Comparison of accuracy between FSL’s FIRST and Freesurfer for caudate nucleus and putamen segmentation

AbstractAlthough several methods have been developed to automatically delineate subcortical gray matter structures from MR images, the accuracy of these algorithms has not been comprehensively examined. Most of earlier studies focused primarily on the hippocampus. Here, we assessed the accuracy of two widely used non-commercial programs (FSL-FIRST and Freesurfer) for segmenting the caudate and putamen. T1-weighted 1 mm3 isotropic resolution MR images were acquired for thirty healthy subjects (15 females). Caudate nucleus and putamen were segmented manually by two independent observers and automatically by FIRST and Freesurfer (v4.5 and v5.3). Utilizing manual labels as reference standard the following measures were studied: Dice coefficient (D), percentage volume difference (PVD), absolute volume difference as well as intraclass correlation coefficient (ICC) for consistency and absolute agreement. For putamen segmentation, FIRST achieved higher D, lower PVD and higher ICC for absolute agreement with manual tracing than either version of Freesurfer. Freesurfer overestimated the putamen, while FIRST was not statistically different from manual tracing. The ICC for consistency with manual tracing was similar between the two methods. For caudate segmentation, FIRST and Freesurfer performed more similarly. In conclusion, Freesurfer and FIRST are not equivalent when comparing to manual tracing. FIRST was superior for putaminal segmentation.</jats:p

Simultaneous segmentation and grading of anatomical structures for patient's classification: application to Alzheimer's Disease

Data used in the preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (www.loni.ucla.edu/ADNI).In this paper, we propose an innovative approach to robustly and accurately detect Alzheimer's disease (AD) based on the distinction of specific atrophic patterns of anatomical structures such as hippocampus (HC) and entorhinal cortex (EC). The proposed method simultaneously performs segmentation and grading of structures to efficiently capture the anatomical alterations caused by AD. Known as SNIPE (Scoring by Non-local Image Patch Estimator), the novel proposed grading measure is based on a nonlocal patch-based frame-work and estimates the similarity of the patch surrounding the voxel under study with all the patches present in different training populations. In this study, the training library was composed of two populations: 50 cognitively normal subjects (CN) and 50 patients with AD, randomly selected from the ADNI database. During our experiments, the classification accuracy of patients (CN vs. AD) using several biomarkers was compared: HC and EC volumes, the grade of these structures and finally the combination of their volume and their grade. Tests were completed in a leave-one-out framework using discriminant analysis. First, we showed that biomarkers based on HC provide better classification accuracy than biomarkers based on EC. Second, we demonstrated that structure grading is a more powerful measure than structure volume to distinguish both populations with a classification accuracy of 90%. Finally, by adding the ages of subjects in order to better separate age-related structural changes from disease-related anatomical alterations, SNIPE obtained a classification accuracy of 93%Data collection and sharing for this project were funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904). ADNI is funded by the National Insti- tute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Abbott, AstraZeneca AB, Bayer Schering Pharma AG, Bristol-Myers Squibb, Eisai Global Clinical Development, Elan Corporation, Genentech, GE Healthcare, GlaxoSmithKline, Innogenetics, Johnson and Johnson, Eli Lilly and Co., Medpace, Inc., Merck and Co., Inc., Novartis AG, Pfizer Inc, F. Hoffman-La Roche, Schering-Plough, Synarc, Inc., as well as non-profit partners the Alzheimer's Association and Alzheimer's Drug Discovery Foundation, with participation from the U.S. Food and Drug Administration. Private sector contributions to ADNI are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of California, Los Angeles. This research was also supported by NIH grants P30AG010129, K01 AG030514, and the Dana Foundation.Coupé, P.; Eskildsen, SF.; Manjón Herrera, JV.; Fonov, VS.; Collins, DL.; Alzheimer's Dis Neuroimaging (2012). Simultaneous segmentation and grading of anatomical structures for patient's classification: application to Alzheimer's Disease. NeuroImage. 59(4):3736-3747. https://doi.org/10.1016/j.neuroimage.2011.10.080S3736374759

