Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Predicting Stroke Patient Recovery from Brain Images: A Machine Learning Approach

898 views

Published on

  • Be the first to comment

  • Be the first to like this

Predicting Stroke Patient Recovery from Brain Images: A Machine Learning Approach

  1. 1. 1PREDICTING STROKE PATIENTRECOVERY FROM BRAIN IMAGES:A MACHINE LEARNINGAPPROACHAlastair SmithSupervised by Prof. Glyn Humphreys
  2. 2. Objectives2  Can machine learning techniques applied to Computed Tomography (CT) brain imaging data provide meaningful predictions of functional recovery in stroke patients?  By exploring multiple machine learning techniques examine which approach provides the most accurate predictions?  What aspects of the images is utilised by the machine learning algorithms to inform predictions? Introduction
  3. 3. Stroke: The Consequences3Impact in the U.K. (National Stroke Strategy, 2007) Every year approximately 110,000 people in England have a stroke, with over 900,000 people currently living in England who have had a stroke. Stroke is the single largest cause of adult disability with a third of people who have a stroke left with long-term disability. Stroke costs the NHS and the economy about £7 billion a year, despite U.K. services being among the most expensive, outcomes for U.K. patients are comparatively poor with unnecessarily long lengths of stay and high levels of avoidable disability and mortality. Recovery & Rehabilitation:  Effects include physical disability, loss of cognitive and communication skills, mental health problems.  Recovery program specific to patient symptoms and commonly requires intervention from physiotherapists, psychologists, occupational therapists, speech therapists and specialist nurses and doctors.  A third of patients make a close to full recovery physically and are able to live an independent life, a third will require assistance in daily activities, and a third of patient will die within a year. (http://www.nhs.uk) Introduction
  4. 4. Machine Learning & Brain Imaging (1)4  Machine Learning Techniques:  Increasingly Influential in Neuroscience and Clinical Medicine (Belazzi & Zupan, 2008)  Informing individual patient management, selecting appropriate treatments (Seker et al, 2003)  Brain Imaging Data  Large number of features, small number of samples  Avoids ‘overfitting’ problem Introduction
  5. 5. Machine Learning & Brain Imaging (2)5 MRI & fMRI  Support Vector Machine (SVM) applied to MRI data  Ecker et al (2010), Autistic Spectrum Disorder  Kloppel et al (2008), Alzheimers Disease (acc = 96%, n=68)  Detection of other diseases: Fan et al (2005), Kawasaki et al (2007)  SVM applied to fMRI data  Classifiers developed to distinguish between stimuli, mental states and behaviours, demonstrating data contains sufficient information  For review see Norman et al (2006) and Haynes & Rees (2006)  Saur et al (2010) predicting recovery of stroke patients language abilities after 6 months, (acc = 76%, n=21)  Relevance Vector Regression (RVR) applied to fMRI data  Stonnington et al (2010):  Predicted continuous measure  Clinical measures of Alzheimers Disease  Predicted Score and actual scores highly correlated (p<0.0001, n=163) Introduction
  6. 6. Machine Learning & Brain Imaging (2)6  PET & RVM  Phillips et al (2011):  Distinguish between levels of consciousness  Acc = 100%, n = 58  Computed Tomography (CT)  Automated image segmentation, Li et al (2006)  Haemorrhage detection, Liu et al (2008)  Reid et al (2010):  CT derived variables did not significantly improve multivariate logistic regression models predictions of functional recovery in stroke patients Introduction
  7. 7. Nottingham Extended ADL7 Ranked assessment of patients ability to complete activities of daily living (ADL) independently Developed specifically for use with stoke patients (Nouri & Lincoln, 1987)  Completed by patient or carer via post or interview Demonstrated to be a useful measure of outcome in stroke research  Gladman et al (1993)  Cited in 14 studies as a measure of stroke patient outcomes (Green et al, 2001) Composed of 21 questions, split in to 4 subsections:  Mobility, Kitchen, Domestic, Leisure High scores indicate low disability  Maximum score = 21, Minimum Score = 0 Method
