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Visualizing MRI Deep Learning Segmentation Algorithms using 3D Printing
Greg Tyler1, Oliver Mathias1* and Andrew Papilion1
1Chapman University, Orange CA, USA
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Abstract:
As the capabilities and roles of Artificial Intelligence (AI) in the medical field are continually expanded, new potential uses, and combinations of technology become viable. This paper highlights a methodology for utilizing AI Magnetic Resonance Imaging (MRI) segmentation networks and 3D printing processes in conjunction for medical diagnosis, planning, and visualization of medical images. We also include promising benefits and potential medical offerings made possible by this system. By training a "U- Net" on the 2019 BraTS dataset, we base our research on an MRI brain lesion segmentation dataset with sustaining performance and world recognition. This network automatically segments novel MRI scans into lesion and non-lesion regions. We pair this network with a 3D printing process that enables us to print fully segmented, 1:1 scale, patient organs aided by AI techniques to better explain cases, test models, and plan for operations. In addition to a clearly outlined process that enables these offerings, we establish a potential trajectory for how these combined tools will continue to revolutionize the ways healthcare professionals interact with patients and their data.
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Keywords: AI Segmentation, 3D Printing, MRI, MRI Visualization
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Received:N/A; Revised:N/A; Accepted:N/A; Published:December 30, 2021
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*Corresponding author; e-mail: mathias@chapman.edu
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Citation:Tyler, G.; Mathias, O.; Papilion, A.Visualizing MRI Deep Learning Segmentation Algorithms using 3D Printing. International Journal of Clinical Medicine and Bioengineering 2021, 1, 37-45. https://doi.org/10.35745/ijcmb2021v01.01.0005
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Copyright: ©
2021
The Author(s). Published with license by IIKII, Singapore. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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