3D-Patch Based Machine Learning Systems Thesis Defense 3D-Patch Based Machine Learning Systems An analysis on FDG-PET data for the clinical determination of Alzheimer’s Anant Srivastava Committee: Dr. Yalin Wang Dr. Ajay Bansal Dr. Jianming Liang Geometry Systems Laboratory (GSL) http://gsl.lab.asu.edu/ *

Table of contents Introduction Problem Background Alzheimer’s Disease History of PET and Machine Learning Method Data Gathering Preprocessing System Design Pipeline - P.S.C. Components Pipeline - P.F.E. Experiments Result Conclusion Future Work Questions ? Geometry Systems Laboratory (GSL) *

Introduction Alzheimer’s Disease (AD) “A progressive disease that destroys memory and other important mental functions. No cure exists, but medications and management strategies may temporarily improve symptoms.” Mayo Clinic Geometry Systems Laboratory (GSL) *

Problem Background Alzheimer’s Disease is a growing concern as it affects 1 in 3 elders. Recent years have shown immense propensity towards finding neuroimaging techniques and different biological markers It is required that AD can be classified with high accuracy as non-AD dementias would not benefit from AD specific treatment. Also AD progression should be tracked and distinguished from lower level of dementias. Geometry Systems Laboratory (GSL) *

Alzheimer’s Disease Is a very common disease more than 3 million U.S. cases/year. Starts off 1-2 decades prior to the first symptoms. Growing urgency in the early diagnosis. Amyloid plaques build improperly and cause neuron death, The cause of which is still unknown. Can these early developmental changes be assessed? Geometry Systems Laboratory (GSL) *

Research “The institute hosts the largest collection of brain data in the world, housing more than 4,800 terabytes of information” - USC News ADNI PPMI multi site projects (59 accusation sites ADNI). 12 mill in 2015 for the year in a 5 year plan Geometry Systems Laboratory (GSL) *

{ A.D.N.I. Alzheimer’s Disease Neuroimaging Initiative Multi Study with 59 accusation sites Goal : Track the progression of the disease using biomarkers. ADNIs three phases : { ADNI 1 ADNI GO ADNI 2 ADNI is a global research effort that actively supports the investigation and development of treatments that slow or stop the progression of AD. Biomarkers are tracers which help measurable some phenomenon such as disease, infection, or environmental exposure. ADNI1: 5 years from October 2004 ADNIGO: 2 years from September 2009 ADNI2: 5 years from September 2011 ADNI GO ADNI 1 ADNI 2 Geometry Systems Laboratory (GSL) *

Clinical Stages. CN : Cognitively Normal EMCI : Early Mild Cognitive Impairment MCI : Mild Cognitive Impairment LMCI : Impairment AD : Alzheimer’s disease Geometry Systems Laboratory (GSL) *

} } Biomarkers in Alzheimer’s The most commonly used biomarker for Alzheimer’s Disease (AD) are : Amyloid beta imaging modality (CSF and Amyloid PET) Neurodegeneration (FDG-PET) Brain atrophy and neuron loss (MRI, most notably hippocampus) Memory loss (Assessment) General cognitive decline (Assessment) } Pre Diagnosis } Post Diagnosis Geometry Systems Laboratory (GSL) *

MRI PET Functional (neuronal injury) Structural Still in exploration Highly developed toolset PET Functional (neuronal injury) Still in exploration Cellular changes are important. Geometry Systems Laboratory (GSL) *

Table of contents Introduction History of PET and Machine Learning Problem Background Alzheimer’s Disease History of PET and Machine Learning Method Data Gathering Preprocessing System Design Pipeline - P.S.C. Components Pipeline - P.F.E. Result Conclusion Future Work Questions ? Geometry Systems Laboratory (GSL) *

