Integrating longitudinal information in hippocampal volume measurements for the early detection of Alzheimer's disease

Background: Structural MRI measures for monitoring Alzheimer's Disease (AD) progression are becoming instrumental in the clinical practice, and more so in the context of longitudinal studies. This investigation addresses the impact of four image analysis approaches on the longitudinal performance of the hippocampal volume. Methods: We present a hippocampal segmentation algorithm and validate it on a gold-standard manual tracing database. We segmented 460 subjects from ADNI, each subject having been scanned twice at baseline, 12-month and 24month follow-up scan (1.5T, T1 MRI). We used the bilateral hippocampal volume v and its variation, measured as the annualized volume change Λ=δv/year(mm3/y). Four processing approaches with different complexity are compared to maximize the longitudinal information, and they are tested for cohort discrimination ability. Reference cohorts are Controls vs. Alzheimer's Disease (CTRL/AD) and CTRL vs. Mild Cognitive Impairment who subsequently progressed to AD dementia (CTRL/MCI-co). We discuss the conditions on v and the added value of Λ in discriminating subjects. Results: The age-corrected bilateral annualized atrophy rate (%/year) were: -. 1.6 (0.6) for CTRL, -. 2.2 (1.0) for MCI-. nc, -. 3.2 (1.2) for MCI-. co and -. 4.0 (1.5) for AD. Combined (. v, Λ) discrimination ability gave an Area under the ROC curve (. auc). =. 0.93 for CTRL vs AD and auc=. 0.88 for CTRL vs MCI-. co. Conclusions: Longitudinal volume measurements can provide meaningful clinical insight and added value with respect to the baseline provided the analysis procedure embeds the longitudinal information

Magnetic resonance imaging In Alzheimer’s disease, mild cognitive impairment and normal aging : Multi-template tensor-based morphometry and visual rating

