DaTSCAN Image Database. Experiments. Comparison. Conclusions and ..... Data augmentation: Mirroring the images to account for bilateral differences.
A 3D Convolutional Neural Network Approach for the Diagnosis of Parkinson’s Disease.
7th. International Work-Conference on the Interplay between Natural and Artificial Computation, IWINAC-2017 June 20, 2017 F.J. Martinez-Murcia1 , A. Ortiz2 , J. M. Górriz1 , J. Ramírez1 , F. Segovia1 , D. Salas-Gonzalez1 , D. Castillo-Barnes1 and I.A. Illán3 1
Dept. of Signal Theory, Networking and Communications. Universidad de Granada, Spain 2 Department of Communications Engineering. Universidad de Malaga, Spain. 3
Department of Scientific Computing. Florida State University, USA.
SiPBA Signal Processing and Biomedical Applications http://sipba.ugr.es
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Table of Contents
A 3D Convolutional Neural Network Approach for the Diagnosis of Parkinson’s Disease.
Introduction
F.J. Martinez-Murcia Introduction
Methodology Volume Selection Algorithm Convolutional Neural Networks Evaluation
Methodology Volume Selection Algorithm Convolutional Neural Networks Evaluation
Experimental Results DaTSCAN Image Database
Experimental Results DaTSCAN Image Database Experiments Comparison
Experiments Comparison
Conclusions and future work
Conclusions and future work
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Introduction
A 3D Convolutional Neural Network Approach for the Diagnosis of Parkinson’s Disease. F.J. Martinez-Murcia
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Parkinsonism is the second most common neurodegenerative disease worldwide.
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Introduction Methodology Volume Selection Algorithm Convolutional Neural Networks Evaluation
Experimental Results DaTSCAN Image Database Experiments Comparison
Conclusions and future work
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Introduction
A 3D Convolutional Neural Network Approach for the Diagnosis of Parkinson’s Disease. F.J. Martinez-Murcia
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Parkinsonism is the second most common neurodegenerative disease worldwide.
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Introduction Methodology Volume Selection Algorithm
Imaging: DaTSCAN ( I-ioflupane) in SPECT → in vivo assessment of DAT in the striatum. 123
Convolutional Neural Networks Evaluation
Experimental Results DaTSCAN Image Database Experiments Comparison
Conclusions and future work
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Introduction
A 3D Convolutional Neural Network Approach for the Diagnosis of Parkinson’s Disease. F.J. Martinez-Murcia
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Parkinsonism is the second most common neurodegenerative disease worldwide.
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Introduction Methodology Volume Selection Algorithm
Imaging: DaTSCAN ( I-ioflupane) in SPECT → in vivo assessment of DAT in the striatum. 123
Convolutional Neural Networks Evaluation
Experimental Results
Many CAD systems: Discriminating PD and CTLs, and extrapydarmidal symptoms.
DaTSCAN Image Database Experiments Comparison
Conclusions and future work
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Introduction
A 3D Convolutional Neural Network Approach for the Diagnosis of Parkinson’s Disease. F.J. Martinez-Murcia
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Parkinsonism is the second most common neurodegenerative disease worldwide.
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Methodology Volume Selection Algorithm
Imaging: DaTSCAN ( I-ioflupane) in SPECT → in vivo assessment of DAT in the striatum. 123
Convolutional Neural Networks Evaluation
Experimental Results
Many CAD systems: Discriminating PD and CTLs, and extrapydarmidal symptoms.
DaTSCAN Image Database Experiments Comparison
CNNs are a trend in 2D image processing.
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Introduction
Conclusions and future work
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Introduction
A 3D Convolutional Neural Network Approach for the Diagnosis of Parkinson’s Disease. F.J. Martinez-Murcia
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Parkinsonism is the second most common neurodegenerative disease worldwide.
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Methodology Volume Selection Algorithm
Imaging: DaTSCAN ( I-ioflupane) in SPECT → in vivo assessment of DAT in the striatum. 123
Convolutional Neural Networks Evaluation
Experimental Results
Many CAD systems: Discriminating PD and CTLs, and extrapydarmidal symptoms.
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CNNs are a trend in 2D image processing.
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3D CNN applied to PD.
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Introduction
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Volume Selection Algorithm
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Methodology 3
Volume Selection Algorithm Convolutional Neural Networks
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T=0.35 T=0.32
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Figure: Example of selected area for different threshold values.
