3D Deep Learning on Medical Images: A Review

By Satya Prakash Singh, Lipo Wang, Sukrit Gupta, Haveesh Goli, Parasuraman Padmanabhan, Bal'azs Guly'as. Italian National Conference on Sensors, 2020

569 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/f0ed174401beb546cf1034fe804b635125dfd52d

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In Artificial Intelligence

In Computer Vision and Pattern Recognition

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Domain Adaptation for Medical Image Analysis: A Survey (2021, unknown): “Compared with 2D CNNs pre-trained on ImageNet, 3D models are usually more powerful to explore medical image features and yield better learning performance [142].”
A review on deep learning in medical image analysis (2021, methodology): “Localization [19] for biological architectures would be a fundamental prerequisite for different medical image investigation initiatives.”
Deep ConvLSTM With Self-Attention for Human Activity Decoding Using Wearable Sensors (2020, background): “CNNs extract features within itself using convolutional operations and are domain-independent with generalization capabilities [22].”