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A review of deep learning models: applications for 3D MRI and CT scans
A review of deep learning models: applications for 3D MRI and CT scans
One of the main differences between medical imaging data and other everyday images is that they are usually 3D, especially when dealing with DICOM series data. DICOM images are composed of multiple 2D slices and are used to scan or represent specific parts of the body
In this article, we will introduce 6 neural network architectures for training depth Learning models to solve problems with 3D medical data
3d U-Net
3D U-Net is a powerful medical image segmentation model that extends the classic U-Net model to 3D segmentation, and consists of encoding path and decoding path
3D U-Net captures contextual information through the encoding path and achieves precise positioning through the decoding path when processing volume images, showing efficient 3D characteristics Processing capabilities
V-Net
V-Net is a 3D convolutional neural network for volumetric image segmentation that uses full-resolution 3D convolutions and therefore more computationally expensive compared to U-Net
HighResNet
This model passes through a series of 3D convolutions with residual connections The cumulative layer is trained end-to-end and can process the entire 3D image simultaneously
EfficientNet3D
Although EfficientNet's 3D improvement is not as good as U-Net or V-Net As widely used for 3D segmentation, it is an option worth considering when computing resources are limited, as it strikes a good balance between computational cost and performance
Attention U-Net
This variant is based on U-Net, which introduces an attention mechanism that enables the network to focus on specific parts of the image that are relevant to the current task
DeepMedic
This 3D CNN uses dual paths, one of which is normal resolution and the other is downsampled input to comprehensively utilize local and greater contextual information
Summary
In this article, we explored some of the deep learning models used in the medical imaging industry for processing 3D MRI and CT scans. These neural networks are designed to receive 3D data as input in order to learn complex features of specific body parts in the DICOM series
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