Alzheimer's disease (AD) is the most common neurodegenerative disease preceded by a stage of mild cognitive impairment (MCI). The structural brain changes in AD can be detected more than 20 years before symptoms appear. If we are to reveal early brain changes in AD process, it is important to develop new diagnostic methods. Magnetic resonance imaging (MRI) is an imaging technique used in the diagnosis and monitoring of neurodegenerative diseases. Magnetic resonance imaging can detect the typical signs of brain atrophy of degenerative diseases, but similar changes can also be seen in normal aging. Visual rating methods (VRM) have been developed for visual evaluation of atrophy in dementia. A computer-based tensor-based morphometry (TBM) analysis is capable of assessing the brain volume changes typically encountered in AD. This study compared the VRM and TBM analysis in MCI and AD subjects by cross-sectional and longitudinal examination. The working hypothesis was that TBM analysis would be better than the visual methods in detecting atrophy in the brain. TBM was also used to analyze volume changes in the deep gray matter (DGM). Possible associations between TBM changes and neuropsychological tests performances were examined. This working hypothesis was that the structural DGM changes would be associated with impairments in cognitive functions. In the cross-sectional study, TBM distinguished the MCI from controls more sensitively than VRM, but the methods were equally effective in differentiating AD from MCI and controls. In the longitudinal study, both methods were equally good in the evaluation of atrophy in MCI, if the groups were sufficiently large and the disease progressed to AD. Volume changes were found in DGM structures, and the atrophy of DGM structures was related to cognitive impairment in AD. Based on these results, a TBM analysis is more sensitive in detecting brain changes in early AD as compared to VRM. In addition, the study produced information about the involvement of the deep gray matter in cognitive impairment in AD.Magneettikuvaus Alzheimerin taudissa, lievässä muistihäiriössä ja normaalissa ikääntymisessä: Tensoripohjainen muotoanalyysi ja visuaalinen arviointimenetelmä Alzheimerin tauti (AT) on yleisin dementoiva sairaus, jota edeltää yleensä lievä muistitoimintojen heikentyminen. AT:n aivomuutoksia voidaan todeta yli 20 vuotta ennen sairastumista. Jotta vielä varhaisempia AT:n aivomuutoksia voidaan todeta, on tärkeää kehittää uusia diagnostisia menetelmiä. Magneettikuvausta (MK) käytetään rappeuttavien aivosairauksien diagnostiikassa ja seurannassa. MK:lla voidaan havaita aivorappeumasairauksille tyypillistä kutistumista, mutta samanlaisia muutoksia voi esiintyä myös normaalissa ikääntymisessä. Aivorappeuman arviointiin on kehitetty silmämääräisiä arviointimenetelmiä. Tietokoneperusteinen tensoripohjainen muotoanalyysi (TPM) laskee esimerkiksi AT:lle tyypillisiä aivojen tilavuusmuutoksia. Tämä tutkimus vertaili silmämääräisiä arvioitimenetelmiä ja TPM:ä lievässä muistitoimintojen heikentymisessä ja AT:ssa poikittais- ja pitkittäistutkimuksella. TPM:n oletettiin olevan silmämääräisiä menetelmiä parempi tunnistamaan aivojen kutistumismuutoksia. Lisäksi TPM:llä tutkittiin AT:iin liittyviä aivojen syvän harmaan aiheen muutoksia, joita verrattiin neuropsykologisten testien tuloksiin. Syvän harmaan aineen kutistumisen oletettiin olevan yhteydessä tietojenkäsittelyn heikentymiseen. Tulosten perustella TPM tunnisti AT:iin liittyviä aivomuutoksia silmämääräistä menetelmää paremmin jo lievän muistitoimintojen heikentymisen vaiheessa. AT:iin liittyviä aivomuutoksia löytyi myös aivojen syvästä harmaasta aineesta ja ne olivat osittain yhteydessä neuropsykologisten testien tuloksiin. Tutkimuksen perusteella TPM voi parantaa AT:n varhaisdiagnostiikkaa verrattuna silmämääräisiin arviointimenetelmiin. Tutkimus antoi myös tietoa aivojen syvän harmaan aineen osallisuudesta ihmisen tietojenkäsittelyyn

Machine learning for efficient recognition of anatomical structures and abnormalities in biomedical images

Three studies have been carried out to investigate new approaches to efficient image segmentation and anomaly detection. The first study investigates the use of deep learning in patch based segmentation. Current approaches to patch based segmentation use low level features such as the sum of squared differences between patches. We argue that better segmentation can be achieved by harnessing the power of deep neural networks. Currently these networks make extensive use of convolutional layers. However, we argue that in the context of patch based segmentation, convolutional layers have little advantage over the canonical artificial neural network architecture. This is because a patch is small, and does not need decomposition and thus will not benefit from convolution. Instead, we make use of the canonical architecture in which neurons only compute dot products, but also incorporate modern techniques of deep learning. The resulting classifier is much faster and less memory-hungry than convolution based networks. In a test application to the segmentation of hippocampus in human brain MR images, we significantly outperformed prior art with a median Dice score up to 90.98% at a near real-time speed (<1s). The second study is an investigation into mouse phenotyping, and develops a high-throughput framework to detect morphological abnormality in mouse embryo micro-CT images. Existing work in this line is centred on, either the detection of phenotype-specific features or comparative analytics. The former approach lacks generality and the latter can often fail, for example, when the abnormality is not associated with severe volume variation. Both these approaches often require image segmentation as a pre-requisite, which is very challenging when applied to embryo phenotyping. A new approach to this problem in which non-rigid registration is combined with robust principal component analysis (RPCA), is proposed. The new framework is able to efficiently perform abnormality detection in a batch of images. It is sensitive to both volumetric and non-volumetric variations, and does not require image segmentation. In a validation study, it successfully distinguished the abnormal VSD and polydactyly phenotypes from the normal, respectively, at 85.19% and 88.89% specificities, with 100% sensitivity in both cases. The third study investigates the RPCA technique in more depth. RPCA is an extension of PCA that tolerates certain levels of data distortion during feature extraction, and is able to decompose images into regular and singular components. It has previously been applied to many computer vision problems (e.g. video surveillance), attaining excellent performance. However these applications commonly rest on a critical condition: in the majority of images being processed, there is a background with very little variation. By contrast in biomedical imaging there is significant natural variation across different images, resulting from inter-subject variability and physiological movements. Non-rigid registration can go some way towards reducing this variance, but cannot eliminate it entirely. To address this problem we propose a modified framework (RPCA-P) that is able to incorporate natural variation priors and adjust outlier tolerance locally, so that voxels associated with structures of higher variability are compensated with a higher tolerance in regularity estimation. An experimental study was applied to the same mouse embryo micro-CT data, and notably improved the detection specificity to 94.12% for the VSD and 90.97% for the polydactyly, while maintaining the sensitivity at 100%.Open Acces

