[O1‐03‐03]: Predicting MCI progression to AD via automated analysis of T1‐weighted MR image intensity

Duchesne, Simon; Sousa, Kathy De; Bocti, Christian; Chertkow, Howard; Collins, D. Louis

doi:10.1016/j.jalz.2005.06.294

Alzheimer's & Dementia

2005

DOI: 10.1016/j.jalz.2005.06.294

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[O1‐03‐03]: Predicting MCI progression to AD via automated analysis of T1‐weighted MR image intensity

Simon Duchesne

Kathy De Sousa

Christian Bocti

et al.

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“…Our original contribution in ACC design is the combination of intensity and local shape descriptor [17] extracted from a large volume of interest (VOI), avoiding the pitfalls of structure segmentation and modeling de facto neighboring tissue interactions. Thus, the ACC assessment is based on estimating the individual subject's propensity at exhibiting pathology-specific patterns of covarying tissue changes within the VOI; these can be expressed as areas of signal changes or tissue atrophy [17], [26] and related to biological phenomena. This is in line with recent findings in aging and probable AD [27]- [29].…”

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confidence: 99%

MRI-Based Automated Computer Classification of Probable AD Versus Normal Controls

Duchesne

Caroli

Geroldi

et al. 2008

IEEE Trans. Med. Imaging

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Automated computer classification (ACC) techniques are needed to facilitate physician's diagnosis of complex diseases in individual patients. We provide an example of ACC using computational techniques within the context of cross-sectional analysis of magnetic resonance images (MRI) in neurodegenerative diseases, namely Alzheimer's dementia (AD). In this paper, the accuracy of our ACC methodology is assessed when presented with real life, imperfect data, i.e., cohorts of MRI with varying acquisition parameters and imaging quality. The comparative methodology uses the Jacobian determinants derived from dense deformation fields and scaled grey-level intensity from a selected volume of interest centered on the medial temporal lobe. The ACC performance is assessed in a series of leave-one-out experiments aimed at separating 75 probable AD and 75 age-matched normal controls. The resulting accuracy is 92% using a support vector machine classifier based on least squares optimization. Finally, it is shown in the Appendix that determinants and scaled grey-level intensity are appreciably more robust to varying parameters in validation studies using simulated data, when compared to raw intensities or grey/white matter volumes. The ability of cross-sectional MRI at detecting probable AD with high accuracy could have profound implications in the management of suspected AD candidates.

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confidence: 99%

MRI-Based Automated Computer Classification of Probable AD Versus Normal Controls

Duchesne

Caroli

Geroldi

et al. 2008

IEEE Trans. Med. Imaging

Self Cite

139

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MR-based neurological disease classification methodology: Application to lateralization of seizure focus in temporal lobe epilepsy

Duchesne

Bernasconi

et al. 2006

NeuroImage

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[O1‐03‐03]: Predicting MCI progression to AD via automated analysis of T1‐weighted MR image intensity

Cited by 2 publications

References 0 publications

MRI-Based Automated Computer Classification of Probable AD Versus Normal Controls

MRI-Based Automated Computer Classification of Probable AD Versus Normal Controls

MR-based neurological disease classification methodology: Application to lateralization of seizure focus in temporal lobe epilepsy

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