History of PET and Machine Learning FDG-PET Statistical Voxel-Based Analysis Approach Machine Learning Independent component analysis (ICA), Principal component analysis (PCA) (Feature extraction) (support vector machine )SVM, AdaBoost, Neural Nets (Classification, regression) Sparse Coding Geometry Systems Laboratory (GSL) *

History ... Patch based analysis Brain extraction is important in analysis of brain image. A patch is a region of voxels. 2-D patches along with sparse coding has proven to be efficient in classifying Alzheimer's Applying sparse coding to surface multivariate tensor-based morphometry to predict future cognitive decline Geometry Systems Laboratory (GSL) *

Table of contents Introduction History of PET and Machine Learning Problem Background Alzheimer’s Disease History of PET and Machine Learning Method Data Gathering Preprocessing System Design Pipeline - P.S.C. Components Pipeline - P.F.E. Result Conclusion Future Work Questions ? Geometry Systems Laboratory (GSL) *

Data Gathering Geometry Systems Laboratory (GSL) *

Preprocessing SPM (Statistical Parametric Mapping) Segement Align Original scans Aligned & Normalized Segmented Geometry Systems Laboratory (GSL) *

Demographics Geometry Systems Laboratory (GSL) *

System Design. We design two systems for the classification of AD vs lower level dementia. Patch Based Sparse Coding (PSC) Patch Generator Down Sampler Stochastic Coordinate Coding Adaboost classifier Patch Based Feature Extraction (PFE) Pooler Feature Extraction (PCA) Geometry Systems Laboratory (GSL) *

Table of contents Introduction History of PET and Machine Learning Problem Background Alzheimer’s Disease History of PET and Machine Learning Method Data Gathering Preprocessing System Design Pipeline - P.S.C. Components Pipeline - P.F.E. Result Conclusion Future Work Questions ? Geometry Systems Laboratory (GSL) *

Pipeline 1 - Patch Based Sparse Coding (PSC) Geometry Systems Laboratory (GSL) *

Architecture Geometry Systems Laboratory (GSL) Input : Segmented FDG-PET scans Geometry Systems Laboratory (GSL) *

Components 1. Patch Generation 10 x 10 x 10 patches generate random overlapping patches ensure overlap by creating many patches. 1. Patch Generation Geometry Systems Laboratory (GSL) *

Components 2. Downsample Done to reduce computational time and space. 332,000,000 value matrix Reduces the 10 x 10 x 10 (1000 feature vector) matrix to 125 vector by using a window of 2 x 2 x 2. 41,500,000 Objective matrix for Sparse Coding. 2. Downsample Geometry Systems Laboratory (GSL) *

Components 3. Stochastic Coordinate Coding Used in audio and image processing domain Model the data vectors as sparse linear combination of basis elements. Over complete dictionaries may represent data more flexibly 3. Stochastic Coordinate Coding Geometry Systems Laboratory (GSL) *

Components 3. Stochastic Coordinate Coding Patches of 1st sample Patches of 2nd sample Patches of nth sample Input Matrix Dictionary learning Geometry Systems Laboratory (GSL) *

Components 3. Stochastic Coordinate Coding Take an image patch xi Perform few steps of coordinate descent to find the support (non zero entries) of the sparse code. Update the support of the dictionary by second order stochastic gradient descent to obtain a new dictionary 10 epochs Geometry Systems Laboratory (GSL) *

Components 4. AdaBoost Geometry Systems Laboratory (GSL) Strong classifier based on weak learners Use weights to force weak learners to classify generally misclassified examples. Figure on the right - Round 1 : First weak classifier (left vertical line) Round 2 : Second weak classifier (right vertical line) Round 3 : Third weak classifier (horizontal line) Final Adaboost classifier. Geometry Systems Laboratory (GSL) *

Pipeline 2 - Patch Based Feature Extraction (PFE) Geometry Systems Laboratory (GSL) *

Pipeline - P.F.E. Geometry Systems Laboratory (GSL) Input : Segmented FDG-PET scans Geometry Systems Laboratory (GSL) *

