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Review

3D Deep Learning on Medical Images: A Review

1
Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 608232, Singapore
2
Cognitive Neuroimaging Centre, Nanyang Technological University, Singapore 636921, Singapore
3
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore
4
School of Computer Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore
5
Department of Clinical Neuroscience, Karolinska Institute, 17176 Stockholm, Sweden
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(18), 5097; https://doi.org/10.3390/s20185097
Received: 10 July 2020 / Revised: 31 August 2020 / Accepted: 3 September 2020 / Published: 7 September 2020
(This article belongs to the Special Issue Machine Learning for Biomedical Imaging and Sensing)
The rapid advancements in machine learning, graphics processing technologies and the availability of medical imaging data have led to a rapid increase in the use of deep learning models in the medical domain. This was exacerbated by the rapid advancements in convolutional neural network (CNN) based architectures, which were adopted by the medical imaging community to assist clinicians in disease diagnosis. Since the grand success of AlexNet in 2012, CNNs have been increasingly used in medical image analysis to improve the efficiency of human clinicians. In recent years, three-dimensional (3D) CNNs have been employed for the analysis of medical images. In this paper, we trace the history of how the 3D CNN was developed from its machine learning roots, we provide a brief mathematical description of 3D CNN and provide the preprocessing steps required for medical images before feeding them to 3D CNNs. We review the significant research in the field of 3D medical imaging analysis using 3D CNNs (and its variants) in different medical areas such as classification, segmentation, detection and localization. We conclude by discussing the challenges associated with the use of 3D CNNs in the medical imaging domain (and the use of deep learning models in general) and possible future trends in the field. View Full-Text
Keywords: 3D convolutional neural networks; 3D medical images; classification; segmentation; detection; localization 3D convolutional neural networks; 3D medical images; classification; segmentation; detection; localization
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MDPI and ACS Style

Singh, S.P.; Wang, L.; Gupta, S.; Goli, H.; Padmanabhan, P.; Gulyás, B. 3D Deep Learning on Medical Images: A Review. Sensors 2020, 20, 5097. https://doi.org/10.3390/s20185097

AMA Style

Singh SP, Wang L, Gupta S, Goli H, Padmanabhan P, Gulyás B. 3D Deep Learning on Medical Images: A Review. Sensors. 2020; 20(18):5097. https://doi.org/10.3390/s20185097

Chicago/Turabian Style

Singh, Satya P., Lipo Wang, Sukrit Gupta, Haveesh Goli, Parasuraman Padmanabhan, and Balázs Gulyás. 2020. "3D Deep Learning on Medical Images: A Review" Sensors 20, no. 18: 5097. https://doi.org/10.3390/s20185097

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