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Deep learning based imaging data completion for improved brain disease diagnosis

Authors

Li, Rongjian, Zhang, Wenlu, Suk, Heung-Il, Wang, Li, Li, Jiang, Shen, Dinggang, Ji, Shuiwang

Journal

Medical Image Computing And Computer-Assisted Intervention: MICCAI ... International Conference On Medical Image Computing And Computer-Assisted Intervention, Volume: 17, No.: Pt 3, Pages.: 305-312

Year of Publication

2014

Abstract

Combining multi-modality brain data for disease diagnosis commonly leads to improved performance. A challenge in using multimodality data is that the data are commonly incomplete; namely, some modality might be missing for some subjects. In this work, we proposed a deep learning based framework for estimating multi-modality imaging data. Our method takes the form of convolutional neural networks, where the input and output are two volumetric modalities. The network contains a large number of trainable parameters that capture the relationship between input and output modalities. When trained on subjects with all modalities, the network can estimate the output modality given the input modality. We evaluated our method on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, where the input and output modalities are MRI and PET images, respectively. Results showed that our method significantly outperformed prior methods.;

Keywords

algorithms, alzheimer disease, database, diagnosis, humans, image enhancement, image interpretation computerassisted, magnetic resonance imaging, methods, multimodal imaging, neural networks computer, neuroimaging, of, pattern recognition automated, reproducibility of results, sensitivity and specificity, use

Countries of Study

USA

Types of Dementia

Alzheimer’s Disease

Types of Study

Other

Type of Interventions

Diagnostic Target Identification

Diagnostic Targets

Neuroimaging (e.g. MRI, PET, CAT etc.)