Detection of Alzheimer’s Disease using CNN Architectures

Alzheimer’s disease is a neurological condition that causes some structural alterations in the brain. In this paper we have given an overview of all the available good CNN models used in medical imaging for image classification purpose such asAlexNet, GoogleNet, ResNet 18, ResNet 50, SqueezeNet and DenseNet. Using these CNN models, we have been able to classify three different stages of Alzheimer's disease – Cognitively Normal (NC), Mild Cognitive Impairment (MCI) and Alzheimer’s Disease(AD). The dataset is derived from ADNI and has been preprocessed before applying various CNN models. The experimental results demonstrate that all models performed well and the best accuracy has been acquired by the GoogleNet of 96.81%

Development of hippocampus MRI image segmentation algorithm for progression detection of alzheimer’s disease (AD)

Alzheimer's disease is becoming one of the most serious ailments that people face. Alzheimer's disease primarily affects those over the age of 65. is defined by the death of brain cells, which results in memory loss. as well as a lack of judgment, linguistic abilities, and decision-making capability Furthermore, no research has been conducted on developing a monitoring system for Alzheimer's disease that can continuously monitor Alzheimer's patients to identify any signs of development. Current research focuses mostly on early diagnosis and does not include disease monitoring. Monitoring is critical since it allows doctors to assess the disease development of Alzheimer's patients quantitatively. This study indicates developing an algorithm for detecting and progressing through the hippocampus of patients with Alzheimer's disease in MRI images. The active contour method (Chan-Vese) was utilized to extract the ROI parameters (hippocampus). The active contours algorithm deforms an item's initial border in an image to latch onto typical features inside the region of interest given an approximation of the object's perimeter. This is constantly stretched until it reaches the ROI's boundary. The interactive area selection approach is used to allow the user to determine the ROI depending on their needs. The algorithm will be applied once the ROI has been specified. The algorithm will be able to identify the parameters, such as the number of pixels, area pixels, and mean value, by extracting the hippocampal shape. The extraction of parameters will allow us to determine the extent of the patient's Alzheimer's progression. As a result, the study was successful in developing a semi-automated and robust model based on the Chan-Vese segmentation methodology, where it could observe the shrinking of the patient brain by the progression method using the total pixels of the hippocampus and its area by getting decreased at the second visit, one of the results showed at the first visit the total number of the pixels was 707 then at the second visit it shows 650 so the progression percentage 9%, and the proposed method produced promising segmentation results. In addition, a graphical user interface (GUI) was created to identify the progression percentage. As a future plan, this project can use machine learning to train the data for auto-detection for the hippocampus which will be significantly robust and more effective

DEEP-AD: The deep learning model for diagnostic classification and prognostic prediction of alzheimer's disease

