Fast and robust extraction of hippocampus from MR images for diagnostics of Alzheimer's disease
Knowledge Intensive Services, VTT Technical Research Centre of Finland, Tampere, Finland. NeuroImage
(Impact Factor: 6.36).
05/2011; 56(1):185-96. DOI: 10.1016/j.neuroimage.2011.01.062
Assessment of temporal lobe atrophy from magnetic resonance images is a part of clinical guidelines for the diagnosis of prodromal Alzheimer's disease. As hippocampus is known to be among the first areas affected by the disease, fast and robust definition of hippocampus volume would be of great importance in the clinical decision making. We propose a method for computing automatically the volume of hippocampus using a modified multi-atlas segmentation framework, including an improved initialization of the framework and the correction of partial volume effect. The method produced a high similarity index, 0.87, and correlation coefficient, 0.94, with semi-automatically generated segmentations. When comparing hippocampus volumes extracted from 1.5T and 3T images, the absolute value of the difference was low: 3.2% of the volume. The correct classification rate for Alzheimer's disease and cognitively normal cases was about 80% while the accuracy 65% was obtained for classifying stable and progressive mild cognitive impairment cases. The method was evaluated in three cohorts consisting altogether about 1000 cases, the main emphasis being in the analysis of the ADNI cohort. The computation time of the method is about 2 minutes on a standard laptop computer. The results show a clear potential for applying the method in clinical practice.
Available from: Ian Brian Malone
- "Furthermore, the differences between mean Jaccard index comparing a second manual segmentor (S2) and HMAPS between 1.5T and 3T scans were also small, suggesting that the accuracy of HMAPS is not segmentor dependent. Our results are consistent with Lötjönen et al. (2011) who examined differences in hippocampal segmentations using an automated multi-atlas segmentation method on 1.5T and 3T images and found a high ICC of 0.98 between 1.5T and 3T segmentations. Our data gave a comparable ICC of 0.97 between 1.5T and 3T HMAPS segmentations. "
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ABSTRACT: Hippocampal volumetric measures may be useful for Alzheimer’s disease (AD) diagnosis and disease tracking; however, manual segmentation of the hippocampus is labour-intensive. Therefore, automated techniques are necessary for large studies and to make hippocampal measures feasible for clinical use. As large studies and clinical centres are moving from using 1.5 Tesla (T) scanners to higher field strengths it is important to assess whether specific image processing techniques can be used at these field strengths. This study investigated whether an automated hippocampal segmentation technique (HMAPS: hippocampal multi-atlas propagation and segmentation) and volume change measures (BSI: boundary shift integral) were as accurate at 3T as at 1.5T. Eighteen Alzheimer’s disease patients and 18 controls with 1.5T and 3T scans at baseline and 12-month follow-up were used from the Alzheimer’s Disease Neuroimaging Initiative cohort. Baseline scans were segmented manually and using HMAPS and their similarity was measured by the Jaccard index. BSIs were calculated for serial image pairs. We calculated pair-wise differences between manual and HMAPS rates at 1.5T and 3T and compared the SD of these differences at each field strength. The difference in mean Jaccards (manual and HMAPS) between 1.5T and 3T was small with narrow confidence intervals (CIs) and did not appear to be segmentor dependent. The SDs of the difference between volumes from manual and automated segmentations were similar at 1.5T and 3T, with a relatively narrow CI for their ratios. The SDs of the difference between BSIs from manual and automated segmentations were also similar at 1.5T and 3T but with a wider CI for their ratios. This study supports the use of our automated hippocampal voluming methods, developed using 1.5T images, with 3T images.
Neuroinformatics 07/2014; 12(3). DOI:10.1007/s12021-013-9217-y · 2.83 Impact Factor
- "Structures in the medial temporal lobe areas, in particular hippocampus and entorhinal cortex, are reported as the first regions to be atrophied in AD patients. Hippocampal volume    and shape   have been used as input features of classifiers. Furthermore, other less common types of features such as spatial frequency representation of cortical thickness data , and spatially normalized tissue density maps  have been investigated. "
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ABSTRACT: Machine learning algorithms and multivariate data analysis methods have been widely utilized in the field of Alzheimer's disease (AD) research in recent years. Advances in medical imaging and medical image analysis have provided a means to generate and extract valuable neuroimaging information. Automatic classification techniques provide tools to analyze this information and observe inherent disease-related patterns in the data. In particular, these classifiers have been used to discriminate AD patients from healthy control subjects and to predict conversion from mild cognitive impairment to AD. In this paper, recent studies are reviewed that have used machine learning and multivariate analysis in the field of AD research. The main focus is on studies that used structural magnetic resonance imaging (MRI), but studies that included positron emission tomography and cerebrospinal fluid biomarkers in addition to MRI are also considered. A wide variety of materials and methods has been employed in different studies, resulting in a range of different outcomes. Influential factors such as classifiers, feature extraction algorithms, feature selection methods, validation approaches, and cohort properties are reviewed, as well as key MRI-based and multi-modal based studies. Current and future trends are discussed.
Journal of Alzheimer's disease: JAD 04/2014; 41(3). DOI:10.3233/JAD-131928 · 4.15 Impact Factor
Available from: Saar Oz
- "To quantify tubulin polymerization, a simple assay was developed by modifying a method originally described , . Cells grown to confluence in 6-well plates were washed with MT-buffer w/o TritonX-100 and lysed at 37°C for 5 min, with 150 µl of MT-buffer with TritonX-100 in order to extract soluble (cytosolic) tubulin (S); Pelleted cells were rinsed once again with equal volume of modified RIPA buffer in order to collect the polymerized (cytoskeletal) tubulin (P). "
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ABSTRACT: Microtubules (MTs), key cytoskeletal elements in living cells, are critical for axonal transport, synaptic transmission, and maintenance of neuronal morphology. NAP (NAPVSIPQ) is a neuroprotective peptide derived from the essential activity-dependent neuroprotective protein (ADNP). In Alzheimer's disease models, NAP protects against tauopathy and cognitive decline. Here, we show that NAP treatment significantly affected the alpha tubulin tyrosination cycle in the neuronal differentiation model, rat pheochromocytoma (PC12) and in rat cortical astrocytes. The effect on tubulin tyrosination/detyrosination was coupled to increased MT network area (measured in PC12 cells), which is directly related to neurite outgrowth. Tubulin beta3, a marker for neurite outgrowth/neuronal differentiation significantly increased after NAP treatment. In rat cortical neurons, NAP doubled the area of dynamic MT invasion (Tyr-tubulin) into the neuronal growth cone periphery. NAP was previously shown to protect against zinc-induced MT/neurite destruction and neuronal death, here, in PC12 cells, NAP treatment reversed zinc-decreased tau-tubulin-MT interaction and protected against death. NAP effects on the MT pool, coupled with increased tau engagement on compromised MTs imply an important role in neuronal plasticity, protecting against free tau accumulation leading to tauopathy. With tauopathy representing a major pathological hallmark in Alzheimer's disease and related disorders, the current findings provide a mechanistic basis for further development. NAP (davunetide) is in phase 2/3 clinical trial in progressive supranuclear palsy, a disease presenting MT deficiency and tau pathology.
PLoS ONE 12/2012; 7(12):e51458. DOI:10.1371/journal.pone.0051458 · 3.23 Impact Factor
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