a checkpoint ensemble method. BMC Med Imaging 18(1):48.  https://doi.org/10.1186/s12880-018-0286-0 CrossRefGoogle Scholar
  100. Kalinovsky A, Liauchuk V, Tarasau A (2017) Lesion detection in CT images using deep learning semantic segmentation technique. In: International archives of the photogrammetry, remote sensing and spatial information sciences (ISPRS), vol XLII-2/W4, pp 13–17. https://doi.org/10.5194/isprs-archives-XLII-2-W4-13-2017
  101. Kang E, Min J, Ye JC (2017) A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction. Med Phys 44(10):e360–e375.  https://doi.org/10.1002/mp.12344 CrossRefGoogle Scholar
  102. Kasban H, El-Bendary MAM, Salama DH (2015) A comparative study of medical imaging techniques. Int J Inf Sci Intell Syst (IJISIS) 4:37–58Google Scholar
  103. Keshavamurthy KN, Leary OP, Merck LH, Kimia B, Collins S, Wright DW, Allen JW, Brock JF, Merck D (2017) Machine learning algorithm for automatic detection of CT-identifiable hyperdense lesions associated with traumatic brain injury. In: Medical imaging: computer-aided diagnosis, SPIE, 1, p 10134-2G. https://doi.org/10.1117/12.2254227
  104. Kim BC, Sung YS, Suk HI (2016) Deep feature learning for pulmonary nodule classification in a lung CT. In: IEEE 4th international winter conference on brain–computer interface (BCI), pp 1–3. https://doi.org/10.1109/IWW-BCI.2016.7457462
  105. Kim K, Wu D, Gong K, Dutta J, Kim JH, Son YD, Kim HK, El Fakhri G, Li Q (2018) Penalized PET reconstruction using deep learning prior and local linear fitting. IEEE Trans Med Imaging 37(6):1478–1487.  https://doi.org/10.1109/TMI.2018.2832613 CrossRefGoogle Scholar
  106. Kingma DP, Welling M (2014) Stochastic gradient VB and the variational auto-encoder. In: 2nd International conference on learning representations (ICLR)Google Scholar
  107. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: 25th International conference on neural information processing systems (NIPS). Curran Associates Inc., USA, pp 1097–1105Google Scholar
  108. Kumar A, Fulham MJ, Feng D, Kim J (2018) Co-learning feature fusion maps from PET-CT images of lung cancer. CoRR arXiv:1810.02492
  109. Kumar D, Wong A, Clausi DA (2015) Lung nodule classification using deep features in CT images. In: IEEE 12th conference on computer and robot vision, pp 133–138. https://doi.org/10.1109/CRV.2015.25
  110. Lakshmanaprabu S, Mohanty SN, Shankar K, Arunkumar N, Ramirez G (2019) Optimal deep learning model for classification of lung cancer on CT images. Future Gener Comput Syst 92:374–382.  https://doi.org/10.1016/j.future.2018.10.009 CrossRefGoogle Scholar
  111. Lee JG, Jun S, Cho YW, Lee H, Kim GB, Seo JB, Kim N (2017) Deep learning in medical imaging: general overview. Korean J Radiol 18(4):570.  https://doi.org/10.3348/kjr.2017.18.4.570 CrossRefGoogle Scholar
  112. Lee H, Hong H, Kim J, Jung DC (2018) Deep feature classification of angiomyolipoma without visible fat and renal cell carcinoma in abdominal contrast-enhanced CT images with texture image patches and hand-crafted feature concatenation. Med Phys 45(4):1550–1561.  https://doi.org/10.1002/mp.12828 CrossRefGoogle Scholar
  113. Lee D, Choi S, Kim HJ (2019) High quality imaging from sparsely sampled computed tomography data with deep learning and wavelet transform in various domains. Med Phys 46(1):104–115.  https://doi.org/10.1002/mp.13258 CrossRefGoogle Scholar
  114. Lei L, Zhu H, Gong Y, Cheng Q (2018) A deep residual networks classification algorithm of fetal heart CT images. In: IEEE international conference on imaging systems and techniques (IST), pp 1–4. https://doi.org/10.1109/IST.2018.8577179
  115. Li Q, Nishikawa RM (2015) Computer-aided detection and diagnosis in medical imaging. Taylor & Francis, New YorkCrossRefGoogle Scholar
  116. Lian C, Li H, Vera P, Ruan S (2018) Unsupervised co-segmentation of tumor in PET-CT images using belief functions based fusion. In: IEEE 15th international symposium on biomedical imaging (ISBI), pp 220–223. https://doi.org/10.1109/ISBI.2018.8363559
  117. Lian C, Ruan S, Denoeux T, Li H, Vera P (2019) Joint tumor segmentation in PET-CT images using co-clustering and fusion based on belief functions. IEEE Trans Image Process 28(2):755–766.  https://doi.org/10.1109/TIP.2018.2872908 MathSciNetCrossRefzbMATHGoogle Scholar
  118. Lin EC (2010) Radiation risk from medical imaging. Mayo Clin Proc 85(12):1142–1146.  https://doi.org/10.4065/mcp.2010.0260 CrossRefGoogle Scholar