Optimized, automated shimming procedure for improved experimental cardiac magnetic resonance imaging and spectroscopy at ultra-high magnetic fields

Background: As técnicas de ressonância magnética cardíaca por imagem (MRI) e espetroscopia (MRS) são ferramentas usadas para caraterizar, de forma não invasiva, modelos de rato com doenças cardíacas humanas. As experiências são tipicamente conduzidas em sistemas de Ressonância Magnética (MR) equipados com magnetos de elevada intensidade (≥ 7 Tesla). Um requisito fundamental da MR é a homogeneidade do campo magnético estático, B0 (Grutter, 1993), e as flutuações (inomogeneidades) do campo magnético principal na região de imagem devem ser menores a três partes por milhão (3 ppm). Inserindo uma amostra aumenta-se a inomogeneidade do campo (devido a diferentes graus de magnetização ao longo da amostra como resposta a B0 ("suscetibilidade magnética")), a qual necessita de ser compensada (Crijns et al, 2011; Koch et al, 2006). Homogeneizar (shimming) o campo magnético estático é uma tarefa crucial em qualquer experiência de MR para maximizar a resolução e a razão entre sinal e ruído. Isto é particularmente importante em campos magnéticos de elevada intensidade devido à dependência linear da suscetibilidade magnética com B0. O ajuste manual das bobinas de shim é laborioso e subjetivo. Para além disso, este processo é particularmente desafiante onde vários tecidos (por exemplo, osso, fluxo de sangue, entre outros) estão numa vizinhança próxima dentro do tórax, tendo cada um diferentes suscetibilidades magnéticas e movimentos relativos. Métodos automáticos de shimming, como o FASTMAP ou FASTERMAP (Shen et al, 1997), estão experimental e clinicamente bem estabelecidos no tecido cerebral mas falham no coração devido à fase de sinal mal definida de MR, particularmente no interior dos ventrículos. Com base numa técnica previamente implementada para o cérebro humano, foi investigada a implementação de uma nova abordagem para corações de ratos, in vivo, capaz de homogeneizar B0 na região de interesse, com uma forma aleatória. Objetivo: O objetivo deste projeto é investigar os parâmetros ótimos de digitalização e pós-processamento, por forma a otimizar e alcançar um procedimento automático de shimming, potenciando, assim, as técnicas de MRI e MRS cardíacas. Métodos: Diversos ratos (n=5) foram submetidos à técnica de MR, realizada num magneto horizontal de 9.4 Tesla (T). A aquisição de imagem foi conduzida através de sequências rápidas echo variando os seguintes parâmetros: resolução, compensação de fluxo (on / off), orientação (short-axis / axial) e dimensão (multi-cortes 2D vs 3D). Três diferentes configurações de bobinas de shim foram investigadas e a sequência ótima de MR foi avaliada. Resultados: O nível de 17% de threshold demonstrou ser aceitável para a remoção das discontinuidades de fase. A análise quantitativa do desempenho das diferentes abordagens de phase unwrapping mostrou que a abordagem 3D é a mais eficaz na resolução das discontinuidades de fase presentes nos mapas de campo. A aplicação de orientação axial, os dados de maior resolução, a ausência de compensação de fluxo e a introdução de bobinas de shim de maiores ordens demonstraram um peso significativo na redução das inomogeneidades de B0, quando aplicados. Conclusões: Este projeto permitiu estabelecer parâmetros ótimos de aquisição e opções de