To classify Alzheimer’s Disease from 3D structural MRI data

1. Pattern Recognition Laboratory
 Goal
 To classify the Alzheimer’s Disease from 3D structural MRI data
 Motivation
 Improving the diagnosis rate of Alzheimer’s Disease
 Most of AD Patients at a early stage classified as amnestic MCI
• AD vs. MCI vs. NC
– AD : Alzheimer’s Disease
– MCI : Mild Cognitive Impairment
– NC : Normal Condition
Introduction
Step 1
MRI input image
(256×256×128)
2D
Convolution
Neural
Network
Step 2 Step 3
Trained
model
evaluation
Pre-
processing
Preprocessed
input image
(256× 256 ×256)
3 class classification
Overall Framework
Prediction
2D
Convolution
Neural
Network
MRI Image

2. Pattern Recognition Laboratory
Dataset
 Dataset
 Using Alzheimer’s Disease Neuroimaging Initiative(ADNI) database
• AD: 22 image, MCI: 22 image, NC: 22 image (Total: 66 image)
 Data Preprocessing
References : H.-l. Suk, S.-W Lee, D. Shen, and The Alzheimer’s Disease Neuroiamging
Initiative,”PMC, Vol. 221, No. 5, 2016, pp. 2569-2587.
Training(12) Validation(5) Test(5)
 Follow the paper
[H. Suk et al., 2016]
 Using Medical Image
Processing Analysis
and Visualization
(MIPAV) software
(256X256X128)
AC-PC
correction Resampling
Rotate Skull stripping
N3 algorithm
(256X256X256)Raw data (256X256X128)
(256X256X256) (256X256X256) (256X256X256)
Process of Data Preprocessing

3. Pattern Recognition Laboratory
Network Model
 2D Convolutional Neural Network (CNN)
 Architecture: AlexNet
• 5 Convolutional layers, 2 Fully Connected layers
 Input : preprocessed MRI data (126X126X126)
 Parameters
• Batch size : 1
• Learning Rate : 0.000004
 Loss function
• Cross entropy
 Optimization
• Adam optimization
96X64X64 256X15X15 384X15X15 256X7X7
output
Input image
126X126X126
Conv1
Conv2
Conv3 Conv4
Conv5
FC1 FC2
1024 1024
Softmax
(11 X 11)
(5 X 5)
(3 X 3) (3 X 3) (3 X 3)
Architecture of Network model

4. Pattern Recognition Laboratory
 Experiments
 Preprocessed input data
 Experiment Data Analysis (MRI voxel)
Result(1/2)
AD NC
Mean Standard Deviation Variance
Train
AD 27.99 115.74 19424.79
MCI 30.41 114.26 13629.33
NC 24.61 96.45 10139.13
Validation
AD 44.68 165.22 36942.11
MCI 52.92 159.02 27099.43
NC 56.10 170.96 30744.72
Test
AD 25.40 97.14 9947.93
MCI 24.63 90.60 8266.77
NC 19.53 79.93 6406.18
MCI
 Feature maps

5. Pattern Recognition Laboratory
 Experiment Model Result
 Conclusion
 Classification of MCI vs. NC is better than that of AD vs. MCI
 Cannot use in real life because of Bad Performance
• Using only 66 MRI data -> Increase the number of data
• Unsure data preprocessing -> Correct the flaw of preprocessing
• Inefficient feature extraction -> Find the methods of efficient feature extraction
Result(2/2)
Classification
Positive
Predictive
Value
(PPV)
Negative
Predictive
Value
(NPV)
F1 Score
AD vs. MCI vs. NC 0.5 0.53 0.37
MCI vs. NC 0.75 0.66 0.66
AD vs. MCI 0.5 0.5 0.28