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Endo-Praxis 2020; 36(01): 30-43
DOI: 10.1055/a-0983-8972
Originalarbeit
© Georg Thieme Verlag KG Stuttgart · New York

Künstliche Intelligenz in der Endoskopie: Neuronale Netze und maschinelles Sehen – Techniken und Perspektiven

Rüdiger Schmitz
1   Department for Interdisciplinary Endoscopy, University Hospital Hamburg-Eppendorf
2   Institute of Anatomy and Experimental Morphology, University Hospital Hamburg-Eppendorf
3   DAISYlab, Forschungszentrum Medizintechnik Hamburg, Hamburg, Germany
,
René Werner
3   DAISYlab, Forschungszentrum Medizintechnik Hamburg, Hamburg, Germany
4   Institute of Computational Neuroscience, University Hospital Hamburg-Eppendorf
,
Thomas Rösch
1   Department for Interdisciplinary Endoscopy, University Hospital Hamburg-Eppendorf
› Author Affiliations
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Publication History

Publication Date:
11 February 2020 (online)

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Zusammenfassung

Künstliche neuronale Netze als Methoden der künstlichen Intelligenz (KI) können der Endoskopie neue Möglichkeiten eröffnen, etwa im Sinne einer automatischen Polypenerkennung oder der präzisen Vorhersage des histopathologischen Befunds einer Läsion anhand ihres endoskopischen Bildes. Während erste Versuche tatsächlich ein weitreichendes Potenzial erahnen lassen, leiten sich öffentliche und medial transportierte Erwartungen häufig mehr von einer abstrakten Faszination als von der detaillierten Funktionsweise der Methoden ab. Dieser Artikel soll anhand einer selektiven Literaturübersicht ein intuitives Verständnis der Methoden vermitteln und helfen, die Lücke zwischen Funktion und Faszination zu schließen, um Potenzial und Grenzen dieser Techniken im Bereich der Endoskopie realistisch abschätzen zu können.

Mit ihrem Erfolg bei der maschinellen Klassifikation von Bildern haben insbesondere „tiefe neuronale Netze“ der KI nach jahrzehntelanger Forschung zu rasant anwachsendem Interesse verholfen. Wir umreißen kurz die diesbezüglichen Entwicklungen und die Gründe für ihre Bedeutung weit über die Informatik hinaus. Durch den Vergleich von maschinellem und menschlichem Sehen wird ein Verständnis der detaillierten Funktionsweise dieser Methoden und ihrer Erfolge bei Seh-Aufgaben vermittelt. Darauf aufbauend analysieren wir die Funktionsweise jüngst demonstrierter Anwendungen in Hinblick auf methodische Perspektiven und Grenzen, die Aussagekraft bisher erbrachter Leistungsnachweise und die Notwendigkeit weiterer Tests. Zudem geben wir einen Eindruck von weiteren, konkret absehbaren Einzelanwendungen und besprechen, welchen Charakter diese dem Einsatz der künstlichen Intelligenz in der Endoskopie insgesamt geben könnten.

Schlüsselwörter

Endoskopie - künstliche Intelligenz - deep learning - tiefe neuronale Netze - computer-aided detection - computer-aided diagnosis - maschinelles Sehen
 