pós-processamento que melhoram a homogeneidade de B0, importantes na validação de futuros estudos complementares.Background: Cardiac magnetic resonance imaging and spectroscopy are tools to non-invasively characterize rodent models of human heart disease. The experiments are typically carried out on dedicated MR systems equipped with ultra-high field magnets (≥ 7 Tesla). One fundamental requirement of MR is the homogeneity of the static magnetic field B0 (Grutter, 1993), and fluctuations of the main magnetic field (B0 inhomogeneities) within the scan region should be less than three parts per million (3 ppm). Inserting a sample inherently increases the field inhomogeneity (due to different degree of magnetization across the sample in response to the B0 field (“magnetic susceptibility”)), which needs to be compensated for (Crijns et al, 2011; Koch et al, 2006). Homogenizing (i.e. shimming) the static magnetic field is crucial for any MR experiment in order to maximize resolution and signal-to-noise. This is particularly important at ultra-high magnetic fields due to linear dependence of magnetic susceptibility. Adjusting the three linear and typically up to 14 higher order shims manually is laborious and subjective. Moreover, this process is particularly challenging where various tissues (i.e. heart and skeletal muscle, bone, lungs and flowing blood) are in close vicinity within the chest, each having different magnetic susceptibilities and relative motions. Auto-shim methods such as FASTMAP or FASTERMAP (Shen et al, 1997), are clinically and experimentally well established in brain tissue, but inevitably fail in the heart due to the ill-defined phase of the MR-signal, particularly inside the ventricles. Based on a technique, previously applied to human brain – implemented a novel approach for the application to mouse hearts in vivo, that is able to homogenize the B0-field in an arbitrarily shaped, but connected region of interest. Aim: The aim of this project is to investigate optimal scan parameters and post-processing approach to optimize and advance an automated shimming procedure for improved experimental cardiac magnetic resonance imaging and spectroscopy at ultra-high magnetic fields. Methods: Mice (n = 5) underwent MR experiments carried out in a 9.4 Tesla (T) horizontal magnet. The image acquisition was performed using fast gradient echo sequences varying the following parameters: resolution, flow compensation on / off, orientation (short-axis / axial), and dimension (2D multislice vs 3D). Three different shim coils’ configurations (shim coils up to the third order) were investigated and optimal MR sequence was assessed. Results: The threshold level of 17% proved to be acceptable for removal of phase discontinuities and hence it was used in subsequent studies. Quantitative analysis of the performance of different phase unwrapping approaches showed that the 3D approach is the most effective in resolving phase discontinuities present in field maps. The application of axial orientation, highest resolution data, absence of compensation flow and the introduction of higher order shim coils showed a significant reduction of B0 inhomogeneities when applied. Conclusions: This project established optimal acquisition parameters and post-processing options to improve the homogeneity of B0, and will aid the validation process in further follow-up studies

