Semi-supervised pattern classification of medical images: Application to mild cognitive impairment (MCI)

Many progressive disorders are characterized by unclear or transient diagnoses for specific subgroups of patients. Commonly used supervised pattern recognition methodology may not be the most suitable approach to deriving image-based biomarkers in such cases, as it relies on the availability of categorically labeled data (e.g., patients and controls). In this paper, we explore the potential of semi-supervised pattern classification to provide image-based biomarkers in the absence of precise diagnostic information for some individuals. We employ semi-supervised support vector machines (SVM) and apply them to the problem of classifying MR brain images of patients with uncertain diagnoses. We examine patterns in serial scans of ADNI participants with mild cognitive impairment (MCI), and propose that in the absence of sufficient follow-up evaluations of individuals with MCI, semi-supervised strategy is potentially more appropriate than the fully-supervised paradigm employed up to date.

[1]  Thorsten Joachims,et al.  Transductive Inference for Text Classification using Support Vector Machines , 1999, ICML.

[2]  Xiaoying Wu,et al.  Structural and functional biomarkers of prodromal Alzheimer's disease: A high-dimensional pattern classification study , 2008, NeuroImage.

[3]  A. Dale,et al.  Alzheimer disease: quantitative structural neuroimaging for detection and prediction of clinical and structural changes in mild cognitive impairment. , 2009, Radiology.

[4]  Jerry L. Prince,et al.  Adaptive fuzzy segmentation of magnetic resonance images , 1999, IEEE Transactions on Medical Imaging.

[5]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[6]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[7]  Marie Chupin,et al.  Automatic classi fi cation of patients with Alzheimer ' s disease from structural MRI : A comparison of ten methods using the ADNI database , 2010 .

[8]  Dinggang Shen,et al.  Very High-Resolution Morphometry Using Mass-Preserving Deformations and HAMMER Elastic Registration , 2003, NeuroImage.

[9]  D. Louis Collins,et al.  MRI-Based Automated Computer Classification of Probable AD Versus Normal Controls , 2008, IEEE Transactions on Medical Imaging.

[10]  L. Beckett,et al.  Annual Incidence of Alzheimer Disease in the United States Projected to the Years 2000 Through 2050 , 2001, Alzheimer disease and associated disorders.

[11]  R. Gur,et al.  Unaffected Family Members and Schizophrenia Patients Share Brain Structure Patterns: A High-Dimensional Pattern Classification Study , 2008, Biological Psychiatry.

[12]  Christos Davatzikos,et al.  Voxel-Based Morphometry Using the RAVENS Maps: Methods and Validation Using Simulated Longitudinal Atrophy , 2001, NeuroImage.

[13]  J. Trojanowski,et al.  Prediction of MCI to AD conversion, via MRI, CSF biomarkers, and pattern classification , 2011, Neurobiology of Aging.

[14]  S. Resnick,et al.  An image-processing system for qualitative and quantitative volumetric analysis of brain images. , 1998, Journal of computer assisted tomography.

[15]  Janaina Mourão Miranda,et al.  Investigating the predictive value of whole-brain structural MR scans in autism: A pattern classification approach , 2010, NeuroImage.

[16]  Moo K. Chung,et al.  Spatially augmented LPboosting for AD classification with evaluations on the ADNI dataset , 2009, NeuroImage.

[17]  Yiping P. Du,et al.  Hippocampus volume loss due to chronic heavy drinking. , 2006, Alcoholism, clinical and experimental research.

[18]  S. Resnick,et al.  One-year age changes in MRI brain volumes in older adults. , 2000, Cerebral cortex.

[19]  Fei Wang,et al.  Cuts3vm: a fast semi-supervised svm algorithm , 2008, KDD.

[20]  Dinggang Shen,et al.  COMPARE: Classification of Morphological Patterns Using Adaptive Regional Elements , 2007, IEEE Transactions on Medical Imaging.

[21]  C. Jack,et al.  MRI and CSF biomarkers in normal, MCI, and AD subjects , 2009, Neurology.

[22]  John Platt,et al.  Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods , 1999 .

[23]  S. Resnick,et al.  Longitudinal Magnetic Resonance Imaging Studies of Older Adults: A Shrinking Brain , 2003, The Journal of Neuroscience.

[24]  S. Resnick,et al.  Longitudinal progression of Alzheimer's-like patterns of atrophy in normal older adults: the SPARE-AD index. , 2009, Brain : a journal of neurology.

[25]  et al.,et al.  Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline , 2008, NeuroImage.

[26]  C. Davatzikos Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to AD: Results from ADNI , 2009, Alzheimer's & Dementia.

[27]  D. Collins,et al.  Amnestic MCI future clinical status prediction using baseline MRI features , 2010, Neurobiology of Aging.

[28]  Alexander J. Smola,et al.  Advances in Large Margin Classifiers , 2000 .

[29]  John Q. Trojanowski,et al.  Healthy brain aging: A meeting report from the Sylvan M. Cohen Annual Retreat of the University of Pennsylvania Institute on Aging , 2008, Alzheimer's & Dementia.

[30]  D. Shen,et al.  Past adult lead exposure is linked to neurodegeneration measured by brain MRI , 2006, Neurology.

[31]  Nick C Fox,et al.  Automatic classification of MR scans in Alzheimer's disease. , 2008, Brain : a journal of neurology.

[32]  Yiping P. Du,et al.  Hypercortisolism in alcohol dependence and its relation to hippocampal volume loss. , 2006, Journal of studies on alcohol.