  119. Lin J, Kligerman S, Goel R, Sajedi P, Suntharalingam M, Chuong MD (2015) State-of-the-art molecular imaging in esophageal cancer management: implications for diagnosis, prognosis, and treatment. J Gastrointest Oncol 6(1):3.  https://doi.org/10.3978/j.issn.2078-6891.2014.062 CrossRefGoogle Scholar
  120. Lisowska A, Beveridge E, Muir K, Poole I (2017) Thrombus detection in CT brain scans using a convolutional neural network. In: 10th International joint conference on biomedical engineering systems and technologies. SciTePress: Science and Technology Publications, pp 24–33. https://doi.org/10.5220/0006114600240033
  121. Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, van der Laak JA, van Ginneken B, Sánchez CI (2017) A survey on deep learning in medical image analysis. Med Image Anal 42(1995):60–88.  https://doi.org/10.1016/j.media.2017.07.005 CrossRefGoogle Scholar
  122. Liu S (2018) Automated analysis of quantitative image biomarkers from low-dose chest CT scans. Doctor of philosophy, Cornell University. https://doi.org/10.7298/X4VM49JN
  123. Liu J, Wang S, Linguraru MG, Yao J, Summers RM (2015) Computer-aided detection of exophytic renal lesions on non-contrast CT images. Med Image Anal 19(1):15–29.  https://doi.org/10.1016/j.media.2014.07.005 CrossRefGoogle Scholar
  124. Liu J, Lay N, Wei Z, Lu L, Kim L, Turkbey E, Summers RM (2016) Colitis detection on abdominal CT scans by rich feature hierarchies. In: Medical imaging: computer-aided diagnosis, SPIE, vol 9785, p 97851N. https://doi.org/10.1117/12.2217681
  125. Liu J, Lu L, Yao J, Bagheri M, Summers RM (2017) Pelvic artery calcification detection on CT scans using convolutional neural networks. In: Medical imaging: computer-aided diagnosis, SPIE, vol 10134, p 101341A. https://doi.org/10.1117/12.2255247
  126. Liu M, Cheng D, Yan W (2018a) Classification of Alzheimer’s disease by combination of convolutional and recurrent neural networks using FDG-PET images. Front Neuroinform.  https://doi.org/10.3389/fninf.2018.00035 CrossRefGoogle Scholar
  127. Liu X, Hou F, Qin H, Hao A (2018b) Multi-view multi-scale CNNs for lung nodule type classification from CT images. Pattern Recognit 77:262–275.  https://doi.org/10.1016/j.patcog.2017.12.022 CrossRefGoogle Scholar
  128. Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 3431–3440. https://doi.org/10.1109/CVPR.2015.7298965
  129. Lustberg T, van Soest J, Gooding M, Peressutti D, Aljabar P, van der Stoep J, van Elmpt W, Dekker A (2018) Clinical evaluation of atlas and deep learning based automatic contouring for lung cancer. Radiother Oncol 126(2):312–317.  https://doi.org/10.1016/j.radonc.2017.11.012 CrossRefGoogle Scholar
  130. Lyu J, Ling SH (2018) Using multi-level convolutional neural network for classification of lung nodules on CT images. In: 40th Annual international conference of the IEEE engineering in medicine and biology society (EMBC), pp 686–689. https://doi.org/10.1109/EMBC.2018.8512376
  131. Ma L, Guo R, Zhang G, Tade F, Schuster DM, Nieh P, Master V, Fei B (2017) Automatic segmentation of the prostate on CT images using deep learning and multi-atlas fusion. In: Medical imaging: image processing, SPIE, vol 10133, p 101332O. https://doi.org/10.1117/12.2255755
  132. Maier J, Eulig E, Vöth T, Knaup M, Kuntz J, Sawall S, Kachelrieß M (2019) Real-time scatter estimation for medical CT using the deep scatter estimation: method and robustness analysis with respect to different anatomies, dose levels, tube voltages, and data truncation. Med Phys 46(1):238–249.  https://doi.org/10.1002/mp.13274 CrossRefGoogle Scholar
  133. Martins P, Carbone I, Silva A, Teixeira A (2007) An MRI study of European Portuguese nasals. In: 8th Annual conference of the international speech communication association (Interspeech), C, pp 58–61Google Scholar
  134. Masood A, Sheng B, Li P, Hou X, Wei X, Qin J, Feng D (2018) Computer-assisted decision support system in pulmonary cancer detection and stage classification on CT images. J Biomed Inform 79:117–128.  https://doi.org/10.1016/j.jbi.2018.01.005 CrossRefGoogle Scholar
  135. McCollough CH, Leng S, Yu L, Fletcher JG (2015) Dual- and multi-energy CT: principles, technical approaches, and clinical applications. Radiology 276(3):637–653.  https://doi.org/10.1148/radiol.2015142631 CrossRefGoogle Scholar
  136. Men K, Dai J, Li Y (2017) Automatic segmentation of the clinical target volume and organs at risk in the planning CT for rectal cancer using deep dilated convolutional neural networks. Med Phys 44(12):6377–6389.  https://doi.org/10.1002/mp.12602 CrossRefGoogle Scholar