Segmentación del hipocampo en imágenes de resonancia magnética utilizando un modelo de forma activa

Actualmente, el uso de las imágenes médicas tiene impacto en el área clínica, gracias a que el desarrollo científico y tecnológico permite a los médicos hacer un análisis y diagnóstico para distintas patologías del cerebro y otras estructuras anatómicas. La segmentación del área del hipocampo es de interés en el área médica, debido a que se considera un biomarcador para el diagnóstico de patologías neurológicas y psiquiátricas, incluyendo enfermedad de Alzheimer (EA), epilepsia y esquizofrenia (Dill et al., 2015; Boccardi et al., 2015), así como para revelar las diferencias anatómicas (atrofia) de personas, debido al envejecimiento o la demencia (Kim et al., 2013), su anatomía puede ser analizada con neuroimágenes médicas, por ejemplo, las imágenes de resonancia magnética (IRM). Dicha segmentación de la estructura anatómica puede ser de forma manual, semiautomática o automática. En esta tesis se evalúa un método para la segmentación de la forma del hipocampo en imágenes de resonancia magnética, utilizando un modelo de forma activa (ASM, por sus siglas en inglés Active Shape Model), el cual es utilizado en dos etapas: entrenamiento y ajuste. En la etapa del entrenamiento de ASM se utiliza un conjunto de imágenes segmentadas manualmente que sirven para formar un modelo de distribución de puntos (MDP), donde cada forma es representada por un conjunto de puntos que describen el borde de una estructura. Por otra parte, la etapa de ajuste consiste en segmentar nuevas formas en el que se analizan los niveles de gris alrededor de cada punto de referencia de la forma. Además, se utiliza una métrica de distancia (distancia euclidiana) con la que se mide la distancia entre los puntos de la segmentación manual y la segmentación ajustada con ASM para obtener los errores de ajuste. El modelo de forma activa fue construido con 41 imágenes de resonancia magnética tomadas de la base de datos Alzheimer’s Disease Neuroimagen Initiative (ADNI), de la Universidad del Sur de California (University, 2020). Las imágenes del conjunto de entrenamiento fueron marcadas con 30 puntos de referencia, dado que el hipocampo es una estructura anatómica cuya dimensión es de 4 a 4.5 cm de longitud y de 1 a 1.5 cm de ancho (Duvernoy, 2013). Se presenta una experimentación para validar el nivel de ajuste del modelo de forma activa, previo a una consistente revisión literaria del estado del arte. La experimentación de ajuste se realiza utilizando la técnica leave one out. En dicha experimentación se obtiene un error de ajuste medio de 1.85 mm, el cual está por debajo del máximo error permisible (2 mm) en diagnósticos clínicos (Yue et al., 2006), lo cual indica que son resultados aceptables. Por otra parte, se obtuvo el coeficiente de similitud de Dice (DSC, por sus siglas en inglés Dice Similarity Coefficient) para cuantificar la precisión de la segmentación. El resultado del DSC medio es de 62 %, lo cual indica un resultado por debajo del valor aceptable que es de 80%.PNPC CONACY

Novel Fast Marching for Automated Segmentation of the Hippocampus (FMASH): method and validation on clinical data.

With hippocampal atrophy both a clinical biomarker for early Alzheimer's Disease (AD) and implicated in many other neurological and psychiatric diseases, there is much interest in the accurate, reproducible delineation of this region of interest (ROI) in structural MR images. Here we present Fast Marching for Automated Segmentation of the Hippocampus (FMASH): a novel approach using the Sethian Fast Marching (FM) technique to grow a hippocampal ROI from an automatically-defined seed point. Segmentation performance is assessed on two separate clinical datasets, utilising expert manual labels as gold standard to quantify Dice coefficients, false positive rates (FPR) and false negative rates (FNR). The first clinical dataset (denoted CMA) contains normal controls (NC) and atrophied AD patients, whilst the second is a collection of NC and bipolar (BP) patients (denoted BPSA). An optimal and robust stopping criterion is established for the propagating FM front and the final FMASH segmentation estimates compared to two commonly-used methods: FIRST/FSL and Freesurfer (FS). Results show that FMASH outperforms both FIRST and FS on the BPSA data, with significantly higher Dice coefficients (0.80±0.01) and lower FPR. Despite some intrinsic bias for FIRST and FS on the CMA data, due to their training, FMASH performs comparably well on the CMA data, with an average bilateral Dice coefficient of 0.82±0.01. Furthermore, FMASH most accurately captures the hippocampal volume difference between NC and AD, and provides a more accurate estimation of the problematic hippocampus-amygdala border on both clinical datasets. The consistency in performance across the two datasets suggests that FMASH is applicable to a range of clinical data with differing image quality and demographics

Automated detection of depression from brain structural magnetic resonance imaging (sMRI) scans

&nbsp;Automated sMRI-based depression detection system is developed whose components include acquisition and preprocessing, feature extraction, feature selection, and classification. The core focus of the research is on the establishment of a new feature selection algorithm that quantifies the most relevant brain volumetric feature for depression detection at an individual level

Clasificadores basados en máquinas de soporte vertical para el diagnóstico y predicción de la enfermedas de Alzheimer

