MIRIAD—Public release of a multiple time point Alzheimer's MR imaging dataset

Malone, Ian B.; Cash, David M.; Ridgway, Gerard R.; MacManus, David; Ourselin, Sébastien; Fox, Nick C.; Schott, Jonathan M.

doi:10.1016/j.neuroimage.2012.12.044

Cited by 128 publications

(99 citation statements)

References 32 publications

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“…Specifically, the analysis was carried out with the MIRIAD database (Malone et al., 2013) using the classifier trained with the OASIS database.…”

Section: Methodsmentioning

confidence: 99%

“…As described in (Malone et al., 2013), images were acquired with a 1.5 T Signa MRI scanner (GE Medical systems, Milwaukee, WI, USA), using a T1‐weighted inversion recovery‐prepared fast spoiled gradient recalled (IR‐FSPGR) sequence. Images were warped into the Talairach and Tournoux atlas and skull‐stripped using the FSL (Jenkinson, Beckmann, Behrens, Woolrich, & Smith, 2012) package.…”

Section: Methodsmentioning

confidence: 99%

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Characterization of brain anatomical patterns by comparing region intensity distributions: Applications to the description of Alzheimer's disease

et al. 2018

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PurposeThis work presents an automatic characterization of the Alzheimer's disease describing the illness as a multidirectional departure from a baseline defining the control state, being these directions determined by a distance between functional‐equivalent anatomical regions.MethodsAfter a brain parcellation, a region is described by its histogram of gray levels, and the Earth mover's distance establishes how close or far these regions are. The medoid of the control group is set as the reference and any brain is characterized by its set of distances to this medoid.EvaluationThis hypothesis was assessed by separating groups of patients with mild Alzheimer's disease and mild cognitive impairment from control subjects, using a subset of the Open Access Series of Imaging Studies (OASIS) database. An additional experiment evaluated the method generalization and consisted in training with the OASIS data and testing with the Minimal Interval Resonance Imaging in Alzheimer's disease (MIRIAD) database.ResultsClassification between controls and patients with AD resulted in an equal error rate of 0.1 (90% of sensitivity and specificity at the same time). The automatic ranking of regions resulting is in strong agreement with those regions described as important in clinical practice. Classification with different databases results in a sensitivity of 85% and a specificity of 91%.ConclusionsThis method automatically finds out a multidimensional expression of the AD, which is directly related to the anatomical changes in specific areas such as the hippocampus, the amygdala, the planum temporale, and thalamus.

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“…Specifically, the analysis was carried out with the MIRIAD database (Malone et al., 2013) using the classifier trained with the OASIS database.…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Characterization of brain anatomical patterns by comparing region intensity distributions: Applications to the description of Alzheimer's disease

et al. 2018

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“…We used the MIRIAD database, 23,24 to assess the transitivity properties of the symmetric and asymmetric approaches. The MIRIAD database consist of brain MRI scans from 46 subjects diagnosed with Alzheimer's disease and 23 age-matched healthy controls.…”

Section: Validation On the Miriad Databasementioning

confidence: 99%

Global image registration using a symmetric block-matching approach

et al. 2014

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Abstract. Most medical image registration algorithms suffer from a directionality bias that has been shown to largely impact subsequent analyses. Several approaches have been proposed in the literature to address this bias in the context of nonlinear registration, but little work has been done for global registration. We propose a symmetric approach based on a block-matching technique and least-trimmed square regression. The proposed method is suitable for multimodal registration and is robust to outliers in the input images. The symmetric framework is compared with the original asymmetric block-matching technique and is shown to outperform it in terms of accuracy and robustness. The methodology presented in this article has been made available to the community as part of the NiftyReg open-source package.

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“…Then, a bag of patches is extracted from the MR image of the testing subject and fed to the learned CNN model for classification. We have evaluated the effectiveness of our proposed LDMIL framework using baseline MR images in ADNI-1, ADNI-2 (Jack et al, 2008), and MIRIAD (Malone et al, 2013) datasets. Experimental results show that LDMIL outperforms the state-of-the-art methods in both AD classification and MCI conversion prediction tasks.…”

Section: Introductionmentioning

confidence: 99%

Landmark-based deep multi-instance learning for brain disease diagnosis

Liu

Zhang

Adeli

et al. 2018

Medical Image Analysis

361

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In conventional Magnetic Resonance (MR) image based methods, two stages are often involved to capture brain structural information for disease diagnosis, i.e., 1) manually partitioning each MR image into a number of regions-of-interest (ROIs), and 2) extracting pre-defined features from each ROI for diagnosis with a certain classifier. However, these pre-defined features often limit the performance of the diagnosis, due to challenges in 1) defining the ROIs and 2) extracting effective disease-related features. In this paper, we propose a landmark-based deep multi-instance learning (LDMIL) framework for brain disease diagnosis. Specifically, we first adopt a data-driven learning approach to discover disease-related anatomical landmarks in the brain MR images, along with their nearby image patches. Then, our LDMIL framework learns an end-to-end MR image classifier for capturing both the local structural information conveyed by image patches located by landmarks and the global structural information derived from all detected landmarks. We have evaluated our proposed framework on 1526 subjects from three public datasets (i.e., ADNI-1, ADNI-2, and MIRIAD), and the experimental results show that our framework can achieve superior performance over state-of-the-art approaches.

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MIRIAD—Public release of a multiple time point Alzheimer's MR imaging dataset

Cited by 128 publications

References 32 publications

Characterization of brain anatomical patterns by comparing region intensity distributions: Applications to the description of Alzheimer's disease

Characterization of brain anatomical patterns by comparing region intensity distributions: Applications to the description of Alzheimer's disease

Global image registration using a symmetric block-matching approach

Landmark-based deep multi-instance learning for brain disease diagnosis

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