  137. Miki Y, Muramatsu C, Hayashi T, Zhou X, Hara T, Katsumata A, Fujita H (2017) Classification of teeth in cone-beam CT using deep convolutional neural network. Comput Biol Med 80:24–29.  https://doi.org/10.1016/j.compbiomed.2016.11.003 CrossRefGoogle Scholar
  138. Moses WW (2011) Fundamental limits of spatial resolution in PET. Nucl Instrum Methods Phys Res Sect A Accel Spectrom Detect Assoc Equip 648:S236–S240.  https://doi.org/10.1016/j.nima.2010.11.092 CrossRefGoogle Scholar
  139. Näppi JJ, Pickhardt P, Kim DH, Hironaka T, Yoshida H (2017) Deep learning of contrast-coated serrated polyps for computer-aided detection in CT colonography. In: Medical imaging: computer-aided diagnosis, SPIE, vol 10134, p 101340H. https://doi.org/10.1117/12.2255634
  140. Nardelli P, Jimenez-Carretero D, Bermejo-Pelaez D, Ledesma-Carbayo MJ, Rahaghi FN, Estepar RSJ (2017) Deep-learning strategy for pulmonary artery-vein classification of non-contrast CT images. In: IEEE 14th international symposium on biomedical imaging (ISBI), pp 384–387. https://doi.org/10.1109/ISBI.2017.7950543
  141. Nardelli P, Jimenez-Carretero D, Bermejo-Pelaez D, Washko GR, Rahaghi FN, Ledesma-Carbayo MJ, San Jose Estepar R (2018) Pulmonary artery-vein classification in CT images using deep learning. IEEE Trans Med Imaging 37(11):2428–2440.  https://doi.org/10.1109/TMI.2018.2833385 CrossRefGoogle Scholar
  142. Nazir S, Shahzad S, Mukhtar N (2019) Software birthmark design and estimation: a systematic literature. Arab J Sci Eng.  https://doi.org/10.1007/s13369-019-03718-9 CrossRefGoogle Scholar
  143. Nogueira MA, Abreu PH, Martins P, Machado P, Duarte H, Santos J (2017) Image descriptors in radiology images: a systematic review. Artif Intell Rev 47(4):531–559.  https://doi.org/10.1007/s10462-016-9492-8 CrossRefGoogle Scholar
  144. Oda H, Bhatia KK, Roth HR, Oda M, Kitasaka T, Iwano S, Homma H, Takabatake H, Mori M, Natori H, Schnabel JA, Mori K (2018) Dense volumetric detection and segmentation of mediastinal lymph nodes in chest CT images. In: Medical imaging: computer-aided diagnosis, SPIE, vol 10575, p 1. https://doi.org/10.1117/12.2287066
  145. Öman O, Mäkelä T, Salli E, Savolainen S, Kangasniemi M (2019) 3D convolutional neural networks applied to CT angiography in the detection of acute ischemic stroke. Eur Radiol Exp 3(1):8.  https://doi.org/10.1186/s41747-019-0085-6 CrossRefGoogle Scholar
  146. Onishi Y, Teramoto A, Tsujimoto M, Tsukamoto T, Saito K, Toyama H, Imaizumi K, Fujita H (2019) Automated pulmonary nodule classification in computed tomography images using a deep convolutional neural network trained by generative adversarial networks. BioMed Res Int 2019:1–9.  https://doi.org/10.1155/2019/6051939 CrossRefGoogle Scholar
  147. Özsavaş EE, Telatar Z, Dirican B, Sağer Ö, Beyzadeoğlu M (2014) Automatic segmentation of anatomical structures from CT scans of thorax for RTP. Comput Math Methods Med.  https://doi.org/10.1155/2014/472890 MathSciNetCrossRefzbMATHGoogle Scholar
  148. Patel A, van de Leemput SC, Prokop M, van Ginneken B, Manniesing R (2017) Automatic cerebrospinal fluid segmentation in non-contrast CT images using a 3D convolutional network. In: Medical imaging: computer-aided diagnosis, SPIE, vol 10134, p 1013420. https://doi.org/10.1117/12.2254022
  149. Paul R, Hawkins SH, Balagurunathan Y, Schabath MB, Gillies RJ, Hall LO, Goldgof DB (2016) Deep feature transfer learning in combination with traditional features predicts survival among patients with lung adenocarcinoma. Tomography 2(4):388–395.  https://doi.org/10.18383/j.tom.2016.00211 CrossRefGoogle Scholar
  150. Peng L, Lin L, Hu H, Li H, Ling X, Wang D, Han X, Iwamoto Y, Chen YW (2018) Classification of pulmonary emphysema in CT images based on multi-scale deep convolutional neural networks. In: 25th IEEE international conference on image processing (ICIP), pp 3119–3123. https://doi.org/10.1109/ICIP.2018.8451514
  151. Pereira G (2018) Deep learning techniques for the evaluation of response to treatment in Hodgkin Lymphoma. Master in biomedical engineering, University of CoimbraGoogle Scholar
  152. Pereira G, Domingues I, Martins P, Abreu PH, Duarte H, Santos J (2018) Registration of CT with PET: a comparison of intensity-based approaches. In: International workshop on combinatorial image analysis (IWCIA)Google Scholar
  153. Prassopoulos VK, Efthymiadou RD (2016) General principles of PET/CT imaging. In: PET/CT in lymphomas. Springer, Cham, pp 21–22. https://doi.org/10.1007/978-3-319-27380-8_2