La enfermedad de Alzheimer (AD) es un desorden neurodegenerativo cuyo diagnóstico clinico es realizado después de excluir otros tipos de demencia y el diagnóstico clinico definitivo requiere además de la presencia de un alto decit cognitivo, la confirmación histológica mediante autopsia de la presencia de proteinas como las neurobras de tau ( ) y amyloid beta (A) en los tejidos cerebrales. AD es una de las enfermedades de mayor impacto social en Europa y América. En el pasado 2005, en Europa fueron diagnosticados 3.600.000 pacientes afectados por AD (fuente: Frost & Sullivan) y en una reciente investigación impulsada por la asociación Alzheimer Europe, se estima que 7.3 millones de personas padecen algún tipo de demencia. A pesar de que la ocurrencia de AD no es un suceso normal en la población mayor, el riesgo de desarrollar la enfermedad se incrementa en esa etapa.Postprint (published version

Clasificadores basados en máquinas de soporte vertical para el diagnóstico y predicción de la enfermedad de Alzheimer

En este trabajo, se estableci&oacute; una metodolog&iacute;a de soporte al diagnostico de AD, principalmente en sus etapas MCI ocasionado por AD y demencia ocasionada por AD. Para este fin fueron obtenidos casos cl&iacute;nicos de dos proyectos de investigaci&oacute;n en demencia del tipo AD de reconocida trayectoria: las bases de datos Alzheimer&#39;s Disease Neuroimaging Initiative (ADNI) (www.loni.ucla.edu/ADNI) y la base de datos The Open Access Series of Imaging Studies (OASIS) (http://www.oasis-brains.org/). Asimismo, fueron establecidas dos tareas principales: la selecci&oacute;n de variables predictoras de AD y la construcci&oacute;n de modelos de clasificacion basados en m&aacute;quinas de soporte vectorial (SVM), entrenados a partir de las variables seleccionadas. Las variables predictoras seleccionadas estuvieron conformadas por biomarcadores morfom&eacute;tricos y caracter&iacute;sticas socio-demogr&aacute;ficas y neuropsicol&oacute;gicas. Estas variables deberan ser &uacute;tiles para la discriminaci&oacute;n de casos cl&iacute;nicos en tres estados: (1) Estado normal (generalmente personas mayores sanas); (2) Estado MCI ocasionado por AD; y (3) Etapa de demencia ocasionada por AD. Por otro lado, los modelos SVM estar&aacute;n enfocados a dos tareas principales: (1) Diagn&oacute;stico de AD mediante la discriminaci&oacute;n entre sujetos sanos y sujetos con AD; y (2) Predicci&oacute;n de AD, orientada a la discriminaci&oacute;n de sujetos MCI con riesgo de convertirse a AD y sujetos MCI sin riesgo de conversi&oacute;n. Asimismo, estos modelos deber&aacute;n garantizar resultados aceptables, respecto a la sensibilidad y especificidad de las tareas de clasificaci&oacute;n. Los resultados obtenidos en esta investigaci&oacute;n son prometedores. Por un lado, el subconjunto de variables seleccionadas como relevantes para el diagn&oacute;stico de AD, tienen correlaci&oacute;n con los resultados de investigaciones previas. Asimismo, en la etapa de testeo, los resultados demostraron que los modelos SVM son de gran utilidad para el soporte diagn&oacute;stico cl&iacute;nico de esta enfermedad, siendo capaces de discriminar sujetos con AD de sujetos sanos (diagn&oacute;stico) con una exactitud mayor al 99% y distinguir a los sujetos MCI con riesgo de conversi&oacute;n a AD de los sujetos MCI sin riego de conversi&oacute;n (predicci&oacute;n) con una exactitud superior al 94%

Quantifying structural changes in the ageing brain from magnetic resonance imaging

