Prof. Manhua Liu Profile

Prof. Manhua Liu

at Shanghai Jiao Tong Univ

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Proceedings Article | 9 August 2018 Paper

Hippocampus analysis based on 3D CNN for Alzheimer’s disease diagnosis

Ruoxuan Cui, Manhua Liu

Proceedings Volume 10806, 108065O (2018) https://doi.org/10.1117/12.2503194

KEYWORDS: 3D modeling, 3D image processing, Magnetic resonance imaging, Brain, Shape analysis, Alzheimer's disease, Convolutional neural networks, Feature extraction, Neuroimaging, Image segmentation

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Alzheimer’s disease (AD) is one of the most common brain dementia, which effects human memory, thinking and behavior. It has been proven that hippocampus is an important region related to AD diagnosis. Most of the existing methods on hippocampus analysis are based on the shape and volume analysis of the bilateral hippocampi. However, the 3D structural magnetic resonance images (MRI) can capture more useful information of hippocampus and its adjacent regions. In this paper, we propose a new method based on deep 3D convolutional neural network (3D CNN) for hippocampus analysis using 3D MR images for AD diagnosis. First, two hippocampi are segmented from other regions and the centers of hippocampus regions are calculated. Then, based on each hippocampus center, a local 3D image patch is extracted from the 3D MR image to cover each hippocampus region. Next, a deep 3D CNN model is constructed to extract the hierarchical imaging features for each hippocampus, followed by a softmax layer to generate a class prediction score for AD diagnosis. Finally, the classification is made by combination of the prediction scores from two hippocampi. Our method is evaluated using T1-weighted structural MR images on 231 subjects including 101 AD patients and 130 normal controls (NC) from Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Experimental results show the proposed method achieves an accuracy of 86.98% for classification of AD vs. NC, demonstrating the promising classification performance.

Proceedings Article | 9 August 2018 Paper

Multimodal analysis of structural and functional MRI for schizophrenia diagnosis

Huixiang Zhuang, Yao Li, Ruihao Liu, Chaowei Wu, Manhua Liu

Proceedings Volume 10806, 108065N (2018) https://doi.org/10.1117/12.2503161

KEYWORDS: Magnetic resonance imaging, Brain, Feature selection, Matrices, Neuroimaging, Image classification, Image segmentation, Dimension reduction, Brain diseases, Magnetism

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Schizophrenia (SZ) is one of the important brain diseases. Multimodal magnetic resonance (MR) images provide the important imaging biomarkers to detect the pathological changes in both brain function and anatomy for SZ diagnosis. In this paper, we propose a multi-modal image classification algorithm based on sparse coding and random forest to combine the structural and functional MR brain image analysis for SZ diagnosis. First, the structural and functional MR images are processed to extract the anatomical features and functional connectivity measures for representation. Second, for each modality, sparse coding is used for initial feature selection and the selected features are used as input for random forest (RF) models to calculate a proximity matrix for each modality. Third, the features from the two modalities are combined by linear combination of two proximity matrices into one matrix and the classical multidimensional scaling (MDS) is applied to the proximity matrix for dimensionality reduction. Finally, the reduced matrices are served as inputs for the RF models for multi-modal classification. Our proposed algorithm is tested on the structural and functional MRIs for classification of SZ and healthy controls. Both sparse coding and RF have capability of estimating the potential relationship among various features to reach an ideal group discriminating performance. Experimental results show the effectiveness of the proposed multimodal classification method for SZ diagnosis.

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