  8. 8. Data Acquisition8 Participants  Patients of to stroke units within West Midlands area  Recruited as part of Birmingham University Cognitive Screen (BUCS) project Inclusion Criteria: Exclusion Criteria: • Informed Consent • Unwell • New Acute Stroke • Decline to participate • Alert • Concentration span <35mins • Sufficient English Comprehension  All patients selected for current study had suffered ischemic stroke Time from stroke Time from stroke Age to scan (days) to testing (days) n NEADL 69.54 1.79 299.3 155 Method
  9. 9. NEADL data sets9 20 18 Very Good Recovery Very Poor Recovery 16 Bottom 42 percentile Top 42 percentile 14 12 No. 10 8 6 4 2 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 NEADL Poor Recovery Good Recovery Score n Mean SD Good Recovery >=17 65 19.3 1.46 Poor Recovery <17 90 9.02 4.72 Very Good Recovery >=17 65 19.3 1.46 Very Poor Recovery <=12 65 14.5 1.24 Method
  10. 10. Data Acquisition10 Computed Tomography (CT) images:  Capture density of tissue  In-plane resolution 0.5x0.5mm², slice thickness 4-5mm  Whole Brain Pre-processing & Image Compression  Images of poor quality (due to head movement or other imaging issues) removed from sample  Images normalised to an in-house CT template (Ashbumer & Friston, 2003) using SPM8  Images segmented using unified segmentation SPM8 (Seghier et al, 2005) to form Grey Matter, White Matter and Cerebrospinal Fluid images  A further Abnormal tissue class was produced by adding an additional probability map (Seghier et al, 2008)  Smoothed Grey and White matter using a 12mm³ FHWM Gaussian kernel Method
  11. 11. Training & Testing11 Cross Validation  Applied in 5 folds  Data set(s) randomly divided into 5 equal test sets  In each fold  Model trained on all samples not present in test set  Model tested on ability to assign correct labels to test set Measures of performance  Performance measures record mean performance across all 5 folds  Accuracy = Proportion of correct classifications  Specificity = Proportion of samples correctly classified as ‘Bad’  Sensitivity = Proportion of samples correctly classified as ‘Good’  MCC = Matthews Correlation Coefficient (Matthews, 1975)  Common measure of performance for classifiers within machine learning literature  Balanced measure allows for uneven samples  Correlation coefficient equal to phi coefficient  +1 = perfect prediction Method
  12. 12. Improving Efficiency12 Recursive Feature Elimination (RFE):  Features with the lowest weights attributed by the model are eliminated iteratively  On each iteration:  Feature with lowest weight identified and eliminated from training data  New model trained on new training set  Training therefore becomes focused on voxels for which high weights are assigned Principle Component Analysis (PCA):  Reduce dimensionality of data set  Transforms set of correlated variables to smaller set of set of uncorrelated variables PCA applied to 2D data set (Jehan, 2005) Method
  13. 13. Machine Learning Techniques13 Support Vector Machine (Classifier):  Images treated as points in higher dimensional space  SVM aims to identify a hyperplane that separates the two classes, while maximising the distance between classes.  The hyperlane is defined by the set of images (support vectors) that lie on the maximal margin  Joachims (2002, 1999), based on Vapnik (1995) Sparse Logistic Regression (Classifier):  Logistic regression method applied within Bayesian framework  Sparse Gaussian prior is assumed with mean zero  Iterative algorithm in which least informative features are pruned according to assigned weights  Yamashita et al (2008) Relevance Vector Machine (Classification & Regression) Optimal Separating Hyperplane defined by  Applies Bayesian techniques within a functional form similar to that of an SVM set of support vectors  Probabilistic model therefore able to indicate probability of class membership  By altering the conditional distribution of the target variable RVMs can be applied to both classification and regression problems  Tipping et al (2001, 2003). Method