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Background
Convolution
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Pool Convolution
Pool
F.J. Martinez-Murcia
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Introduction
Input
Methodology Volume Selection Algorithm 4
Convolutional Neural Networks Evaluation
Experimental Results DaTSCAN Image Database Experiments Comparison
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Convolution layer: Units are not traditional computational neurons → convolutions with filters. (ReLU activation f(x) = max(0, x))
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Conclusions and future work
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Background
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Pool Convolution
Pool
F.J. Martinez-Murcia
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Introduction
Input
Methodology Volume Selection Algorithm 4
Convolutional Neural Networks Evaluation
Experimental Results DaTSCAN Image Database Experiments Comparison
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Convolution layer: Units are not traditional computational neurons → convolutions with filters. (ReLU activation f(x) = max(0, x)) Pooling layer: non-linear downsampling by keeping the maximum value
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Conclusions and future work
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Background
Convolution
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Pool Convolution
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Introduction
Input
Methodology Volume Selection Algorithm 4
Convolutional Neural Networks Evaluation
Experimental Results DaTSCAN Image Database Experiments Comparison
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Convolution layer: Units are not traditional computational neurons → convolutions with filters. (ReLU activation f(x) = max(0, x)) Pooling layer: non-linear downsampling by keeping the maximum value Fully Connected Layer: Traditional neural networks. . . . . . . . . . . . . . . . . . . . . Softmax. .
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Architecture
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A 3D Convolutional Neural Network Approach for the Diagnosis of Parkinson’s Disease. F.J. Martinez-Murcia Introduction
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Figure: Schema of our system. ▶
Evaluation
Experimental Results
2 convolutional layers (P = Q = R = 5, with a structure of 2 layers with K1 = 8 and K2 = 16 filters respectively, stride=1, padding=2)
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A 3D Convolutional Neural Network Approach for the Diagnosis of Parkinson’s Disease. F.J. Martinez-Murcia Introduction
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Figure: Schema of our system. ▶
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Evaluation
Experimental Results
2 convolutional layers (P = Q = R = 5, with a structure of 2 layers with K1 = 8 and K2 = 16 filters respectively, stride=1, padding=2)
DaTSCAN Image Database Experiments Comparison
Conclusions and future work
Activation using ReLU function.
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A 3D Convolutional Neural Network Approach for the Diagnosis of Parkinson’s Disease. F.J. Martinez-Murcia Introduction
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Figure: Schema of our system. ▶
Evaluation
Experimental Results
2 convolutional layers (P = Q = R = 5, with a structure of 2 layers with K1 = 8 and K2 = 16 filters respectively, stride=1, padding=2)
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Activation using ReLU function.
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Max-pooling after every convolutional layer, with block size 2 × 2 × 2.
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A 3D Convolutional Neural Network Approach for the Diagnosis of Parkinson’s Disease. F.J. Martinez-Murcia Introduction
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Figure: Schema of our system. ▶
Evaluation
Experimental Results
2 convolutional layers (P = Q = R = 5, with a structure of 2 layers with K1 = 8 and K2 = 16 filters respectively, stride=1, padding=2)
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Activation using ReLU function.
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Max-pooling after every convolutional layer, with block size 2 × 2 × 2.
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Final dense layer (MLP). .
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Training
A 3D Convolutional Neural Network Approach for the Diagnosis of Parkinson’s Disease.
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F.J. Martinez-Murcia Introduction
Error Rate
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Figure: Evolution of the training error of the network (PD vs CTL) in function of the number of iterations. ▶
Dropout in training with probability p = 0.5.
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Figure: Evolution of the training error of the network (PD vs CTL) in function of the number of iterations. ▶
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Introduction
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10-Fold stratified cross validation
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Training: iterations over the augmented training set.
Methodology Volume Selection Algorithm Convolutional Neural Networks 7
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10-Fold stratified cross validation
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Methodology Volume Selection Algorithm Convolutional Neural Networks 7
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A 3D Convolutional Neural Network Approach for the Diagnosis of Parkinson’s Disease. F.J. Martinez-Murcia Introduction
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10-Fold stratified cross validation
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Training: iterations over the augmented training set. Parameters: Sensitivity, specificity and accuracy, or confusion matrix.
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Methodology Volume Selection Algorithm Convolutional Neural Networks 7
DaTSCAN Image Database
Experiment 1: PD vs CTL diagnosis. Effect of subvolume selection threshold T.
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Evaluation
A 3D Convolutional Neural Network Approach for the Diagnosis of Parkinson’s Disease. F.J. Martinez-Murcia Introduction
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10-Fold stratified cross validation
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Training: iterations over the augmented training set. Parameters: Sensitivity, specificity and accuracy, or confusion matrix.
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Methodology Volume Selection Algorithm Convolutional Neural Networks 7
DaTSCAN Image Database
Experiment 1: PD vs CTL diagnosis. Effect of subvolume selection threshold T.
Experiments Comparison
Conclusions and future work
Experiment 2: Include SWEDD subjects.