  • Literatur

  • 1 Misawa M, Kudo S, Mori Y. et al. Artificial Intelligence-Assisted Polyp Detection for Colonoscopy: Initial Experience. Gastroenterology 2018; 154: 2027-2029.e3
    CrossrefPubMedGoogle Scholar
  • 2 Russakovsky O, Deng J, Su H. et al. ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision 2015; 115: 211-252
    CrossrefGoogle Scholar
  • 3 Rosenblatt F. The Perceptron. A Perceiving and Recocgnizing Automaton. Cornell Aeronautical Laboratory; 1957 Technical Report 85-460-1
    Google Scholar
  • 4 Hubel DH, Wiesel TN. Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. The Journal of Physiology 1962; 160: 106-154
    CrossrefPubMedGoogle Scholar
  • 5 Desimone R, Albright T, Gross C. et al. Stimulus-selective properties of inferior temporal neurons in the macaque. The Journal of Neuroscience 1984; 4: 2051-2062
    CrossrefPubMedGoogle Scholar
  • 6 Zeiler MD, Fergus R. Visualizing and Understanding Convolutional Networks. arXiv:13112901 [cs] 2013
    Google Scholar
  • 7 Gross CG. Genealogy of the “Grandmother Cell”. The Neuroscientist 2002; 8: 512-518
    CrossrefPubMedGoogle Scholar
  • 8 Photographs by Maximilian Groß (Grand mother, private photo, by courtesy of the photographer), Arne J. de Vries (tree, private photo, by courtesy of the photographer), Rüdiger Schmitz (Zurich Opera House) and Thomas Rösch (endoscopy images).
    Google Scholar
  • 9 Minsky M, Papert S. Perceptrons: An Introduction to Computational Geometry. Cambridge, MA, USA: MIT Press; 1969
    Google Scholar
  • 10 LeCun Y. Gradient-Based Learning Applied to Document Recognition. Proceedings of the IEEE 1998; 86: 47
    Google Scholar
  • 11 LeCun Y, Fu Jie H, Bottou L. Learning methods for generic object recognition with invariance to pose and lighting. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004. Washington, DC, USA: IEEE; 2004: 97-104
    Google Scholar
  • 12 Krizhevsky A, Sutskever I, Hinton GE. ImageNet Classification with Deep Convolutional Neural Networks. In: Pereira F, Burges CJC, Bottou L. et al., Hrsg. Advances in Neural Information Processing Systems 25. Curran Associates, Inc; 2012: 1097-1105
    Google Scholar
  • 13 LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015; 521: 436-444
    Google Scholar
  • 14 Ciresan D, Giusti A, Gambardella LM. et al. Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images. In: Pereira F, Burges CJC, Bottou L. et al., Hrsg. Advances in Neural Information Processing Systems 25. Curran Associates, Inc; 2012: 2843-2851
    Google Scholar
  • 15 Redmon J, Divvala S, Girshick R. et al. You Only Look Once: Unified, Real-Time Object Detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Las Vegas, NV, USA: IEEE; 2016: 779-788
    Google Scholar
  • 16 Liu W, Anguelov D, Erhan D. et al. SSD: Single Shot MultiBox Detector. arXiv:151202325 [cs] 2016; 9905: 21–37
    Google Scholar
  • 17 Long J, Shelhamer E, Darrell T. Fully Convolutional Networks for Semantic Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2015: 3431-3440
    Google Scholar
  • 18 Ronneberger O, Fischer P, Brox T. U-Net: Convolutional Networks for Biomedical Image Segmentation. In: Navab N, Hornegger J, Wells WM. et al., Hrsg. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. Cham: Springer International Publishing; 2015: 234-241
    Google Scholar
  • 19 Badrinarayanan V, Kendall A, Cipolla R. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 2017; 39: 2481-2495
    CrossrefPubMedGoogle Scholar
  • 20 Hassan C, Senore C, Radaelli F. et al. Full-spectrum (FUSE) versus standard forward-viewing colonoscopy in an organised colorectal cancer screening programme. Gut 2017; 66: 1949-1955