  154. Qiang Y, Ge L, Zhao X, Zhang X, Tang X (2017) Pulmonary nodule diagnosis using dual-modal supervised autoencoder based on extreme learning machine. Expert Syst 34(6):e12224.  https://doi.org/10.1111/exsy.12224 CrossRefGoogle Scholar
  155. Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 779–788. https://doi.org/10.1109/CVPR.2016.91
  156. Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention (MICCAI). Springer, pp 234–241Google Scholar
  157. Roth HR, Lu L, Seff A, Cherry KM, Hoffman J, Wang S, Liu J, Turkbey E, Summers RM (2014) A new 2.5D representation for lymph node detection using random sets of deep convolutional neural network observations. In: International conference on medical image computing and computer-assisted intervention (MICCAI). Lecture notes in computer science, vol 8673. Springer, pp 520–527. https://doi.org/10.1007/978-3-319-10404-1_65
  158. Roth HR, Farag A, Lu L, Turkbey EB, Summers RM (2015a) Deep convolutional networks for pancreas segmentation in CT imaging. In: Medical imaging: image processing, SPIE, vol 9413, p 94131G. https://doi.org/10.1117/12.2081420, arXiv:1504.03967
  159. Roth HR, Lee CT, Shin HC, Seff A, Kim L, Yao J, Lu L, Summers RM (2015b) Anatomy-specific classification of medical images using deep convolutional nets. In: IEEE 12th international symposium on biomedical imaging (ISBI), pp 101–104. https://doi.org/10.1109/ISBI.2015.7163826
  160. Roth HR, Lu L, Farag A, Shin HC, Liu J, Turkbey EB, Summers RM (2015c) Deeporgan: multi-level deep convolutional networks for automated pancreas segmentation. In: International conference on medical image computing and computer-assisted intervention (MICCAI). Springer, pp 556–564Google Scholar
  161. Roth HR, Lu L, Liu J, Yao J, Seff A, Cherry K, Kim L, Summers RM (2016) Improving computer-aided detection using convolutional neural networks and random view aggregation. IEEE Trans Med Imaging 35(5):1170–1181.  https://doi.org/10.1109/TMI.2015.2482920 CrossRefGoogle Scholar
  162. Sathyan H, Panicker JV (2018) Lung nodule classification using deep ConvNets on CT images. In: IEEE 9th international conference on computing, communication and networking technologies (ICCCNT), pp 1–5. https://doi.org/10.1109/ICCCNT.2018.8494084
  163. Schwyzer M, Ferraro DA, Muehlematter UJ, Curioni-Fontecedro A, Huellner MW, von Schulthess GK, Kaufmann PA, Burger IA, Messerli M (2018) Automated detection of lung cancer at ultralow dose PET/CT by deep neural networks: initial results. Lung Cancer 126:170–173.  https://doi.org/10.1016/j.lungcan.2018.11.001 CrossRefGoogle Scholar
  164. Setio AAA, Ciompi F, Litjens G, Gerke P, Jacobs C, van Riel SJ, Wille MMW, Naqibullah M, Sanchez CI, van Ginneken B (2016) Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks. IEEE Trans Med Imaging 35(5):1160–1169.  https://doi.org/10.1109/TMI.2016.2536809 CrossRefGoogle Scholar
  165. Shaffie A, Soliman A, Ghazal M, Taher F, Dunlap N, Wang B, Van Berkel V, Gimelfarb G, Elmaghraby A, El-Baz A (2018) A novel autoencoder-based diagnostic system for early assessment of lung cancer. In: 25th IEEE international conference on image processing (ICIP), pp 1393–1397. https://doi.org/10.1109/ICIP.2018.8451595
  166. Shaish H, Mutasa S, Makkar J, Chang P, Schwartz L, Ahmed F (2019) Prediction of lymph node maximum standardized uptake value in patients with cancer using a 3D convolutional neural network: a proof-of-concept study. Am J Roentgenol 212(2):238–244.  https://doi.org/10.2214/AJR.18.20094 CrossRefGoogle Scholar
  167. Shan H, Zhang Y, Yang Q, Kruger U, Kalra MK, Sun L, Cong W, Wang G (2018) 3-D convolutional encoder–decoder network for low-dose CT via transfer learning from a 2-D trained network. IEEE Trans Med Imaging 37(6):1522–1534.  https://doi.org/10.1109/TMI.2018.2832217 CrossRefGoogle Scholar
  168. Sharma N, Ray A, Shukla K, Sharma S, Pradhan S, Srivastva A, Aggarwal L (2010) Automated medical image segmentation techniques. J Med Phys 35(1):3.  https://doi.org/10.4103/0971-6203.58777 CrossRefGoogle Scholar
  169. Shen W, Yang F, Mu W, Yang C, Yang X, Tian J (2015a) Automatic localization of vertebrae based on convolutional neural networks. In: Medical imaging: image processing, international society for optics and photonics, vol 9413, p 94132E. https://doi.org/10.1117/12.2081941
