Biomarker for early detection of alzheimer disease
The present disclosure relates to a method for providing biomarker for early detection of Alzheimer's Disease (AD), and particularly to a method that is able to enhance the accuracy of predicting AD from Mild Cognitive Impairment (MCI) patients using the Hippocampus magnetic resonance imaging (MRI) scans and Mini-Mental State Examination (MMSE) data. The providing MRI images containing the anatomical structure of Hippocampus biomarker and MMSE data as a training data set; training a processor using the training data set, and the training comprising acts of receiving MRI images and MMSE data as a testing data set from a target; and classifying the test data by the trained processor to include aggregating predictions.
1. A method of providing biomarker for early detection of Alzheimer's Disease, comprising:
providing magnetic resonance imaging (MRI) images containing the anatomical structure of Hippocampus and Mini-Mental State Examination (MMSE) data as a training data set;
training a processor using the training data set, and the training comprising acts of
proceeding an MRI image preprocessing to determine volume of each Hippocampus in MRI images, and segmenting each Hippocampus into sections;
determining surface areas for each section of each Hippocampus in MRI images;
determining an average of Ratio of Principal Curvature (RPC) for each section of each Hippocampus, wherein the average of RPC for each section of each Hippocampus is a ratio of SUM(maximum curvature) and SUM(minimum curvature) for each section of each Hippocampus; and
selecting candidate parameters as inputs to iteratively train an iterative neural network in the processor, wherein the candidate parameters comprise the volume of Hippocampus, the surface area and the average of the RPC for each section of Hippocampus, and scores of MMSE data;
receiving MRI images and MMSE data as a testing data set from a target; and
classifying the testing data set by the trained processor to include aggregating predictions.
2. The method as claimed in claim 1 , wherein the act of proceeding an MRI image preprocessing to determine volume of each Hippocampus in MRI images and segmenting each Hippocampus into sections, comprises an intensity normalization, linear stereotaxic registration, creating linear mask, linear classification and linear segmentations.
3. The method as claimed in claim 2 , wherein the segmentations of the Hippocampus are alveus, parasubiculum, presubiculum, subiculum, CA1, CA2/3, CA4, GC-DG, HATA, fimbria, molecular layer, Hippocampus fissure and Hippocampus tail.
4. The method as claimed in claim 3 , wherein the candidate parameter comprises values of Hippocampus volume, Subiculum surface area, CA1 surface area, CA3 surface area, an average of RPC for Subiculum, an average of RPC for CA1, and an average of RPC for CA3.
5. The method as claimed in claim 1 , wherein the candidate parameter comprises scores of orientations, attention, recall and language in the MMSE data.
6. The method as claimed in claim 1 , wherein the act of classifying the MRI images and MMSE data by the trained processor are proceed in different iterative neural networks.
7. The method as claimed in claim 1 , wherein the acts of preprocessing and segmenting the MRI images are performed by a computer using FreeSurfer.
8. The method as claimed in claim 1 , wherein the act of determining surface areas for each section of each Hippocampus in MRI images comprises acts of reconstructing the each identified Hippocampus with sections to build a 3 Dimensions (3D) Hippocampus model, smoothing the surface area of the 3D Hippocampus model; and calculating the surface areas of each section of Hippocampus.
9. The method as claimed in claim 8 , wherein the 3D Hippocampus model is built by Marching cubes.
10. The method as claimed in claim 8 , wherein the act of smoothing the surface area of the 3D Hippocampus model is achieved by Laplacian smoothing.