  14. 14. NEADL Results (SVM)14 SVM Standard with PCA with RFE 99% Var ExtremesTissue Type UnG AbT AbT AbT SmG max 65% 69% 69% 70% 74%Accuracy / Pearsons r mean n/a 59% 62% 60% 65%Sensitivity max 54% 46% 66% 66% 71%Specificity max 73% 87% 71% 73% 76%MCC / RMSE max / min 0.27 0.30 0.37 0.40 0.48p< max 0.001 0.001 0.0001 0.0001 0.0001 Results
  15. 15. NEADL Results (SVM)15 SVM Standard with PCA with RFE 99% Var ExtremesTissue Type UnG AbT AbT AbT SmG max 65% 69% 69% 70% 74%Accuracy / Pearsons r mean n/a 59% 62% 60% 65%Sensitivity max 54% 46% 66% 66% 71%Specificity max 73% 87% 71% 73% 76%MCC / RMSE max / min 0.27 0.30 0.37 0.40 0.48p< max 0.001 0.001 0.0001 0.0001 0.0001 Results
  16. 16. NEADL Results (SVM)16 SVM Standard with PCA with RFE 99% Var ExtremesTissue Type UnG AbT AbT AbT SmG max 65% 69% 69% 70% 74%Accuracy / Pearsons r mean n/a 59% 62% 60% 65%Sensitivity max 54% 46% 66% 66% 71%Specificity max 73% 87% 71% 73% 76%MCC / RMSE max / min 0.27 0.30 0.37 0.40 0.48p< max 0.001 0.001 0.0001 0.0001 0.0001 Results
  17. 17. NEADL Results (SVM)17 SVM Standard with PCA with RFE 99% Var ExtremesTissue Type UnG AbT AbT AbT SmG max 65% 69% 69% 70% 74%Accuracy / Pearsons r mean n/a 59% 62% 60% 65%Sensitivity max 54% 46% 66% 66% 71%Specificity max 73% 87% 71% 73% 76%MCC / RMSE max / min 0.27 0.30 0.37 0.40 0.48p< max 0.001 0.001 0.0001 0.0001 0.0001 Results
  18. 18. NEADL Results (SVM)18 SVM Standard with PCA with RFE 99% Var ExtremesTissue Type UnG AbT AbT AbT SmG max 65% 69% 69% 70% 74%Accuracy / Pearsons r mean n/a 59% 62% 60% 65%Sensitivity max 54% 46% 66% 66% 71%Specificity max 73% 87% 71% 73% 76%MCC / RMSE max / min 0.27 0.30 0.37 0.40 0.48p< max 0.001 0.001 0.0001 0.0001 0.0001 Results
  19. 19. NEADL Results (SVM)19 SVM Standard with PCA with RFE 99% Var ExtremesTissue Type UnG AbT AbT AbT SmG Sagittal Plane max 65% 69% 69% 70% 74%Accuracy / Pearsons r mean n/a 59% 62% 60% 65%Sensitivity max 54% 46% 66% 66% 71%Specificity max 73% 87% 71% 73% 76%MCC / RMSE max / min 0.27 0.30 0.37 0.40 0.48p< max 0.001 0.001 0.0001 0.0001 0.0001 Horizontal Plane Frontal Section Relevance map threshold at 90%: • Voxels with weights (absolute value) attributed by model in top 10 percentile • Blue = negative weight • Red = positive weight R L R L Results
  20. 20. NEADL Results (SVM & SLR)20 SVM SLR Standard with PCA with RFE 99% Var Extremes Standard with PCA (99%) & RFETissue Type UnG AbT AbT AbT SmG UnG AbT max 65% 69% 69% 70% 74% 58% 68%Accuracy / Pearsons r mean n/a 59% 62% 60% 65% n/a 58%Sensitivity max 54% 46% 66% 66% 71% 50% 74%Specificity max 73% 87% 71% 73% 76% 63% 62%MCC / RMSE max / min 0.27 0.30 0.37 0.40 0.48 0.13 0.37p< max 0.001 0.001 0.0001 0.0001 0.0001 0.15 0.0001 Results
  21. 21. NEADL Results (SVM & SLR)21 SVM SLR Standard with PCA with RFE 99% Var Extremes Standard with PCA (99%) & RFETissue Type UnG AbT AbT AbT SmG UnG AbT max 65% 69% 69% 70% 74% 58% 68%Accuracy / Pearsons r mean n/a 59% 62% 60% 65% n/a 58%Sensitivity max 54% 46% 66% 66% 71% 50% 74%Specificity max 73% 87% 71% 73% 76% 63% 62%MCC / RMSE max / min 0.27 0.30 0.37 0.40 0.48 0.13 0.37p< max 0.001 0.001 0.0001 0.0001 0.0001 0.15 0.0001 Results
  22. 22. NEADL Results (SVM, SLR & RVM)22 SVM SLR RVM Standard with PCA with RFE 99% Var Extremes Standard with PCA Standard with PCA (99%) & RFE (99%) & RFETissue Type UnG AbT AbT AbT SmG UnG AbT SmG AbT max 65% 69% 69% 70% 74% 58% 68% 67% 69%Accuracy / Pearsons r mean n/a 59% 62% 60% 65% n/a 58% 58%Sensitivity max 54% 46% 66% 66% 71% 50% 74% 53% 77%Specificity max 73% 87% 71% 73% 76% 63% 62% 76% 62%MCC / RMSE max / min 0.27 0.30 0.37 0.40 0.48 0.13 0.37 0.33 0.40p< max 0.001 0.001 0.0001 0.0001 0.0001 0.15 0.0001 0.0001 0.0001 Results
