A novel network architecture combining central-peripheral deviation with image-based convolutional neural networks for diffusion tensor imaging studies

Brain imaging research is a very challenging topic due to complex structure and lack of explicitly identifiable features in the image. With the advancement of magnetic resonance imaging (MRI) technologies, such as diffusion tensor imaging (DTI), developing classification methods to improve clinical...

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Bibliographic Details
Published in:Journal of applied statistics 2023-12, Vol.50 (16), p.3294-3311
Main Authors: Park, Soyun, Yu, Jihnhee, Woo, Hwa-Hyoung, Park, Chun Gun
Format: Article
Language:English
Subjects:
Artificial neural networks
Brain
Classification
Concentric circle pooling
convolutional neural network
Data analysis
Deviation
diffusion tensor image
Head injuries
Image classification
Magnetic resonance imaging
Magnets
Mathematical analysis
Medical imaging
multi-layer perceptron
Multilayer perceptrons
Neural networks
Statistical methods
Tensors
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Summary:Brain imaging research is a very challenging topic due to complex structure and lack of explicitly identifiable features in the image. With the advancement of magnetic resonance imaging (MRI) technologies, such as diffusion tensor imaging (DTI), developing classification methods to improve clinical diagnosis is crucial. This paper proposes a classification method for DTI data based on a novel neural network strategy that combines a convolutional neural network (CNN) with a multilayer neural network using central-peripheral deviation (CPD), which reflects diffusion dynamics in the white matter by spatially evaluating the deviation of diffusion coefficients between the inner and outer parts of the brain. In our method, a multilayer perceptron (MLP) using CPD is combined with the final layers for classification after reducing the dimensions of images in the convolutional layers of the neural network architecture. In terms of training loss and the classification error, the proposed classification method improves the existing image classification with CNN. For real data analysis, we demonstrate how to process raw DTI image data sets obtained from a traumatic brain injury study (MagNeTS) and a brain atlas construction study (ICBM), and apply the proposed approach to the data, successfully improving classification performance with two age groups.
ISSN:0266-4763
1360-0532
DOI:10.1080/02664763.2022.2108386