  170. Shen W, Zhou M, Yang F, Yang C, Tian J (2015b) Multi-scale convolutional neural networks for lung nodule classification. In: International conference on information processing in medical imaging. Springer, pp 588–599. https://doi.org/10.1007/978-3-319-19992-4_46
  171. Shen W, Zhou M, Yang F, Dong D, Yang C, Zang Y, Tian J (2016) Learning from experts: developing transferable deep features for patient-level lung cancer prediction. In: International conference on medical image computing and computer-assisted intervention (MICCAI). Springer, pp 124–131. https://doi.org/10.1007/978-3-319-46723-8_15
  172. Shen D, Wu G, Hi Suk (2017a) Deep learning in medical image analysis. Annu Rev Biomed Eng 19(1):221–248.  https://doi.org/10.1146/annurev-bioeng-071516-044442 CrossRefGoogle Scholar
  173. Shen W, Zhou M, Yang F, Yu D, Dong D, Yang C, Zang Y, Tian J (2017b) Multi-crop convolutional neural networks for lung nodule malignancy suspiciousness classification. Pattern Recognit 61(SI):663–673.  https://doi.org/10.1016/j.patcog.2016.05.029 CrossRefGoogle Scholar
  174. Shen S, Han SX, Aberle DR, Bui AA, Hsu W (2019) An interpretable deep hierarchical semantic convolutional neural network for lung nodule malignancy classification. Expert Syst Appl 128:84–95.  https://doi.org/10.1016/j.eswa.2019.01.048 CrossRefGoogle Scholar
  175. Shi Y, Yang W, Gao Y, Shen D (2017) Does manual delineation only provide the side information in CT prostate segmentation? In: International conference on medical image computing and computer-assisted intervention (MICCAI), vol 10435, pp 692–700. https://doi.org/10.1007/978-3-319-66179-7_79
  176. Shin HC, Roth HR, Gao M, Lu L, Xu Z, Nogues I, Yao J, Mollura D, Summers RM (2016) Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging 35(5):1285–1298.  https://doi.org/10.1109/TMI.2016.2528162 CrossRefGoogle Scholar
  177. Singh S, Srivastava A, Mi L, Chen K, Wang Y, Caselli RJ, Goradia D, Reiman EM (2017) Deep-learning-based classification of FDG-PET data for Alzheimer’s disease categories. In: 13th International conference on medical information processing and analysis, international society for optics and photonics, SPIE, vol 10572, p 84. https://doi.org/10.1117/12.2294537
  178. Smith RL, Florence V, Paisey S, Fittock E, Siebzehnrubl F, Spezi E, Marshall C (2018) Deep learning pre-clinical medical image segmentation for automated organ-wise delineation of PET. Eur J Nucl Med Mol Imaging 45(1):S290Google Scholar
  179. Song Q, Zhao L, Luo X, Dou X (2017) Using deep learning for classification of lung nodules on computed tomography images. J Healthc Eng 2017:1–7.  https://doi.org/10.1155/2017/8314740 CrossRefGoogle Scholar
  180. Spencer B, Wang G (2017) Statistical image reconstruction for shortened dynamic PET using a dual kernel method. In: IEEE nuclear science symposium and medical imaging conference (NSS/MIC), pp 1–3. https://doi.org/10.1109/NSSMIC.2017.8532801
  181. Šprem J, de Vos BD, de Jong PA, Viergever MA, Išgum I (2017) Classification of coronary artery calcifications according to motion artifacts in chest CT using a convolutional neural network. In: Medical imaging: image processing, SPIE, vol 10133, p 101330R. https://doi.org/10.1117/12.2253669
  182. Sun C, Guo S, Zhang H, Li J, Chen M, Ma S, Jin L, Liu X, Li X, Qian X (2017a) Automatic segmentation of liver tumors from multiphase contrast-enhanced CT images based on FCNs. Artif Intell Med 83(SI):58–66.  https://doi.org/10.1016/j.artmed.2017.03.008 CrossRefGoogle Scholar
  183. Sun C, Guo S, Zhang H, Li J, Ma S, Li X (2017b) Liver lesion segmentation in CT images with MK-FCN. In: IEEE 2nd advanced information technology, electronic and automation control conference (IAEAC), pp 1794–1798. https://doi.org/10.1109/IAEAC.2017.8054322
  184. Tajbakhsh N, Shin JY, Gurudu SR, Hurst RT, Kendall CB, Gotway MB, Liang J (2016) Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans Med Imaging 35(5):1299–1312.  https://doi.org/10.1109/TMI.2016.2535302 CrossRefGoogle Scholar
  185. Tang Y, Cai J, Lu L, Harrison AP, Yan K, Xiao J, Yang L, Summers RM (2018) CT image enhancement using stacked generative adversarial networks and transfer learning for lesion segmentation improvement. In: Machine learning in medical imaging. Springer, pp 46–54Google Scholar
  186. Teramoto A, Fujita H, Yamamuro O, Tamaki T (2016) Automated detection of pulmonary nodules in PET/CT images: ensemble false-positive reduction using a convolutional neural network technique. Med Phys 43(6Part1):2821–2827.  https://doi.org/10.1118/1.4948498 CrossRefGoogle Scholar
