Der Nuklearmediziner 2019; 42(02): 118-132
DOI: 10.1055/a-0838-8124
CME-Fortbildung
© Georg Thieme Verlag KG Stuttgart · New York

Deep Learning in der SPECT und PET des Gehirns

Deep Learning in SPECT and PET of the brain
Ralph Buchert
1   Klinik und Poliklinik für Diagnostische und Interventionelle Radiologie und Nuklearmedizin, Universitätsklinikum Hamburg-Eppendorf, Hamburg
,
Julia Krüger
2   jung diagnostics, Hamburg
,
Nils Gessert
3   Institut für Medizintechnische Systeme, Technische Universität Hamburg, Hamburg
,
Wencke Lehnert
1   Klinik und Poliklinik für Diagnostische und Interventionelle Radiologie und Nuklearmedizin, Universitätsklinikum Hamburg-Eppendorf, Hamburg
,
Ivayla Apostolova
1   Klinik und Poliklinik für Diagnostische und Interventionelle Radiologie und Nuklearmedizin, Universitätsklinikum Hamburg-Eppendorf, Hamburg
,
Susanne Klutmann
1   Klinik und Poliklinik für Diagnostische und Interventionelle Radiologie und Nuklearmedizin, Universitätsklinikum Hamburg-Eppendorf, Hamburg
,
Alexander Schlaefer
3   Institut für Medizintechnische Systeme, Technische Universität Hamburg, Hamburg
› Author Affiliations

Subject Editor: Wissenschaftlich verantwortlich gemäß Zertifizierungsbestimmungen für diesen Beitrag ist Prof. Dr. med. Stefan Dresel, Berlin
Further Information

Publication History

Publication Date:
22 July 2019 (online)

Deep Learning hat in den letzten Jahren in vielen Bereichen spektakuläre Erfolge erzielt, nicht zuletzt in der medizinischen Bildverarbeitung. Nach einer kurzen Einführung in die grundlegenden Ideen von Deep Learning sollen in diesem Übersichtsartikel einige ausgewählte Anwendungen in der SPECT und PET des Gehirns vorgestellt werden.

Abstract

Deep learning has led to stunning achievements in many areas in recent years, including medical image processing. After a brief discussion of the basic principles of deep learning, some selected applications of deep learning in SPECT and PET of the brain will be presented.