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A 3D Convolutional Neural Network Approach for the Diagnosis of Parkinson’s Disease.
The Parkinson’s Progression Markers Initiative ▶
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F.J. Martinez-Murcia Introduction
Data used in the preparation of this article were obtained from the Parkinson’s Progression Markers Initiative (PPMI) database (www.ppmi-info.org).
Methodology Volume Selection Algorithm Convolutional Neural Networks
Database selected: 301 DaTSCAN images (111 CTL, 32 SWEDD and 158 PD).
Evaluation
Experimental Results 8
DaTSCAN Image Database Experiments Comparison
Conclusions and future work
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A 3D Convolutional Neural Network Approach for the Diagnosis of Parkinson’s Disease.
The Parkinson’s Progression Markers Initiative ▶
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F.J. Martinez-Murcia Introduction
Data used in the preparation of this article were obtained from the Parkinson’s Progression Markers Initiative (PPMI) database (www.ppmi-info.org).
Methodology Volume Selection Algorithm Convolutional Neural Networks
Database selected: 301 DaTSCAN images (111 CTL, 32 SWEDD and 158 PD).
Evaluation
Experimental Results 8
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DaTSCAN Image Database
A 3D Convolutional Neural Network Approach for the Diagnosis of Parkinson’s Disease.
The Parkinson’s Progression Markers Initiative ▶
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F.J. Martinez-Murcia Introduction
Data used in the preparation of this article were obtained from the Parkinson’s Progression Markers Initiative (PPMI) database (www.ppmi-info.org).
Methodology Volume Selection Algorithm Convolutional Neural Networks
Database selected: 301 DaTSCAN images (111 CTL, 32 SWEDD and 158 PD).
Evaluation
Experimental Results 8
Comparison
Conclusions and future work
Simple data augmentation: mirroring (over the sagital plane) of the DaTSCAN images to account for asymmetrical dopaminergic deficit.
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A 3D Convolutional Neural Network Approach for the Diagnosis of Parkinson’s Disease.
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A 3D Convolutional Neural Network Approach for the Diagnosis of Parkinson’s Disease. F.J. Martinez-Murcia Introduction Methodology Volume Selection Algorithm Convolutional Neural Networks Evaluation
Experimental Results DaTSCAN Image Database 11
Experiments Comparison
Conclusions and future work
Figure: Area selected with a threshold T = 0.35.
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Figure: Summary of the activations in the first and second layers, after feeding a normal control patient with a threshold T = 0.35.
0.955 accuracy (0.961 sensitivity)! .
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A 3D Convolutional Neural Network Approach for the Diagnosis of Parkinson’s Disease.
Type 3D CNN 3D CNN (incl. SWEDD) 2D CNN1 ICA2 EMD3 Textures4 VAF-SVM
Accuracy 0.955 ± 0.044 0.820 ± 0.068 0.951 ± 0.035 0.928 ± 0.055 0.950 ± 0.048 0.970 ± 0.046 0.800 ± 0.071
Sensitivity 0.961 ± 0.066 0.965 ± 0.047 0.909 ± 0.091 0.951 ± 0.069 0.972 ± 0.062 0.831 ± 0.093
F.J. Martinez-Murcia
Specificity 0.945 ± 0.076 0.941 ± 0.052 0.961 ± 0.090 0.948 ± 0.072 0.968 ± 0.052 0.747 ± 0.112
Introduction Methodology Volume Selection Algorithm Convolutional Neural Networks Evaluation
Experimental Results DaTSCAN Image Database Experiments
Table: Comparison of this and other approaches for PD diagnosis
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Conclusions and future work
1 DOI:
10.1007/978-3-319-39687-3_24 10.1016/j.neucom.2013.01.054 3 DOI: 10.1016/j.eswa.2012.11.017 4 DOI: 10.1016/j.cmpb.2013.03.015 2 DOI:
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Experimental Results DaTSCAN Image Database Experiments Comparison 16
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Convolutional Neural Networks Evaluation
Experimental Results
Value of the T in the subvolume selection, which eliminates other regions than the striatum, where significant differences in SWEDD could be found → computational cost.
DaTSCAN Image Database Experiments Comparison 16
Conclusions and future work
Regardless of the SWEDD detection ability, the approach seems very promising → more complex architectures → management of more complex problems.
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A 3D Convolutional Neural Network Approach for the Diagnosis of Parkinson’s Disease. F.J. Martinez-Murcia Introduction Methodology
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Convolutional Neural Networks Evaluation
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Convolutional Neural Networks Evaluation
Experimental Results
Use preprocessing tools such as intensity normalization.
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Dept. of Signal Theory, Networking and Communications Universidad de Granada
Thank you for your attention!
SiPBA Signal Processing and Biomedical Applications http://sipba.ugr.es
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