  187. Tomita N, Cheung YY, Hassanpour S (2018) Deep neural networks for automatic detection of osteoporotic vertebral fractures on CT scans. Comput Biol Med 98:8–15.  https://doi.org/10.1016/j.compbiomed.2018.05.011 CrossRefGoogle Scholar
  188. Townsend DW (2004) Physical principles and technology of clinical PET imaging. Ann Acad Med 33(2):133–45Google Scholar
  189. Townsend DW (2008) Positron emission tomography/computed tomography. Semin Nucl Med 38(3):152–166.  https://doi.org/10.1053/j.semnuclmed.2008.01.003 CrossRefGoogle Scholar
  190. Trullo R, Petitjean C, Nie D, Shen D, Ruan S (2017a) Fully automated esophagus segmentation with a hierarchical deep learning approach. In: IEEE international conference on signal and image processing applications (ICSIPA), pp 503–506. https://doi.org/10.1109/ICSIPA.2017.8120664
  191. Trullo R, Petitjean C, Nie D, Shen D, Ruan S (2017b) Joint segmentation of multiple thoracic organs in CT images with two collaborative deep architectures. In: International workshop on deep learning in medical image analysis (DLMIA), vol 10553, pp 21–29. https://doi.org/10.1007/978-3-319-67558-9_3
  192. Trullo R, Petitjean C, Dubray B, Ruan S (2019) Multiorgan segmentation using distance-aware adversarial networks. J Med Imaging 6(01):1.  https://doi.org/10.1117/1.JMI.6.1.014001 CrossRefGoogle Scholar
  193. Tseng HH, Luo Y, Cui S, Chien JT, Ten Haken RK, Naqa IE (2017) Deep reinforcement learning for automated radiation adaptation in lung cancer. Med Phys 44(12):6690–6705.  https://doi.org/10.1002/mp.12625 CrossRefGoogle Scholar
  194. Umehara K, Näppi JJ, Hironaka T, Regge D, Ishida T, Yoshida H (2017) Deep ensemble learning of virtual endoluminal views for polyp detection in CT colonography. In: Medical imaging: computer-aided diagnosis, SPIE, vol 10134, p 101340G. https://doi.org/10.1117/12.2255606
  195. van Tulder G, de Bruijne M (2016) Combining generative and discriminative representation learning for lung CT analysis with convolutional restricted Boltzmann machines. IEEE Trans Med Imaging 35(5):1262–1272.  https://doi.org/10.1109/TMI.2016.2526687 CrossRefGoogle Scholar
  196. Vaquero JJ, Kinahan P (2015) Positron emission tomography: current challenges and opportunities for technological advances in clinical and preclinical imaging systems. Annu Rev Biomed Eng 17(1):385–414.  https://doi.org/10.1146/annurev-bioeng-071114-040723 CrossRefGoogle Scholar
  197. Vial A, Stirling D, Field M, Ros M, Ritz C, Carolan M, Holloway L, Miller AA (2018) The role of deep learning and radiomic feature extraction in cancer-specific predictive modelling: a review. Transl Cancer Res 7(3):803–816.  https://doi.org/10.21037/tcr.2018.05.02 CrossRefGoogle Scholar
  198. Vivanti R, Szeskin A, Lev-Cohain N, Sosna J, Joskowicz L (2017) Automatic detection of new tumors and tumor burden evaluation in longitudinal liver CT scan studies. Int J Comput Assist Radiol Surg 12(11):1945–1957.  https://doi.org/10.1007/s11548-017-1660-z CrossRefGoogle Scholar
  199. Wadsak W, Mitterhauser M (2010) Basics and principles of radiopharmaceuticals for PET/CT. Eur J Radiol 73(3):461–469.  https://doi.org/10.1016/j.ejrad.2009.12.022 CrossRefGoogle Scholar
  200. Wang Y (2018) Convolutional neural network based malignancy detection of pulmonary nodule on computer tomography. Master of science, University of SaskatchewanGoogle Scholar
  201. Wang H, Zhou Z, Li Y, Chen Z, Lu P, Wang W, Liu W, Yu L (2017a) Comparison of machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer from 18F-FDG PET/CT images. EJNMMI Res 7(1):11.  https://doi.org/10.1186/s13550-017-0260-9 CrossRefGoogle Scholar
  202. Wang Y, Qiu Y, Thai T, Moore K, Liu H, Zheng B (2017b) A two-step convolutional neural network based computer-aided detection scheme for automatically segmenting adipose tissue volume depicting on CT images. Comput Methods Programs Biomed 144(405):97–104.  https://doi.org/10.1016/j.cmpb.2017.03.017 CrossRefGoogle Scholar
  203. Wang Y, Liao Y, Zhang Y, He J, Li S, Bian Z, Zhang H, Gao Y, Meng D, Zuo W, Zeng D, Ma J (2018a) Iterative quality enhancement via residual-artifact learning networks for low-dose CT. Phys Med Biol 63(21):215004.  https://doi.org/10.1088/1361-6560/aae511 CrossRefGoogle Scholar
  204. Wang Y, Yu B, Wang L, Zu C, Lalush DS, Lin W, Wu X, Zhou J, Shen D, Zhou L (2018b) 3D conditional generative adversarial networks for high-quality PET image estimation at low dose. NeuroImage 174(8):550–562.  https://doi.org/10.1016/j.neuroimage.2018.03.045 CrossRefGoogle Scholar