  23. 23. NEADL Results (SVM, SLR & RVM)23 SVM SLR RVM Standard with PCA with RFE 99% Var Extremes Standard with PCA Standard with PCA (99%) & RFE (99%) & RFETissue Type UnG AbT AbT AbT SmG UnG AbT SmG AbT max 65% 69% 69% 70% 74% 58% 68% 67% 69%Accuracy / Pearsons r mean n/a 59% 62% 60% 65% n/a 58% 58%Sensitivity max 54% 46% 66% 66% 71% 50% 74% 53% 77%Specificity max 73% 87% 71% 73% 76% 63% 62% 76% 62%MCC / RMSE max / min 0.27 0.30 0.37 0.40 0.48 0.13 0.37 0.33 0.40p< max 0.001 0.001 0.0001 0.0001 0.0001 0.15 0.0001 0.0001 0.0001 Results
  24. 24. NEADL Results (SVM, SLR, RVM & RVR)24 SVM SLR RVM RVR Standard with PCA with RFE 99% Var Extremes Standard with PCA Standard with PCA Standard with PCA (99%), RFE (99%) & RFE (99%) & RFE & Standardised ScoresTissue Type UnG AbT AbT AbT SmG UnG AbT SmG AbT UnG AbT max 65% 69% 69% 70% 74% 58% 68% 67% 69% 0.28 0.39Accuracy / Pearsons r mean n/a 59% 62% 60% 65% n/a 58% 58% n/a 0.35Sensitivity max 54% 46% 66% 66% 71% 50% 74% 53% 77%Specificity max 73% 87% 71% 73% 76% 63% 62% 76% 62%MCC / RMSE max / min 0.27 0.30 0.37 0.40 0.48 0.13 0.37 0.33 0.40 6.75 0.76p< max 0.001 0.001 0.0001 0.0001 0.0001 0.15 0.0001 0.0001 0.0001 0.001 0.0001 Results
  25. 25. NEADL Results (SVM, SLR, RVM & RVR)25 SVM SLR RVM RVR Standard with PCA with RFE 99% Var Extremes Standard with PCA Standard with PCA Standard with PCA (99%), RFE (99%) & RFE (99%) & RFE & Standardised ScoresTissue Type UnG AbT AbT AbT SmG UnG AbT SmG AbT UnG AbT max 65% 69% 69% 70% 74% 58% 68% 67% 69% 0.28 0.39Accuracy / Pearsons r mean n/a 59% 62% 60% 65% n/a 58% 58% n/a 0.35Sensitivity max 54% 46% 66% 66% 71% 50% 74% 53% 77%Specificity max 73% 87% 71% 73% 76% 63% 62% 76% 62%MCC / RMSE max / min 0.27 0.30 0.37 0.40 0.48 0.13 0.37 0.33 0.40 6.75 0.76p< max 0.001 0.001 0.0001 0.0001 0.0001 0.15 0.0001 0.0001 0.0001 0.001 0.0001 Results
  26. 26. Summary26  Abnormal Tissue, Smoothed Grey Matter and Unsmoothed Grey Matter consistently outperform other tissue types  Application of PCA and RFE improves model performance  Best performance produced when model trained on extreme samples within data set  RVM, SVM & SLR classifiers predict patient recovery with significant levels of accuracy (p<0.001)  SVM & RVM produce similar levels of performance yet outperform SLR  RVR predictions are highly correlated with true scores (p<0.001) Discussion
  27. 27. Wider Implications27  Performance comparable to results in literature  Saur et al (2010) predict language outcome 6 months after stroke with 76% accuracy using SVM classifier  Stonnington et al (2010) correlation between predicted and actual clinical measures of Alzheimers Disease (P<0.0001)  Stroke lesions generally more heterogeneous than those typically found in Alzheimers Disease patients  Few studies within currently literature applying Machine Learning to CT data to predict patient recovery Discussion
  28. 28. Methodological Issues28  Model evaluation and selection  Noise may account for maximum values  Accepted methods of evaluation and model selection:  Average across 100 trials with sample order randomised  Adapt algorithm to select when performance peaks  Analyse in the context of 100 random trials with scores randomly assigned Discussion
  29. 29. Future Study29  Improving Performance:  Poor performance currently restricts application to patient management or assessment of intervention programs  Additional Variables – e.g. blood vessel effected  Isolate ROI:  Informed by literature (Saur et al, 2010)  Weight maps (Ecker, 2010)  Ensemble methods (Optiz, 1999):  Train on individual lobes  Bootstrap Aggregating  Predict improvement in ADL scores  Saur at al, 2010  Investigate role of weighted voxels Discussion
  30. 30. Acknowledgments30  Alan Meeson  Provided:  Original code for machine learning algorithms  Support and guidance throughout project  Vaia Lestou  Assisted in the design and analysis of current study Discussion

×