Convolutional Neural Network Learning Versus Traditional Segmentation for the Approximation of the Degree of Defective Surface in Titanium for Implantable Medical Devices-Reference-Cited by-同舟云学术

Convolutional Neural Network Learning Versus Traditional Segmentation for the Approximation of the Degree of Defective Surface in Titanium for Implantable Medical Devices

Published:2019 Issue: Volume: Page:871-882
ISSN:0302-9743
Container-title:Advances in Computational Intelligence
language:
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Author:

Stoean Ruxandra,Stoean Catalin,Samide Adriana,Joya Gonzalo

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-20521-8_71

Reference15 articles.

1. Ahmed, W., Elhissi, A., Jackson, M., Ahmed, E.: Precision machining of medical devices. In: Davim, J.P. (ed.) The Design and Manufacture of Medical Devices, pp. 59–113. Woodhead Publishing Reviews, Mechanical Engineering Series. Woodhead Publishing (2012)

2. Christoph Leyens, M.P.: Titanium and Titanium Alloys - Fundamentals and Applications. Wiley-VCH, Weinheim (2003)

3. Civantos, A., Martínez-Campos, E., Ramos, V., Elvira, C., Gallardo, A., Abarrategi, A.: Titanium coatings and surface modifications: toward clinically useful bioactive implants. ACS Biomater. Sci. Eng. 3(7), 1245–1261 (2017)

4. Damiati, L., et al.: Impact of surface topography and coating on osteogenesis and bacterial attachment on titanium implants. J. Tissue Eng. 9, 2041731418790694 (2018)

5. Edwards, C.: Materials used in medical implants: how is the industry breaking the mould? Verdict Medical Devices (2018). https://www.medicaldevice-network.com/features/materials-used-medical-implants-industry

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