  205. Wang Z, Li J, Enoh M (2019) Removing ring artifacts in CBCT images via generative adversarial networks with unidirectional relative total variation loss. Neural Comput Appl.  https://doi.org/10.1007/s00521-018-04007-6 CrossRefGoogle Scholar
  206. Wolterink JM, Leiner T, de Vos BD, van Hamersvelt RW, Viergever MA, Išgum I (2016) Automatic coronary artery calcium scoring in cardiac CT angiography using paired convolutional neural networks. Med Image Anal 34:123–136.  https://doi.org/10.1016/j.media.2016.04.004 CrossRefGoogle Scholar
  207. Wolterink JM, Leiner T, Viergever MA, Isgum I (2017) Generative adversarial networks for noise reduction in low-dose CT. IEEE Trans Med Imaging 36(12):2536–2545.  https://doi.org/10.1109/TMI.2017.2708987 CrossRefGoogle Scholar
  208. Xiang L, Qiao Y, Nie D, An L, Lin W, Wang Q, Shen D (2017) Deep auto-context convolutional neural networks for standard-dose PET image estimation from low-dose PET/MRI. Neurocomputing 267:406–416.  https://doi.org/10.1016/j.neucom.2017.06.048 CrossRefGoogle Scholar
  209. Xie Y, Zhang J, Xia Y, Fulham M, Zhang Y (2018) Fusing texture, shape and deep model-learned information at decision level for automated classification of lung nodules on chest CT. Inf Fus 42:102–110.  https://doi.org/10.1016/j.inffus.2017.10.005 CrossRefGoogle Scholar
  210. Xie H, Yang D, Sun N, Chen Z, Zhang Y (2019a) Automated pulmonary nodule detection in CT images using deep convolutional neural networks. Pattern Recognit 85:109–119.  https://doi.org/10.1016/j.patcog.2018.07.031 CrossRefGoogle Scholar
  211. Xie Y, Xia Y, Zhang J, Song Y, Feng D, Fulham M, Cai W (2019b) Knowledge-based collaborative deep learning for benign–malignant lung nodule classification on chest CT. IEEE Trans Med Imaging 38(4):991–1004.  https://doi.org/10.1109/TMI.2018.2876510 CrossRefGoogle Scholar
  212. Xu J, Liu H (2017) Segmentation of pulmonary CT Image by using convolutional neural network based on membership function. In: IEEE international conference on computational science and engineering (CSE) and IEEE international conference on embedded and ubiquitous computing (EUC), pp 198–203. https://doi.org/10.1109/CSE-EUC.2017.42
  213. Xu L, Tetteh G, Lipkova J, Zhao Y, Li H, Christ P, Piraud M, Buck A, Shi K, Menze BH (2018) Automated whole-body bone lesion detection for multiple myeloma on 68 Ga-pentixafor PET/CT imaging using deep learning methods. Contrast Media Mol Imaging 2018:1–11.  https://doi.org/10.1155/2018/2391925 CrossRefGoogle Scholar
  214. Xu M, Qi S, Yue Y, Teng Y, Xu L, Yao Y, Qian W (2019) Segmentation of lung parenchyma in CT images using CNN trained with the clustering algorithm generated dataset. BioMed Eng OnLine 18(1):2.  https://doi.org/10.1186/s12938-018-0619-9 CrossRefGoogle Scholar
  215. Xue Y, Chen S, Qin J, Liu Y, Huang B, Chen H (2017) Application of deep learning in automated analysis of molecular images in cancer: a survey. Contrast Media Mol Imaging 2017:1–10.  https://doi.org/10.1155/2017/9512370 CrossRefGoogle Scholar
  216. Yan Z, Zhan Y, Peng Z, Liao S, Shinagawa Y, Zhang S, Metaxas DN, Zhou XS (2016) Multi-instance deep learning: discover discriminative local anatomies for bodypart recognition. IEEE Trans Med Imaging 35(5):1332–1343.  https://doi.org/10.1109/TMI.2016.2524985 CrossRefGoogle Scholar
  217. Yang H, Yu H, Wang G (2016) Deep learning for the classification of lung nodules. CoRR arXiv:1611.06651
  218. Yang Q, Yan P, Zhang Y, Yu H, Shi Y, Mou X, Kalra MK, Zhang Y, Sun L, Wang G (2018) Low-dose CT image denoising using a generative adversarial network with wasserstein distance and perceptual loss. IEEE Trans Med Imaging 37(6):1348–1357.  https://doi.org/10.1109/TMI.2018.2827462 CrossRefGoogle Scholar
  219. Yi X, Babyn P (2018) Sharpness-aware low-dose CT denoising using conditional generative adversarial network. J Digit Imaging 31(5):655–669.  https://doi.org/10.1007/s10278-018-0056-0 CrossRefGoogle Scholar
  220. You C, Yang Q, Shan H, Gjesteby L, Li G, Ju S, Zhang Z, Zhao Z, Zhang Y, Cong W, Wang G (2018) Structurally-sensitive multi-scale deep neural network for low-dose CT denoising. IEEE Access 6:41839–41855.  https://doi.org/10.1109/ACCESS.2018.2858196 CrossRefGoogle Scholar
  221. Ypsilantis PP, Siddique M, Sohn HM, Davies A, Cook G, Goh V, Montana G (2015) Predicting response to neoadjuvant chemotherapy with PET imaging using convolutional neural networks. PLoS ONE 10(9):e0137036.  https://doi.org/10.1371/journal.pone.0137036 CrossRefGoogle Scholar
