Anatomical Landmark Based Deep Feature Representation for MR Images in Brain Disease Diagnosis
Year: 2018
Type: article
Abstract: Most automated techniques for brain disease diagnosis utilize hand-crafted (e.g., voxel-based or region-based) biomarkers from structural magnetic resonance (MR) images as feature representations. However, these hand-crafted features are usually high-dimensional or require regions-of-interest defined by experts. Also, because of possibly heterogeneous property between the hand-crafted features and the subsequent model, existing methods may lead to sub-optimal performances in brain disease diagnosis. In this paper, we propose a landmark-based deep feature learning (LDFL) framework to automatically extract patch-based representation from MRI for automatic diagnosis of Alzheimer's disease. We first identify discriminative anatomical landmarks from MR images in a data-driven manner, and then propose a convolutional neural network for patch-based deep feature learning. We have evaluated the proposed method on subjects from three public datasets, including the Alzheimer's disease neuroimaging initiative (ADNI-1), ADNI-2, and the minimal interval resonance imaging in alzheimer's disease (MIRIAD) dataset. Experimental results of both tasks of brain disease classification and MR image retrieval demonstrate that the proposed LDFL method improves the performance of disease classification and MR image retrieval.
Institution: University of North Carolina at Chapel Hill
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: green
Awards: UC Davis Alzheimer's Disease Core Center, Analyzing Large-Scale Neuroimaging Data in Alzheimer's Disease, Infant Brain Measurement and Super-Resolution Atlas Construction, "MR Morphometrics and Cognitive Decline Rate in Large-Scale Aging Studies", Assessing Large-scale Brain Connectivities in Mild Cognitive Impairment, Quantifying Brain Abnormality by Multimodality Neuroimage Analysis, Longitudinal Mapping of Human Brain Development in the First Years of Life, Fast, Robust Analysis of Large Population Data, Development of Robust Brain Measurement Tools Informed by Ultrahigh Field 7T MRI, G9000108235
OpenAlex ID: W2783188875