 
  • Literatur

  • 1 Prashanth R, Roy SD, Mandal PK. et al. Automatic classification and prediction models for early Parkinsonʼs disease diagnosis from SPECT imaging. Expert Systems with Applications 2014; 41: 3333-3342
  • 2 Zubal IG, Early M, Yuan O. et al. Optimized, automated striatal uptake analysis applied to SPECT brain scans of Parkinson's disease patients. Journal of Nuclear Medicine 2007; 48: 857-864
  • 3 Kupitz D, Apostolova I, Lange C. et al. Global scaling for semi-quantitative analysis in FP-CIT SPECT. Nuklearmedizin 2014; 53: 234-241
  • 4 Bengio Y, Courville A, Vincent P. Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 2013; 35: 1798-1828
  • 5 Tagare HD, DeLorenzo C, Chelikani S. et al. Voxel-based logistic analysis of PPMI control and Parkinson's disease DaTscans. Neuroimage 2017; 152: 299-311
  • 6 Oliveira FPM, Castelo-Branco M. Computer-aided diagnosis of Parkinson's disease based on [123I]FP-CIT SPECT binding potential images, using the voxels-as-features approach and support vector machines. Journal of Neural Engineering 2015; 12: 026008
  • 7 Bengio Y, Delalleau O. On the Expressive Power of Deep Architectures. Algorithmic Learning Theory 2011; 6925: 18-36
  • 8 Bengio Y. Learning Deep Architectures for AI. Foundations and Trends in Machine Learning 2009; 1-127
  • 9 He K, Zhang X, Ren S. et al. Deep Residual Learning for Image Recognition. arXiv 2015; 1512.03385
  • 10 Eklund A, Dufort P, Forsberg D. et al. Medical image processing on the GPU – Past, present and future. Medical Image Analysis 2013; 17: 1073-1094
  • 11 LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015; 521: 436-444
  • 12 Goodfellow I, Bengio Y, Courville A. Deep Learning. Cambridge, MA, USA: MIT Press; 2016
  • 13 Choi H. Deep Learning in Nuclear Medicine and Molecular Imaging: Current Perspectives and Future Directions. Nuclear Medicine and Molecular Imaging 2018; 52: 109-118
  • 14 Krizhevsky A, Sutskever I, Hinton G. ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems 2012; 25 DOI: 10.1145/3065386.
  • 15 Russakovsky O, Deng J, Su H. et al. ImageNet large scale visual recognition challenge. Journal of Computer Vision 2014; 115: 1-42
  • 16 Lecun Y, Bottou L, Bengio Y. et al. Gradient-based learning applied to document recognition. Proceedings of the Ieee 1998; 86: 2278-2324
  • 17 Obermeyer Z, Emanuel EJ. Predicting the Future – Big Data, Machine Learning, and Clinical Medicine. New England Journal of Medicine 2016; 375: 1216-1219
  • 18 Lee CH, Yoon HJ. Medical big data: promise and challenges. Kidney Research and Clinical Practice 2017; 36: 3-11
  • 19 Mueller SG, Weiner MW, Thal LJ. et al. Ways toward an early diagnosis in Alzheimer's disease: the Alzheimer's Disease Neuroimaging Initiative (ADNI). Alzheimers Dement 2005; 1: 55-66
  • 20 Jagust WJ, Landau SM, Koeppe RA. et al. The Alzheimer's Disease Neuroimaging Initiative 2 PET Core: 2015. Alzheimers Dement 2015; 11: 757-771
  • 21 Parkinson Progression Marker Initiative. The Parkinson Progression Marker Initiative (PPMI). Prog Neurobiol 2011; 95: 629-635
  • 22 Marek K, Chowdhury S, Siderowf A. et al. Parkinson's Progression Markers I. The Parkinson's progression markers initiative (PPMI) – establishing a PD biomarker cohort. Ann Clin Transl Neurol 2018; 5: 1460-1477
  • 23 Martinez-Murcia FJ, Gorriz JM, Ramirez J. et al. Functional Brain Imaging Synthesis Based on Image Decomposition and Kernel Modeling: Application to Neurodegenerative Diseases. Front Neuroinform 2017; 11: 65
  • 24 Kim DH, Wit H, Thurston M. Artificial intelligence in the diagnosis of Parkinson's disease from ioflupane-123 single-photon emission computed tomography dopamine transporter scans using transfer learning. Nucl Med Commun 2018; 39: 887-893
  • 25 Warren E. Strengthening Research through Data Sharing. N Engl J Med 2016; 375: 401-403
  • 26 Litjens G, Kooi T, Bejnordi BE. et al. A survey on deep learning in medical image analysis. Med Image Anal 2017; 42: 60-88
  • 27 Moeskops P, de Bresser J, Kuijf HJ. et al. Evaluation of a deep learning approach for the segmentation of brain tissues and white matter hyperintensities of presumed vascular origin in MRI. Neuroimage-Clinical 2018; 17: 251-262
  • 28 Erlandsson K, Buvat I, Pretorius PH. et al. A review of partial volume correction techniques for emission tomography and their applications in neurology, cardiology and oncology. Physics in Medicine and Biology 2012; 57: R119-R159
