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A comparativestudy of deeplearning based
methods for MRIimage processing
RobertDadashi-Tazehozi
rd2669
Introduction
Articles
Motivation
Medicalbackground
Neurological Diseases
MRI
Image processingpipeline
Datasets
Preprocessing
Random Forest forClassification
Results
A comparative study of deep learning basedmethods for MRI image processing
Robert Dadashi-Tazehozird2669
Department of Computer ScienceColumbia University
Deep Learning for Computer Vision and NaturalLanguage Processing
EECS 6894
A comparativestudy of deeplearning based
methods for MRIimage processing
RobertDadashi-Tazehozi
rd2669
Introduction
Articles
Motivation
Medicalbackground
Neurological Diseases
MRI
Image processingpipeline
Datasets
Preprocessing
Random Forest forClassification
Results
Outline
IntroductionArticlesMotivation
Medical backgroundNeurological DiseasesMRI
Image processing pipelineDatasetsPreprocessingRandom Forest for ClassificationResults
A comparativestudy of deeplearning based
methods for MRIimage processing
RobertDadashi-Tazehozi
rd2669
Introduction
Articles
Motivation
Medicalbackground
Neurological Diseases
MRI
Image processingpipeline
Datasets
Preprocessing
Random Forest forClassification
Results
Outline
IntroductionArticlesMotivation
Medical backgroundNeurological DiseasesMRI
Image processing pipelineDatasetsPreprocessingRandom Forest for ClassificationResults
A comparativestudy of deeplearning based
methods for MRIimage processing
RobertDadashi-Tazehozi
rd2669
Introduction
Articles
Motivation
Medicalbackground
Neurological Diseases
MRI
Image processingpipeline
Datasets
Preprocessing
Random Forest forClassification
Results
Articles
Deep Learning for Cerebellar Ataxia Classification andFunctional Score RegressionZhen Yang, Shenghua Zhong, Aaron Carass, Sarah H. Ying,and Jerry L. PrinceJohns Hopkins University
Deep Learning of Image Features from Unlabeled Datafor Multiple Sclerosis Lesion SegmentationYoungjin Yoo, Tom Brosch, Anthony Traboulsee, David K.B.Li, and Roger TamUniversity of British Columbia
A comparativestudy of deeplearning based
methods for MRIimage processing
RobertDadashi-Tazehozi
rd2669
Introduction
Articles
Motivation
Medicalbackground
Neurological Diseases
MRI
Image processingpipeline
Datasets
Preprocessing
Random Forest forClassification
Results
Outline
IntroductionArticlesMotivation
Medical backgroundNeurological DiseasesMRI
Image processing pipelineDatasetsPreprocessingRandom Forest for ClassificationResults
A comparativestudy of deeplearning based
methods for MRIimage processing
RobertDadashi-Tazehozi
rd2669
Introduction
Articles
Motivation
Medicalbackground
Neurological Diseases
MRI
Image processingpipeline
Datasets
Preprocessing
Random Forest forClassification
Results
Motivation
I MRI data
I Deep Learning + Machine Learning
I Different goals and barriers while same data type, hencea comparative study- Data description- Preprocessing- Methods and Algorithms used- Results
I Project: Diabetic Retinopathy Detection
I Personal reasons
A comparativestudy of deeplearning based
methods for MRIimage processing
RobertDadashi-Tazehozi
rd2669
Introduction
Articles
Motivation
Medicalbackground
Neurological Diseases
MRI
Image processingpipeline
Datasets
Preprocessing
Random Forest forClassification
Results
Outline
IntroductionArticlesMotivation
Medical backgroundNeurological DiseasesMRI
Image processing pipelineDatasetsPreprocessingRandom Forest for ClassificationResults
A comparativestudy of deeplearning based
methods for MRIimage processing
RobertDadashi-Tazehozi
rd2669
Introduction
Articles
Motivation
Medicalbackground
Neurological Diseases
MRI
Image processingpipeline
Datasets
Preprocessing
Random Forest forClassification
Results
Ataxia
Overview A neuro-degenerative disease- Affects the cerebellum- Symptoms: lack of muscular coordination
https://www.youtube.com/watch?v=5eBwn22Bnio
Goals:Classify different types of Ataxia: HC, SCA2, SCA6, ATQuantify functional loss based on structural change
A comparativestudy of deeplearning based
methods for MRIimage processing
RobertDadashi-Tazehozi
rd2669
Introduction
Articles
Motivation
Medicalbackground
Neurological Diseases
MRI
Image processingpipeline
Datasets
Preprocessing
Random Forest forClassification
Results
Multiple sclerosis
Overview A inflammatory disease- Brain cells damaged: Demyelination- Symptoms: Mental, Physical, Psychatric troubles- Cause: Genetic? Environmental factors?
