Segmentation compétitive de l'hippocampe et de l'amygdale à partir de volumes IRM

L'hippocampe et l'amygdale sont deux structures cérébrales intervenant dans plusieurs fonctions cognitives fondamentales. Leur segmentation est un outil essentiel pour mesurer leur atteinte dans certaines pathologies neurologiques, mais elle est rendue difficile par leur complexité. Nous considérons leur segmentation simultanée par une méthode de déformation homotopique compétitive de régions. celle-ci est guidée par des connaissances anatomiques relationnelles, et non des a priori statistiques, pour pouvoir considérer des structures atrophiées. Rapide, l'algorithme donne des résultats satisfaisants pour les deux structures par rapport à la segmentation manuelle et à la littérature

Automatic Morphological Analysis of Medial Temporal Lobe

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Competitive segmentation of the hippocampus and the amygdala from MRI scans

The hippocampus and the amygdala are two brain structures which play a central role in several fundamental cognitive processes. Their segmentation from Magnetic Resonance Imaging (MRI) scans is a unique way to measure their atrophy in some neurological diseases, but it is made difficult by their complex geometry. Their simultaneous segmentation is considered here through a competitive homotopic region growing method. It is driven by relational anatomical knowledge, which enables to consider the segmentation of atrophic structures in a straightforward way. For both structures, this fast algorithm gives results which are comparable to manual segmentation with a better reproducibility. Its performances regarding segmentation quality, automation and computation time, are amongst the best published data.L’hippocampe et l’amygdale sont deux structures cérébrales intervenant dans plusieurs fonctions cognitives fondamentales. Leur segmentation, à partir de volumes d’imagerie par résonance magnétique (IRM), est un outil essentiel pour mesurer leur atteinte dans certaines pathologies neurologiques, mais elle est rendue difficile par leur géométrie complexe. Nous considérons leur segmentation simultanée par une méthode de déformation homotopique compétitive de régions. Celle-ci est guidée par des connaissances anatomiques relationnelles ; ceci permet de considérer directement des structures atrophiées. Rapide, l’algorithme donne, pour les deux structures, des résultats comparables à la segmentation manuelle avec une meilleure reproductibilité. Ses performances, concernant la qualité de la segmentation, le degré d’automatisation et le temps de calcul, sont parmi les meilleures de la littérature

Cerebral atrophy in mild cognitive impairment and Alzheimer disease: rates and acceleration.

OBJECTIVE: To quantify the regional and global cerebral atrophy rates and assess acceleration rates in healthy controls, subjects with mild cognitive impairment (MCI), and subjects with mild Alzheimer disease (AD). METHODS: Using 0-, 6-, 12-, 18-, 24-, and 36-month MRI scans of controls and subjects with MCI and AD from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, we calculated volume change of whole brain, hippocampus, and ventricles between all pairs of scans using the boundary shift integral. RESULTS: We found no evidence of acceleration in whole-brain atrophy rates in any group. There was evidence that hippocampal atrophy rates in MCI subjects accelerate by 0.22%/year2 on average (p = 0.037). There was evidence of acceleration in rates of ventricular enlargement in subjects with MCI (p = 0.001) and AD (p < 0.001), with rates estimated to increase by 0.27 mL/year2 (95% confidence interval 0.12, 0.43) and 0.88 mL/year2 (95% confidence interval 0.47, 1.29), respectively. A post hoc analysis suggested that the acceleration of hippocampal loss in MCI subjects was mainly driven by the MCI subjects that were observed to progress to clinical AD within 3 years of baseline, with this group showing hippocampal atrophy rate acceleration of 0.50%/year2 (p = 0.003). CONCLUSIONS: The small acceleration rates suggest a long period of transition to the pathologic losses seen in clinical AD. The acceleration in hippocampal atrophy rates in MCI subjects in the ADNI seems to be driven by those MCI subjects who concurrently progressed to a clinical diagnosis of AD

Reproducibility of hippocampal atrophy rates measured with manual, FreeSurfer, AdaBoost, FSL/FIRST and the MAPS-HBSI methods in Alzheimer's disease

The purpose of this study is to assess the reproducibility of hippocampal atrophy rate measurements of commonly used fully-automated algorithms in Alzheimer disease (AD). The reproducibility of hippocampal atrophy rate for FSL/FIRST, AdaBoost, FreeSurfer, MAPS independently and MAPS combined with the boundary shift integral (MAPS-HBSI) were calculated. Back-to-back (BTB) 3D T1-weighted MPRAGE MRI from the Alzheimer's Disease Neuroimaging Initiative (ADNI1) study at baseline and year one were used. Analysis on 3 groups of subjects was performed – 562 subjects at 1.5 T, a 75 subject group that also had manual segmentation and 111 subjects at 3 T. A simple and novel statistical test based on the binomial distribution was used that handled outlying data points robustly. Median hippocampal atrophy rates were −1.1%/year for healthy controls, −3.0%/year for mildly cognitively impaired and −5.1%/year for AD subjects. The best reproducibility was observed for MAPS-HBSI (1.3%), while the other methods tested had reproducibilities at least 50% higher at 1.5 T and 3 T which was statistically significant. For a clinical trial, MAPS-HBSI should require less than half the subjects of the other methods tested. All methods had good accuracy versus manual segmentation. The MAPS-HBSI method has substantially better reproducibility than the other methods considered

Visual ratings of atrophy in MCI: prediction of conversion and relationship with CSF biomarkers.