Components 1. Patch Generation 10 x 10 x 10 patches Generate structured uniform overlapping patches 1. Patch Generation Geometry Systems Laboratory (GSL) *

Components 2. Pooling Feature Selection Technique (R80x95x80 R1x4050) (reduce noise, highlight relevant feature in a patch, translation invariant) 2. Pooling 3-D Max-pooling 2-D Geometry Systems Laboratory (GSL) *

Components 3. Feature Extraction Probabilistic PCA (M.E. Tipping) To further reduce dimensionality probabilistic version of PCA deals with missing data finds MLE (maximum likelihood estimate) ~ Principal components 3. Feature Extraction Geometry Systems Laboratory (GSL) *

Components 4. AdaBoost As seen previously Geometry Systems Laboratory (GSL) *

Table of contents Introduction History of PET and Machine Learning Problem Background Alzheimer’s Disease History of PET and Machine Learning Method Data Gathering Preprocessing System Design Pipeline - P.S.C. Components Pipeline - P.F.E. Result Conclusion Future Work Questions ? Geometry Systems Laboratory (GSL) *

Experiments. Geometry Systems Laboratory (GSL) *

Results Run the experiments over the two proposed pipelines PSC PFE Comparing sets of features Meta analysis PSC comparing classifier no. of patches involved in training PFE comparing classifiers comparing feature extraction techniques. *

Comparing sets of features AF = Apoe + FAQ AFAG = Apoe + FAQ + Age + Gender AD vs. CU stages with greatest class separation voxels + AFAG ~ 96% AD vs. EMCI stages with class separation of 2 voxels + AFAG ~ 83% Geometry Systems Laboratory (GSL) *

Comparing sets of features Geometry Systems Laboratory (GSL) *

PSC -classifier comparison NN : nearest Neighbour, SVM: support vector machine AdaBoost & SVM gives near 97% F1 measure, 2000 patches Geometry Systems Laboratory (GSL) *

PSC -number of patches patches of 500, 1000, 2000. F1 Measure In exp. with high class separation f1 increases with number of patches Decode Time is proportional to the number of patches. Geometry Systems Laboratory (GSL) *

PFC -classifier comparison NN : nearest Neighbour, SVM: support vector machine (Gaussian performs the best) ,PCA , Adaboost more stable Geometry Systems Laboratory (GSL) *

PFC -feature extraction comparison PCA : principle component analysis, SVD: singular valued Decomposition PCA’s performs vary when compared to other feature extraction methods Geometry Systems Laboratory (GSL) *

AUC Curves Geometry Systems Laboratory (GSL) *

Table of contents Introduction History of PET and Machine Learning Problem Background Alzheimer’s Disease History of PET and Machine Learning Method Data Gathering Preprocessing System Design Pipeline - P.S.C. Components Pipeline - P.F.E. Result Conclusion Future Work Questions ? Geometry Systems Laboratory (GSL) *

Conclusion Comprehensive experimentation shows the effectiveness of 3D patch based methods. We examine other rich features like the APOE gene information, FAQ scores Age and Gender. huge boost when other features are involved . 90% to 96% With experiments with lower group separability the methods struggled. May indicate lower cellular metabolism difference. *

Future Work This method can be used to classify DTI images PET imaging is effective in detecting Parkinson's and so these methods can be applied to detect Parkinson's Use of autoencoders similar to Sparse coding in the pipeline might give interesting results. Geometry Systems Laboratory (GSL) *

Publications MICCAI 2017 (under review) paper titled, “3D-Patch Based Sparse Coding System for Alzheimer’s Disease Diagnosis via FDG-PET Analysis” AAC May 2017 (poster publication) Arizona Alzheimer’s Consortium Scientific Conference. Abstract titled “3D-Patch Analysis Based Sparse Coding System for Alzheimer’s Clinical Group Classification ” *

Questions? Geometry Systems Laboratory (GSL) *

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