  222. Yuan Y (2017) Hierarchical convolutional–deconvolutional neural networks for automatic liver and tumor segmentation. CoRR i:3–6Google Scholar
  223. Yuan J, Liu X, Hou F, Qin H, Hao A (2018) Hybrid-feature-guided lung nodule type classification on CT images. Comput Graph 70:288–299.  https://doi.org/10.1016/j.cag.2017.07.020 CrossRefGoogle Scholar
  224. Zahedinasab R, Mohseni H (2018) Enhancement of CT brain images classification based on deep learning network with adaptive activation functions. In: IEEE 8th international conference on computer and knowledge engineering (ICCKE), pp 182–187. https://doi.org/10.1109/ICCKE.2018.8566362
  225. Zhang Y, Yu H (2018) Convolutional neural network based metal artifact reduction in X-ray computed tomography. IEEE Trans Med Imaging 37(6):1370–1381.  https://doi.org/10.1109/TMI.2018.2823083 CrossRefGoogle Scholar
  226. Zhang Y, He Z, Zhong C, Zhang Y, Shi Z (2017) Fully convolutional neural network with post-processing methods for automatic liver segmentation from CT. In: IEEE Chinese automation congress (CAC), pp 3864–3869. https://doi.org/10.1109/CAC.2017.8243454
  227. Zhao F, Xie X (2013) An overview of interactive medical image segmentation. Ann BMVA 2013(7):1–22Google Scholar
  228. Zhao X, Li L, Lu W, Tan S (2018a) Tumor co-segmentation in PET/CT using multi-modality fully convolutional neural network. Phys Med Biol 64(1):015011.  https://doi.org/10.1088/1361-6560/aaf44b CrossRefGoogle Scholar
  229. Zhao X, Liu L, Qi S, Teng Y, Li J, Qian W (2018b) Agile convolutional neural network for pulmonary nodule classification using CT images. Int J Comput Assist Radiol Surg 13(4):585–595.  https://doi.org/10.1007/s11548-017-1696-0 CrossRefGoogle Scholar
  230. Zhao L, Lu Z, Jiang J, Zhou Y, Wu Y, Feng Q (2019) Automatic nasopharyngeal carcinoma segmentation using fully convolutional networks with auxiliary paths on dual-modality PET-CT images. J Digit Imaging 32(3):462–470.  https://doi.org/10.1007/s10278-018-00173-0 CrossRefGoogle Scholar
  231. Zheng Y, Liu D, Georgescu B, Nguyen H, Comaniciu D (2015) 3D deep learning for efficient and robust landmark detection in volumetric data. In: International conference on medical image computing and computer-assisted intervention (MICCAI). Springer, pp 565–572. https://doi.org/10.1007/978-3-319-24553-9_69
  232. Zhong Z, Kim Y, Zhou L, Plichta K, Allen B, Buatti J, Wu X (2018) 3D fully convolutional networks for co-segmentation of tumors on PET-CT images. In: IEEE 15th international symposium on biomedical imaging (ISBI), pp 228–231. https://doi.org/10.1109/ISBI.2018.8363561
  233. Zhou X, Ito T, Takayama R, Wang S, Hara T, Fujita H (2016) Three-dimensional CT image segmentation by combining 2D fully convolutional network with 3D majority voting. In: Deep learning and data labeling for medical applications. Lecture notes in computer science, vol 10008. Springer, pp 111–120. https://doi.org/10.1007/978-3-319-46976-8_12
  234. Zhou X, Takayama R, Wang S, Hara T, Fujita H (2017a) Deep learning of the sectional appearances of 3D CT images for anatomical structure segmentation based on an FCN voting method. Med Phys 44(10):5221–5233.  https://doi.org/10.1002/mp.12480 CrossRefGoogle Scholar
  235. Zhou X, Takayama R, Wang S, Zhou X, Hara T, Fujita H (2017b) Automated segmentation of 3D anatomical structures on CT images by using a deep convolutional network based on end-to-end learning approach. In: Medical imaging: image processing, SPIE, vol 10133, p 1013324. https://doi.org/10.1117/12.2254201
  236. Zhu J, Zhang J, Qiu B, Liu Y, Liu X, Chen L (2019) Comparison of the automatic segmentation of multiple organs at risk in CT images of lung cancer between deep convolutional neural network-based and atlas-based techniques. Acta Oncol 58(2):257–264.  https://doi.org/10.1080/0284186X.2018.1529421 CrossRefGoogle Scholar
  237. Zreik M, van Hamersvelt RW, Wolterink JM, Leiner T, Viergever MA, Isgum I (2018) A recurrent CNN for automatic detection and classification of coronary artery plaque and stenosis in coronary CT angiography. IEEE Trans Med Imaging.  https://doi.org/10.1109/TMI.2018.2883807 CrossRefGoogle Scholar

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© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.CI-IPOP, IPO Porto, Rua Dr. António Bernardino de AlmeidaPortoPortugal
  2. 2.CISUC, Department of Informatics EngineeringUniversity of CoimbraCoimbraPortugal

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