    PubMedGoogle Scholar
  • 21 Urban G, Tripathi P, Alkayali T. et al. Deep Learning Localizes and Identifies Polyps in Real Time with 96% Accuracy in Screening Colonoscopy. Gastroenterology 2018; 155: 1069-1078.e8
    CrossrefPubMedGoogle Scholar
  • 22 Wang P, Xiao X, Glissen Brown JR. et al. Development and validation of a deep-learning algorithm for the detection of polyps during colonoscopy. Nature Biomedical Engineering 2018; 2: 741-748
    CrossrefPubMedGoogle Scholar
  • 23 Wang P, Berzin TM, Glissen Brown JR. et al. Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study. Gut 2019; DOI: gutjnl-2018-317500.
    CrossrefPubMedGoogle Scholar
  • 24 Hirasawa T, Aoyama K, Tanimoto T. et al. Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images. Gastric Cancer 2018; 21: 653-660
    CrossrefPubMedGoogle Scholar
  • 25 Shichijo S, Nomura S, Aoyama K. et al. Application of Convolutional Neural Networks in the Diagnosis of Helicobacter pylori Infection Based on Endoscopic Images. EBioMedicine 2017; 25: 106-111
    CrossrefPubMedGoogle Scholar
  • 26 Byrne MF, Chapados N, Soudan F. et al. Real-time differentiation of adenomatous and hyperplastic diminutive colorectal polyps during analysis of unaltered videos of standard colonoscopy using a deep learning model. Gut 2017; DOI: gutjnl-2017-314547.
    CrossrefPubMedGoogle Scholar
  • 27 Mori Y, Kudo S, Misawa M. et al. Real-Time Use of Artificial Intelligence in Identification of Diminutive Polyps During Colonoscopy: A Prospective Study. Annals of Internal Medicine 2018; 169: 357
    CrossrefPubMedGoogle Scholar
  • 28 Mori Y, Kudo S, Misawa M. et al. Simultaneous detection and characterization of diminutive polyps with the use of artificial intelligence during colonoscopy. VideoGIE 2019; 4: 7-10
    CrossrefPubMedGoogle Scholar
  • 29 Horie Y, Yoshio T, Aoyama K. et al. The diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks. Gastrointestinal Endoscopy 2019; 89: 25-32
    CrossrefPubMedGoogle Scholar
  • 30 Săftoiu A, Vilmann P, Gorunescu F. et al. Efficacy of an Artificial Neural Network – Based Approach to Endoscopic Ultrasound Elastography in Diagnosis of Focal Pancreatic Masses. Clinical Gastroenterology and Hepatology 2012; 10: 84-90.e1
    CrossrefPubMedGoogle Scholar
  • 31 Shibata J, Ozawa T, Ishihara S. et al. Novel computer-aided diagnosis system using convolutional neural networks for endoscopic disease activity in patiens with ulcerative colitis. Oral presentation at the United European Gastroenterology Week (UEGW). Wien: 2018
    Google Scholar
  • 32 Mendel R, Ebigbo A, Probst A. et al. Barrett’s Esophagus Analysis Using Convolutional Neural Networks. In: Maier-Hein, geb. Fritzsche KH, Deserno, geb. Lehmann TM, Handels H, Tolxdorff T, Hrsg. Bildverarbeitung für die Medizin 2017. Berlin, Heidelberg: Springer; 2017: 80-85
    CrossrefGoogle Scholar
  • 33 de Groof J, van der Putten J, van der Sommen F. et al. Tu2011 – The Argos Project: Evaluation of Results of a Clinicallyinspired Algorithm vs. a Deep Learning Algorithm for the Detection and Delineation of Barrett’s Neoplasia. Gastroenterology 2018; 154: S-1368
    Google Scholar
  • 34 de Groof J, van der Putten J, Struyvenberg MR. et al. The Argos Project II: The Development Of An Endoscopic Deep Learning Computer Aided Detection System For Barrett’s Neoplasia. Oral presentation at the United European Gastroenterology Week (UEGW) Wien: 2018
    Google Scholar
  • 35 Ebigbo A, Mendel R, Probst A. et al. Computer-aided diagnosis using deep learning in the evaluation of early oesophageal adenocarcinoma. Gut 2018; DOI: gutjnl-2018-317573.
