The rapid advancements in machine learning, graphics processing technologies
and availability of medical imaging data has led to a rapid increase in 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
analysis of medical images. In this paper, we trace the history of how the 3D
CNN was developed from its machine learning roots, give a brief mathematical
description of 3D CNN and 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.Comment: 13 pages, 4 figures, 2 table
