3DSiameseNet to Analyze Brain MRI

Prediction of the cognitive evolution of a person susceptible to develop a neurodegenerative disorder is crucial to provide an appropriate treatment as soon as possible. In this paper we propose a 3D siamese network designed to extract features from whole-brain 3D MRI images. We show that it is possible to extract meaningful features using convolution layers, reducing the need of classical image processing operations such as segmentation or pre-computing features such as cortical thickness. To lead this study we used the Alzheimer's Disease Neuroimaging Initiative (ADNI), a public data base of 3D MRI brain images. A set of 247 subjects has been extracted, all of the subjects having 2 images in a range of 12 months. In order to measure the evolution of the patients states we have compared these 2 images. Our work has been inspired at the beginning by an article of Bhagwat et al. in 2018, who have proposed a siamese network to predict the status of patients but without any convolutional layers and reducing the MRI images to a vector of features extracted from predefined ROIs. We show that our network achieves an accuracy of 90\% in the classification of cognitively declining VS stable patients. This result has been obtained without the help of a cognitive score and with a small number of patients comparing to the current datasets size claimed in deep learning domain.

[1]  Gregory R. Koch,et al.  Siamese Neural Networks for One-Shot Image Recognition , 2015 .

[2]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[3]  Martín Abadi,et al.  TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems , 2016, ArXiv.

[4]  Hafiz Zia Ur Rehman,et al.  3D U-Net for Skull Stripping in Brain MRI , 2019, Applied Sciences.

[5]  Ghassem Tofighi,et al.  Classification of Alzheimer's Disease using fMRI Data and Deep Learning Convolutional Neural Networks , 2016, ArXiv.

[6]  Nick C Fox,et al.  The clinical use of structural MRI in Alzheimer disease , 2010, Nature Reviews Neurology.

[7]  Ayman El-Baz,et al.  Alzheimer's Disease Diagnostics by a Deeply Supervised Adaptable 3D Convolutional Network , 2016, ArXiv.

[8]  Jenny Benois-Pineau,et al.  Classification of sMRI for Alzheimer's disease Diagnosis with CNN: Single Siamese Networks with 2D+? Approach and Fusion on ADNI , 2017, ICMR.

[9]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[10]  M. Mallar Chakravarty,et al.  Modeling and prediction of clinical symptom trajectories in Alzheimer’s disease using longitudinal data , 2018, PLoS Comput. Biol..

[11]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[12]  Giovanni Montana,et al.  Predicting Alzheimer's disease: a neuroimaging study with 3D convolutional neural networks , 2015, ICPRAM 2015.

[13]  Prospero C. Naval,et al.  DemNet: A Convolutional Neural Network for the detection of Alzheimer's Disease and Mild Cognitive Impairment , 2016, 2016 IEEE Region 10 Conference (TENCON).

[14]  Nikos Komodakis,et al.  Learning to compare image patches via convolutional neural networks , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Fei Su,et al.  Homemade TS-Net for Automatic Face Recognition , 2016, ICMR.

[16]  Andrew L. Maas.  Rectifier Nonlinearities Improve Neural Network Acoustic Models , 2013 .

[17]  Yulia Dodonova,et al.  Residual and plain convolutional neural networks for 3D brain MRI classification , 2017, 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017).

[18]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[19]  Jenny Benois-Pineau,et al.  3D CNN-based classification using sMRI and MD-DTI images for Alzheimer disease studies , 2018, ArXiv.