Understanding the ageing process is of increasing importance to an ageing society and one aspect of this is investigating what role the brain has in this process. Cognitive ability declines as we age and it is one of the most distressing aspects of getting older. Brain tissue deterioration is a significant contributor to lower cognitive ability in late life but the underlying biological mechanisms in the brain are not yet fully understood. One reason for this is the difficulty in obtaining accurate measures of potential ageing-related brain biomarkers. The chapters in this thesis explore the difficulties of quantifying brain changes in the ageing brain from Magnetic Resonance Imaging (MRI), and how the changes identified are related to cognition in later life. The data was acquired as part of the second wave of the longitudinal Lothian Birth Cohort 1936 study in which 866 people aged 73 years, returned for cognitive and medical assessment. At this stage of the study 702 underwent MR imaging resulting in 627 complete datasets across all testing. The entire data, a randomly chosen subset of 150 and 416 freely available data were used to investigate global and regional measurement methods in older brains and how the resultant measurements related to cognitive performance. Furthermore the presence of early life cognitive data in the form of a general intelligence test sat at age 11, served as an indicator of cognitive ability prior to the potential influence of the ageing process. The chapters concerning global measures at first establish, that a measure of intracranial volume (ICV) serves as both a way of correcting for individual differences in brain size between participants and as a proxy premorbid measure of brain size. The analysis, utilising freely available cross-sectional MRI data (http://www.oasis-brains.org) revealed that ICV differed very little between 18-28 year olds and 84-96 year olds where as total brain tissue volume (TBV) differed by 14.1% between the two groups, which was more than twice the standard deviation across the entire age range (18-96 years). Second a validated, reliable method for measuring ICV was investigated using 150 people randomly chosen from the LBC1936 study. Automated and semi-automated methods were validated against reference measurements the results of which showed that common ageing features make automated and semi-automated methods that do not have an additional manual editing step, ineffective at producing accurate ICV measurements. This analysis also highlighted the need to employ additional spatial overlap assessment to volumetric comparison of measurement methods to reduce the effect of false-positives and false-negatives skewing apparent discrepancies between methods. Using the information gained here ICV and TBV from the entire LBC1936 cohort were analysed in a structural equation model, alongside cognitive ability measures at both age 11 and age 73. We found that TBV was a stronger predictor of later life cognitive ability, after accounting for early life ability, but that a modest association remained between ICV and late life cognition. This suggests that early life factors pay a role in how well we age, though the relationship is complex. The regional measures chapters look at two brain regions commonly associated with ageing, the hippocampus and the frontal lobes. Measuring either of these brain regions in large samples of healthy older adults is challenging for many reasons. The hippocampus is small and as with all brain regions shows greater variation in older age, this makes employing automated methods that have the advantage of being fast and reproducible difficult. Following the results of our systematic review of automated methods for measuring the hippocampus, the two most commonly used and available automated methods were validated against reference standard measurements. The results indicated that although automated methods present an attractive alternative to laborious manual measurements they still require manual editing to produce accurate measurements in older adults. The modified strategy employed across the LBC1936 was to use an automated method and then manually edit the output; these segmentations were used to investigate the potential of multimodal image analysis in clarifying associations between the hippocampus and cognitive ability in old age. The analysis focused on associations between longitudinal relaxation time (T1), magnetization transfer ratio (MTR), fractional anisotropy (FA) and mean diffusivity (MD) in the hippocampus and general factors of fluid intelligence, cognitive processing speed and memory. The findings show that multi-modal MRI assessments were more sensitive than volumetric measurements at detecting associations with cognitive measures. The difficulty with producing a relevant frontal lobe measure was made apparent when the result of a large systematic review looking at the manual protocols used revealed 19 methods and 15 different landmarks had been employed. This resulted in an analysis that took the 5 most common boundaries reported and applied them to 10 randomly selected participants from the LBC1936. The results showed significant differences between the resultant volumes, with the smallest measurement when using the genu as the posterior marker representing only 35% of the measurement acquired using the central sulcus. The results from the studies presented in this thesis strongly highlight the need to develop age specific methods when using brain MRI to study ageing. Furthermore the implications of using unstandardised protocols, making assumptions about a methods performance based on validation in younger samples and the need to account for early life factors in this area of research have been made clearer. Studies building on these findings will be beneficial in elucidating the role of the brain in ageing