    CrossrefPubMedGoogle Scholar
  • 36 Miao S, Wang ZJ, Liao R. A CNN Regression Approach for Real-Time 2D/3D Registration. IEEE Transactions on Medical Imaging 2016; 35: 1352-1363
    CrossrefPubMedGoogle Scholar
  • 37 de Vos BD, Berendsen FF, Viergever MA. et al. End-to-End Unsupervised Deformable Image Registration with a Convolutional Neural Network. In: Cardoso MJ, Arbel T, Carneiro G. et al., Hrsg. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support. DLMIA 2017, ML-CDS 2017. Cham: Springer International Publishing; 2017: 204-212
    Google Scholar
  • 38 Rohé MM, Datar M, Heimann T. et al. SVF-Net: Learning Deformable Image Registration Using Shape Matching. In: Descoteaux M, Maier-Hein L, Franz A. et al., Hrsg. Medical Image Computing and Computer Assisted Intervention − MICCAI 2017. Cham: Springer International Publishing; 2017: 266-274
    Google Scholar
  • 39 Sentker T, Madesta F, Werner R. GDL-FIRE4D: Deep Learning-Based Fast 4D CT Image Registration. In: Frangi AF, Schnabel JA, Davatzikos C. et al., Hrsg. Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. Cham: Springer International Publishing; 2018: 765-773
    Google Scholar
  • 40 Turan M, Almalioglu Y, Araujo H. et al. Deep EndoVO: A recurrent convolutional neural network (RCNN) based visual odometry approach for endoscopic capsule robots. Neurocomputing 2018; 275: 1861-1870
    CrossrefGoogle Scholar
  • 41 Chen H, Ni D, Qin J. et al. Standard Plane Localization in Fetal Ultrasound via Domain Transferred Deep Neural Networks. IEEE Journal of Biomedical and Health Informatics 2015; 19: 1627-1636
    CrossrefPubMedGoogle Scholar
  • 42 Schachschal G, Mayr M, Treszl A. et al. Endoscopic versus histological characterisation of polyps during screening colonoscopy. Gut 2014; 63: 458-465
    CrossrefPubMedGoogle Scholar
  • 43 Ladabaum U, Fioritto A, Mitani A. et al. Real-Time Optical Biopsy of Colon Polyps With Narrow Band Imaging in Community Practice Does Not Yet Meet Key Thresholds for Clinical Decisions. Gastroenterology 2013; 144: 81-91
    CrossrefPubMedGoogle Scholar
  • 44 Chen PJ, Lin MC, Lai MJ. et al. Accurate Classification of Diminutive Colorectal Polyps Using Computer-Aided Analysis. Gastroenterology 2018; 154: 568-575
    CrossrefPubMedGoogle Scholar
  • 45 Hochreiter S, Schmidhuber J. Long Short-Term Memory. Neural Computation 1997; 9: 1735-1780
    CrossrefPubMedGoogle Scholar
  • 46 Fernández S, Graves A, Schmidhuber J. An Application of Recurrent Neural Networks to Discriminative Keyword Spotting. In: de Sá JM, Alexandre LA, Duch W. et al., Hrsg. Artificial Neural Networks – ICANN 2007. Berlin, Heidelberg: Springer; 2007: 220-229
    Google Scholar
  • 47 Schmitz R, Krause J, Krech T. et al. Virtual Endoscopy Based on 3-Dimensional Reconstruction of Histopathology Features of Endoscopic Resection Specimens. Gastroenterology 2018; 154: 1234-1236.e4
    CrossrefPubMedGoogle Scholar
  • 48 Nguyen A, Yosinski J, Clune J. Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Boston, MA, USA: IEEE; 2015: 427-436
    Google Scholar
  • 49 Szegedy C, Zaremba W, Sutskever I. et al. Intriguing properties of neural networks. arXiv:13126199 [cs] 2013
    Google Scholar
  • 50 Selvaraju RR, Cogswell M, Das A. et al. Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization. In: 2017 IEEE International Conference on Computer Vision (ICCV) Venice: IEEE; 2017: 618-626
    Google Scholar
  • 51 Vinsard DG, Mori Y, Misawa M. et al. Quality Assurance of Computer-Aided Detection and Diagnosis in Colonoscopy. Gastrointestinal Endoscopy 2019; DOI: 10.1016/j.gie.2019.03.019.
    CrossrefPubMedGoogle Scholar