Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Smart Grid, Future Innovation and Investment Opportunities Kapitel lesen Erstes Kapitel lesen

2020 | OriginalPaper | Buchkapitel

Using Augmented Reality and Machine Learning in Radiology

verfasst von : Lucian Trestioreanu, Patrick Glauner, Jorge Augusto Meira, Max Gindt, Radu State

Erschienen in: Innovative Technologies for Market Leadership

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Surgeries are one of the main cost factors of health care systems. To reduce the costs related to diagnoses and surgeries, we propose a system for automated segmentation of medical images in order to segment body parts like liver or lesions. The model is based on convolutional neural networks, for which we show promising results on real computed tomography scans. The deep learning algorithm is part of a larger system that aims to support doctors by visualizing the segments in a Microsoft HoloLens, an augmented reality device. Our approach allows doctors to intuitively look at and interact with the holographic data rather than using 2D screens, enabling them to provide better health care. Both the machine learning algorithm and the visualization utilize high-performance GPUs in order to enable doctors to interact efficiently with our system.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Innovative Technologies in the Ageing Population: Breaking the Boundaries
Nächstes Kapitel Digitalization in Mechanical Engineering
Literatur
Aljabar, P., Heckemann, R. A., Hammers, A., Hajnal, J.V., & Rueckert, D. (2009). Multi-atlas based segmentation of brain images: Atlas selection and its effect on accuracy. NeuroImage, 46(3), 726–738.CrossRef
Bellver, M., Maninis, K.-K., Pont-Tuset, J., Giró-i-Nieto, X., Torres, J., & Van Gool, L. (2017). Detection-aided liver lesion segmentation using deep learning. arXiv:1711.11069v1.
Beydoun, A., Gupta, V., & Siegel, E. (2017). DICOM to 3D Holograms: Use Case for Augmented Reality in Diagnostic and Interventional Radiology. In SIIM scientific session posters and demonstrations.
Calhoun, P., Kuszyk, B., Heath, D., Carley, J., & Fishman, E. (1999). Three-dimensional volume rendering of spiral CT data: Theory and method. Radiographics: A Review Publication of the Radiological Society of North America, Inc., 19(3), 745–764.CrossRef
Carmigniani, J., & Furht, B. (2011). Augmented reality: An overview. In B. Furht (Ed.) Handbook of Augmented Reality. New York: Springer.
Christ, P., Ettlinger, F., Grun, F., Elshaera, M. E. A., Lipkova, J., Schlecht, S., et al. (2017). Automatic liver and tumor segmentation of CT and MRI volumes using cascaded fully convolutional neural networks. arXiv 1702.05970.
Douglas, D., Venets, D., Wilke, C., Gibson, D., Liotta, L., Petricoin, E., et al. (2018). Augmented reality and virtual reality: initial successes in diagnostic radiology. In State of the art virtual reality and augmented reality knowhow. London: IntechOpen Limited.
Douglas, D., Wilke, C., Gibson, D., Boone, J., & Wintermark, M. (2017). Augmented reality: advances in diagnostic imaging. Multimodal Technologies and Interact, 1, 29.CrossRef
Fishman, E., Ney, D., Heath, D., Corl, F., Horton, K., & Johnson, P. (2006). Volume rendering versus maximum intensity projection in CT angiography: What works best, when, and why. Radiographics: A Review Publication of the Radiological Society of North America, Inc., 26(3), 905–922.CrossRef
Glauner, P., Migliosi, A., Meira, J. A., Valtchev, P., State, R., & Bettinger, B. (2017). Is big data sufficient for a reliable detection of non-technical losses? In 2017 19th International Conference on Intelligent System Application to Power Systems (ISAP).
Glauner, P., State, R., Valtchev, P., & Duarte, D. (2018a). On the reduction of biases in big data sets for the detection of irregular power usage. In Proceedings of the 13th International FLINS Conference on Data Science and Knowledge Engineering for Sensing Decision Support (FLINS 2018).
Glauner, P., Valtchev, P., & State, R. (2018b). Impact of biases in big data. In Proceedings of the 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018).
Gotra, A., Sivakumaran, L., Chartrand, G., Vu, K. N., Vandenbroucke-Menu, F., Kauffmann, C., et al. (2017). Liver segmentation: indications, techniques and future directions. Insights Imaging, 8(4), 377–392.CrossRef
Heath, D., Corl, F., Horton, K., Fishman, E., & Johnson, P. (2006). Volume rendering versus maximum intensity projection in CT angiography: What works best, when, and why. Radiographics: A Review Publication of the Radiological Society of North America, Inc., 26(3), 905–922.CrossRef
Hohne, K., Bomans, M., Tiede, U., & Riemer, M. (1988). Display of multiple 3D-objects using the generalized voxel-model. In Medical Imaging II. Newport Beach: SPIE.
Hoogi, A., Beaulieu, C. F., Cunha, G. M., Heba, E., Sirlin, C. B., Napel, S., et al. (2017a). Adaptive local window for level set segmentation of CT and MRI liver lesions. Medical Image Analysis, 37, 46–55.CrossRef
Hoogi, A., Lambert, J. W., Zheng, Y., Comaniciu, D., & Rubin, D. L. (2017b). A fully automated pipeline for detection and segmentation of liver lesions and pathological lymph nodes. arXiv 1703.06418.
Johnson, P., Heath, D., Kuszyk, B., & Fishman, E. (1996). CT angiography with volume rendering: Advantages and applications in splanchnic vascular imaging. Radiology, 200(2), 564–568.CrossRef
Jung, T. (2019). The power of AR and VR for business. In M. Claudia, T. Dieck, & T. Jung (Eds.) Augmented reality and virtual reality. Berlin: Springer.
Maninis, K.-K., Pont-Tuset, J., Arbelaez, P., Van Gool, L. (2016). Deep retinal image understanding. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 140–148). Berlin: Springer.
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 (pp. 234–241). Berlin: Springer.
Rothman, D. (2018). Artificial intelligence by example: Develop machine intelligence from scratch using real artificial intelligence use cases. Birmingham: Packt Publishing.
Metadaten
Titel
Using Augmented Reality and Machine Learning in Radiology
verfasst von
Lucian Trestioreanu
Patrick Glauner
Jorge Augusto Meira
Max Gindt
Radu State
Copyright-Jahr
2020
Buch
Innovative Technologies for Market Leadership

Print ISBN: 978-3-030-41308-8

Electronic ISBN: 978-3-030-41309-5

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-3-030-41309-5_8