Medial temporal lobe atrophy (MTA) and cerebrospinal fluid (CSF) markers of Alzheimer's disease (AD) pathology may aid the early detection of AD in mild cognitive impairment (MCI). However, the relationship between structural and pathological markers is not well understood. Furthermore, while posterior atrophy (PA) is well recognized in AD, its value in predicting conversion from late-onset amnestic MCI to AD is unclear. In this study we used visual ratings of MTA and PA to assess their value in predicting conversion to AD in 394 MCI patients. The relationship of atrophy patterns with CSF Aβ1-42, tau, and p-tau(181) was further investigated in 114 controls, 192 MCI, and 99 AD patients. There was a strong association of MTA ratings with conversion to AD (p < 0.001), with a weaker association for PA ratings (p = 0.047). Specific associations between visual ratings and CSF biomarkers were found; MTA was associated with lower levels of Aβ1-42 in MCI, while PA was associated with elevated levels of tau in MCI and AD, which may reflect widespread neuronal loss including posterior regions. These findings suggest both that posterior atrophy may predict conversion to AD in late-onset MCI, and that there may be differential relationships between CSF biomarkers and regional atrophy patterns

Computer aided diagnosis in temporal lobe epilepsy and Alzheimer's dementia

Computer aided diagnosis within neuroimaging must rely on advanced image processing techniques to detect and quantify subtle signal changes that may be surrogate indicators of disease state. This thesis proposes two such novel methodologies that are both based on large volumes of interest, are data driven, and use cross-sectional scans: appearance-based classification (ABC) and voxel-based classification (VBC).The concept of appearance in ABC represents the union of intensity and shape information extracted from magnetic resonance images (MRI). The classification method relies on a linear modeling of appearance features via principal components analysis, and comparison of the distribution of projection coordinates for the populations under study within a reference multidimensional appearance eigenspace. Classification is achieved using forward, stepwise linear discriminant analyses, in multiple cross-validated trials. In this work, the ABC methodology is shown to accurately lateralize the seizure focus in temporal lobe epilepsy (TLE), differentiate normal aging individuals from patients with either Alzheimer's dementia (AD) or Mild Cognitive Impairment (MCI), and finally predict the progression of MCI patients to AD. These applications demonstrated that the ABC technique is robust to different signal changes due to two distinct pathologies, to low resolution data and motion artifacts, and to possible differences inherent to multi-site acquisition.The VBC technique relies on voxel-based morphometry to identify regions of grey and white matter concentration differences between co-registered cohorts of individuals, and then on linear modeling of variables extracted from these regions. Classification is achieved using linear discriminant analyses within a multivariate space composed of voxel-based morphometry measures related to grey and white matter concentration, along with clinical variables of interest. VBC is shown to increase the accuracy of prediction of one-year clinical status from three to four out of five TLE patients having undergone selective amygdalo-hippocampectomy. These two techniques are shown to have the necessary potential to solve current problems in neurological research, assist clinical physicians with their decision-making process and influence positively patient management

Computational processing and analysis of ear images

Tese de mestrado. Engenharia Biomédica. Faculdade de Engenharia. Universidade do Porto. 201

Nuevos métodos para el análisis automático del volumen de estructuras cerebrales a partir de imágenes de resonancia magnética nuclear

En la actualidad, la medicina y en especial el área de imagen médica, ha sido uno de los campos de la ciencia que más se ha beneficiado de las tecnologías de la información. En esta tesis nos centramos en las imágenes de resonancia magnética cerebral y los métodos de segmentación automática del volumen cerebral. En el primer capítulo describimos los antecedentes de este trabajo mediante una breve introducción de los principios físicos de la resonancia magnética y una revisión del estado del arte en relación con las técnicas de segmentación y su evolución a lo largo de las últimas décadas. En los capítulos tres, cuatro y cinco presentamos tres métodos de segmentación automática de diferentes partes del cerebro que mejoran el estado del arte en términos de calidad del resultado y velocidad. Finalmente, en el capítulo seis se comentan las conclusiones generales y se proponen líneas de desarrollo futuras.Nowadays, medicine and specially the medical image area, is one of the science fields that has benefited more from the information technologies. In this thesis we focus on cerebral magnetic resonance imaging and automatic segmentation methods of the brain volume. In the first chapter we describe a background for this work by a brief introduction of the physic fundaments of magnetic resonance and a revision of the state-of-the-art regarding to segmentation techniques and its evolution along the last decades. In the chapters three, four and five we present three methods for automatically segment different parts of the brain that leverage the state-of-the-art in terms of result quality and computation time. Finally, in chapter six we comment the general conclusions and propose lines for future works.En l'actualitat, la medicina i en especial l'àrea d'imatge mèdica, ha estat un dels camps de la ciència que més s'ha beneficiat de les tecnologies de la informació. En aquesta tesi ens centrem en les imatges de ressonància magnètica cerebral i el desenvolupament de mètodes de segmentaciò automàtica del volum cerebral. En el primer capítol descrvim els antecedents d'aquest treball mitjançant una breu introducció dels principis físics de la ressonància magnètica i una revisió de l'estat de l'art en relació amb les tècniques de segmentació i la seva evolució al llarg de les últimes dècades. En els capítols tres, quatre, i cinc presentem tres mètodes de segmentació automàtica per a diferents parts del cervell que milloren l'estat de l'art en quant a qualitat dels seus resultats i velocitat. Finalment, en el capitol sis es comenten les conclusions generals i es proposen línies de desenvolupament ions generals i es proposen línies de desenvolupament futures.Romero Gómez, JE. (2018). Nuevos métodos para el análisis automático del volumen de estructuras cerebrales a partir de imágenes de resonancia magnética nuclear [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/10634