https://www.youtube.com/watch?v=qgySDmRRzxY
Goals:Automatic segmentation of lesionned areas
A comparativestudy of deeplearning based
methods for MRIimage processing
RobertDadashi-Tazehozi
rd2669
Introduction
Articles
Motivation
Medicalbackground
Neurological Diseases
MRI
Image processingpipeline
Datasets
Preprocessing
Random Forest forClassification
Results
Outline
IntroductionArticlesMotivation
Medical backgroundNeurological DiseasesMRI
Image processing pipelineDatasetsPreprocessingRandom Forest for ClassificationResults
A comparativestudy of deeplearning based
methods for MRIimage processing
RobertDadashi-Tazehozi
rd2669
Introduction
Articles
Motivation
Medicalbackground
Neurological Diseases
MRI
Image processingpipeline
Datasets
Preprocessing
Random Forest forClassification
Results
MRI imaging
I Human body composed of small magnets
I Magnets aligned then excited by pulses
I Magnets go back to their lowest energy state,electromagnetic waves emitted
I Processing of these waves enable to reconstruct 3Dstructure, differentiate muscles tissues from fat, whitematter from grey matter in the brain
A comparativestudy of deeplearning based
methods for MRIimage processing
RobertDadashi-Tazehozi
rd2669
Introduction
Articles
Motivation
Medicalbackground
Neurological Diseases
MRI
Image processingpipeline
Datasets
Preprocessing
Random Forest forClassification
Results
MRI imaging
Figure: Initial state
Figure: Excitation
Figure: Energy emission
A comparativestudy of deeplearning based
methods for MRIimage processing
RobertDadashi-Tazehozi
rd2669
Introduction
Articles
Motivation
Medicalbackground
Neurological Diseases
MRI
Image processingpipeline
Datasets
Preprocessing
Random Forest forClassification
Results
Outline
IntroductionArticlesMotivation
Medical backgroundNeurological DiseasesMRI
Image processing pipelineDatasetsPreprocessingRandom Forest for ClassificationResults
A comparativestudy of deeplearning based
methods for MRIimage processing
RobertDadashi-Tazehozi
rd2669
Introduction
Articles
Motivation
Medicalbackground
Neurological Diseases
MRI
Image processingpipeline
Datasets
Preprocessing
Random Forest forClassification
Results
DatasetsAtaxia
I 168 scansI 3D Images projected on 9 plans (Resulting in 32 * 32
images)
Not enough labelled data ⇒ Interest of generatingsynthetic data
Multiple SclerosisI 1450 scansI Resolution 256 * 256 * 50I Only 100 scans segmented
Lot of unlabelled data ⇒ Interest of unsupervisedmethods
A comparativestudy of deeplearning based
methods for MRIimage processing
RobertDadashi-Tazehozi
rd2669
Introduction
Articles
Motivation
Medicalbackground
Neurological Diseases
MRI
Image processingpipeline
Datasets
Preprocessing
Random Forest forClassification
Results
Outline
IntroductionArticlesMotivation
Medical backgroundNeurological DiseasesMRI
Image processing pipelineDatasetsPreprocessingRandom Forest for ClassificationResults
A comparativestudy of deeplearning based
methods for MRIimage processing
RobertDadashi-Tazehozi
rd2669
Introduction
Articles
Motivation
Medicalbackground
Neurological Diseases
MRI
Image processingpipeline
Datasets
Preprocessing
Random Forest forClassification
Results
Preprocessing
Ataxia
I 3D Images projected on 9 plans (Resulting in 32 * 32images)
I Generate translated and rotated images
I Reduce dimensions using a Stacked Auto-Encoder foreach plan
Multiple Sclerosis
I Images of resolution 256 * 256 * 50
I Patches 9 * 9 * 3 (low-scale features)⇒ Train Restricted Boltzmann Machines for featureextraction
I Patches 15 * 15 * 5 (high-scale features)⇒ Train Deep Belief Network for feature extraction
A comparativestudy of deeplearning based
methods for MRIimage processing
RobertDadashi-Tazehozi
rd2669
Introduction
Articles
Motivation
Medicalbackground
Neurological Diseases
MRI
Image processingpipeline
Datasets
Preprocessing
