Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 608232, Singapore.
Cognitive Neuroimaging Centre, Nanyang Technological University, Singapore 636921, Singapore.
Sensors (Basel). 2020 Sep 7;20(18):5097. doi: 10.3390/s20185097.
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.
机器学习、图形处理技术的快速发展以及医学成像数据的可用性，使得深度学习模型在医学领域的应用迅速增加。卷积神经网络（CNN）架构的快速发展加剧了这种情况，医学成像界采用了这些架构来帮助临床医生进行疾病诊断。自 2012 年 AlexNet 的巨大成功以来，CNN 已越来越多地用于医学图像分析，以提高人类临床医生的效率。近年来，三维（3D）CNN 已被用于医学图像的分析。在本文中，我们追溯了 3D CNN 从其机器学习根源发展的历史，简要描述了 3D CNN，并提供了将医学图像输入 3D CNN 之前所需的预处理步骤。我们回顾了在不同医学领域（如分类、分割、检测和定位）使用 3D CNN（及其变体）进行 3D 医学成像分析的重要研究。最后，我们讨论了在医学成像领域（以及一般的深度学习模型）使用 3D CNN 相关的挑战以及该领域未来的可能趋势。