  • 29 Gonzalez-Escamilla G, Lange C, Teipel S. et al. Neuroimaging AsD. PETPVE12: an SPM toolbox for Partial Volume Effects correction in brain PET Application to amyloid imaging with AV45-PET. Neuroimage 2017; 147: 669-677
  • 30 Thyreau B, Sato K, Fukuda H. et al. Segmentation of the hippocampus by transferring algorithmic knowledge for large cohort processing. Medical Image Analysis 2018; 43: 214-228
  • 31 Choi H, Jin KH. Fast and robust segmentation of the striatum using deep convolutional neural networks. Journal of Neuroscience Methods 2016; 274: 146-153
  • 32 Cheng X, Zhang L, Zheng YF. Deep similarity learning for multimodal medical images. Computer Methods in Biomechanics and Biomedical Engineering-Imaging and Visualization 2018; 6: 248-252
  • 33 Li RJ, Zhang WL, Suk HI. et al. Deep Learning Based Imaging Data Completion for Improved Brain Disease Diagnosis. Medical Image Computing and Computer-Assisted Intervention - Miccai 2014; 17: 305-312
  • 34 Goodfellow I, Pouget-Abadiey J, Mirza M. et al. Generative adversarial nets. Advances in neural information processing systems. Montreal, Canada: 2014
  • 35 Nie D, Trullo R, Petitjean C. et al. Medical image synthesis with context-aware generative adversarial networks. International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer; 2017
  • 36 Karpathy A, Li FF. Deep Visual-Semantic Alignments for Generating Image Descriptions. Ieee Transactions on Pattern Analysis and Machine Intelligence 2017; 39: 664-676
  • 37 Shin HC, Roberts K, Lu L. et al. Learning to read chest x-rays: Recurrent neural cascade model for automated image annotation. IEEE Conference on Computer Vision and Pattern Recognition. 2016
  • 38 Wang X, Peng Y, Lu L. et al. Tienet: Text-image embedding network for common thorax disease classification and reporting in chest x-rays. IEEE Conference on Computer Vision and Pattern Recognition. 2018
  • 39 Xiang L, Qiao Y, Nie D. et al. Deep auto-context convolutional neural networks for standard-dose PET image estimation from low-dose PET/MRI. Neurocomputing 2017; 267: 406-416
  • 40 Choi H, Ha S, Im HJ. et al. Refining diagnosis of Parkinson's disease with deep learning-based interpretation of dopamine transporter imaging. Neuroimage-Clinical 2017; 16: 586-594
  • 41 Erro R, Schneider SA, Stamelou M. et al. What do patients with scans without evidence of dopaminergic deficit (SWEDD) have? New evidence and continuing controversies. J Neurol Neurosurg Psychiatry 2016; 87: 319-323
  • 42 Nicastro N, Garibotto V, Badoud S. et al. Scan without evidence of dopaminergic deficit: A 10-year retrospective study. Parkinsonism Relat Disord 2016; 31: 53-58
  • 43 Ding Y, Sohn JH, Kawczynski MG. et al. A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using (18)F-FDG PET of the Brain. Radiology 2018; 180958
  • 44 Szegedy C, Vanhouke V, Ioffe S. et al. Rethinking the inception architecture for computer vision. IEEE Conference on Computer Vision and Pattern Recognition 2015; 2818-2826
  • 45 Choi H, Jin KH. Predicting cognitive decline with deep learning of brain metabolism and amyloid imaging. Behav Brain Res 2018; 344: 103-109
  • 46 Lu D, Popuri K, Ding GW. et al. Multiscale deep neural network based analysis of FDG-PET images for the early diagnosis of Alzheimerʼs disease. Med Image Anal 2018; 46: 26-34
  • 47 Segovia F, Gorriz JM, Ramirez J. et al. Using deep neural networks along with dimensionality reduction techniques to assist the diagnosis of neurodegenerative disorders. Log J IGPL 2018; 26: 618-628
  • 48 Shi J, Zheng X, Li Y. et al. Multimodal Neuroimaging Feature Learning With Multimodal Stacked Deep Polynomial Networks for Diagnosis of Alzheimer's Disease. IEEE J Biomed Health Inform 2018; 22: 173-183
  • 49 Zhou T, Thung KH, Zhu X. et al. Effective feature learning and fusion of multimodality data using stage-wise deep neural network for dementia diagnosis. Hum Brain Mapp 2019; 40: 1001-1016
  • 50 Liu M, Cheng D, Yan W. Alzheimer's Disease Neuroimaging I. Classification of Alzheimer's Disease by Combination of Convolutional and Recurrent Neural Networks Using FDG-PET Images. Front Neuroinform 2018; 12: 35
  • 51 Hwang D, Kim KY, Kang SK. et al. Improving the Accuracy of Simultaneously Reconstructed Activity and Attenuation Maps Using Deep Learning. J Nucl Med 2018; 59: 1624-1629