Random Forest forClassification
Results
Stacked Auto-Encoder
I Target vector is the output
I Along the way, compression of the data
I Trained layer after layer (greedy)
A comparativestudy of deeplearning based
methods for MRIimage processing
RobertDadashi-Tazehozi
rd2669
Introduction
Articles
Motivation
Medicalbackground
Neurological Diseases
MRI
Image processingpipeline
Datasets
Preprocessing
Random Forest forClassification
Results
Restricted Boltzman Machines
I Visible and Hidden Units
I Energy Based Method
E (v , h) = −vTWh − aT v − bTh
P(v , h) =1
Ze−E(v ,h)
A comparativestudy of deeplearning based
methods for MRIimage processing
RobertDadashi-Tazehozi
rd2669
Introduction
Articles
Motivation
Medicalbackground
Neurological Diseases
MRI
Image processingpipeline
Datasets
Preprocessing
Random Forest forClassification
Results
Restricted Boltzman Machines
A comparativestudy of deeplearning based
methods for MRIimage processing
RobertDadashi-Tazehozi
rd2669
Introduction
Articles
Motivation
Medicalbackground
Neurological Diseases
MRI
Image processingpipeline
Datasets
Preprocessing
Random Forest forClassification
Results
Restricted Boltzman Machines
How to train RBM ? [Hinton 2002]
I Generate hidden units from visible units
I Generate visible units from these very hidden units
I Update weight:
∆Wi ,j = α(< vj , hi >input − < vj , hi >generateddata)
A comparativestudy of deeplearning based
methods for MRIimage processing
RobertDadashi-Tazehozi
rd2669
Introduction
Articles
Motivation
Medicalbackground
Neurological Diseases
MRI
Image processingpipeline
Datasets
Preprocessing
Random Forest forClassification
Results
Deep Belief Networks [Hinton 2009]
I Stacked RBMs
I Greedy training
A comparativestudy of deeplearning based
methods for MRIimage processing
RobertDadashi-Tazehozi
rd2669
Introduction
Articles
Motivation
Medicalbackground
Neurological Diseases
MRI
Image processingpipeline
Datasets
Preprocessing
Random Forest forClassification
Results
Outline
IntroductionArticlesMotivation
Medical backgroundNeurological DiseasesMRI
Image processing pipelineDatasetsPreprocessingRandom Forest for ClassificationResults
A comparativestudy of deeplearning based
methods for MRIimage processing
RobertDadashi-Tazehozi
rd2669
Introduction
Articles
Motivation
Medicalbackground
Neurological Diseases
MRI
Image processingpipeline
Datasets
Preprocessing
Random Forest forClassification
Results
ClassificationAtaxia
Multiple Sclerosis
A comparativestudy of deeplearning based
methods for MRIimage processing
RobertDadashi-Tazehozi
rd2669
Introduction
Articles
Motivation
Medicalbackground
Neurological Diseases
MRI
Image processingpipeline
Datasets
Preprocessing
Random Forest forClassification
Results
Random Forests
I Bagging Data
I Selecting Features
I Generate Decision Tree
Averaging all the decision trees ⇒ Classifier
A comparativestudy of deeplearning based
methods for MRIimage processing
RobertDadashi-Tazehozi
rd2669
Introduction
Articles
Motivation
Medicalbackground
Neurological Diseases
MRI
Image processingpipeline
Datasets
Preprocessing
Random Forest forClassification
Results
Outline
IntroductionArticlesMotivation
Medical backgroundNeurological DiseasesMRI
Image processing pipelineDatasetsPreprocessingRandom Forest for ClassificationResults
A comparativestudy of deeplearning based
methods for MRIimage processing
RobertDadashi-Tazehozi
rd2669
Introduction
Articles
Motivation
Medicalbackground
Neurological Diseases
MRI
Image processingpipeline
Datasets
Preprocessing
Random Forest forClassification
Results
Results
Ataxia
Multiple SclerosisDice Similarity Measure:Q : Lesionned voxels (manually segmented)P : Lesionned voxels (automatically segmented)
d =2|Q ∩ P||Q|+ |P|
Weiss (state of the art) : mean 29, std 13Method: mean 38, std 19
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