In terms of context, the aim of this dissertation is to aid neuroradiologists in their clinical judgment regarding the early detection of AD by using DL. To that aim, the system design research methodology is suggested in this dissertation for achieving three goals. The first goal is to investigate the DL models that have performed well at identifying patterns associated with AD, as well as the accuracy so far attained, limitations, and gaps. A systematic review of the literature (SLR) revealed a shortage of empirical studies on the early identification of AD through DL. In this regard, thirteen empirical studies were identified and examined. We concluded that three-dimensional (3D) DL models have been generated far less often and that their performance is also inadequate to qualify them for clinical trials. The second goal is to provide the neuroradiologist with the computer-interpretable information they need to analyze neuroimaging biomarkers. Given this context, the next step in this dissertation is to find the optimum DL model to analyze neuroimaging biomarkers. It has been achieved in two steps. In the first step, eight state-of-the-art DL models have been implemented by training from scratch using end-to-end learning (E2EL) for two binary classification tasks (AD vs. CN and AD vs. stable MCI) and compared by utilizing MRI scans from the publicly accessible datasets of neuroimaging biomarkers. Comparative analysis is carried out by utilizing efficiency-effects graphs, comprehensive indicators, and ranking mechanisms. For the training of the AD vs. sMCI task, the EfficientNet-B0 model gets the highest value for the comprehensive indicator and has the fewest parameters. DenseNet264 performed better than the others in terms of evaluation matrices, but since it has the most parameters, it costs more to train. For the AD vs. CN task by DenseNet264, we achieved 100% accuracy for training and 99.56% accuracy for testing. However, the classification accuracy was still only 82.5% for the AD vs. sMCI task. In the second step, fusion of transfer learning (TL) with E2EL is applied to train the EfficientNet-B0 for the AD vs. sMCI task, which achieved 95.29% accuracy for training and 93.10% accuracy for testing. Additionally, we have also implemented EfficientNet-B0 for the multiclass AD vs. CN vs. sMCI classification task with E2EL to be used in ensemble of models and achieved 85.66% training accuracy and 87.38% testing accuracy. To evaluate the model&#8217;s robustness, neuroradiologists must validate the implemented model. As a result, the third goal of this dissertation is to create a tool that neuroradiologists may use at their convenience. To achieve this objective, this dissertation proposes a web-based application (DEEP-AD) that has been created by making an ensemble of Efficient-Net B0 and DenseNet 264 (based on the contribution of goal 2). The accuracy of a DEEP-AD prototype has undergone repeated evaluation and improvement. First, we validated 41 subjects of Spanish MRI datasets (acquired from HT Medica, Madrid, Spain), achieving an accuracy of 82.90%, which was later verified by neuroradiologists. The results of these evaluation studies showed the accomplishment of such goals and relevant directions for future research in applied DL for the early detection of AD in clinical settings.En términos de contexto, el objetivo de esta tesis es ayudar a los neurorradiólogos en su juicio clínico sobre la detección precoz de la AD mediante el uso de DL. Para ello, en esta tesis se propone la metodología de investigación de diseño de sistemas para lograr tres objetivos. El segundo objetivo es proporcionar al neurorradiólogo la información interpretable por ordenador que necesita para analizar los biomarcadores de neuroimagen. Dado este contexto, el siguiente paso en esta tesis es encontrar el modelo DL óptimo para analizar biomarcadores de neuroimagen. Esto se ha logrado en dos pasos. En el primer paso, se han implementado ocho modelos DL de última generación mediante entrenamiento desde cero utilizando aprendizaje de extremo a extremo (E2EL) para dos tareas de clasificación binarias (AD vs. CN y AD vs. MCI estable) y se han comparado utilizando escaneos MRI de los conjuntos de datos de biomarcadores de neuroimagen de acceso público. El análisis comparativo se lleva a cabo utilizando gráficos de efecto-eficacia, indicadores exhaustivos y mecanismos de clasificación. Para el entrenamiento de la tarea AD vs. sMCI, el modelo EfficientNet-B0 obtiene el valor más alto para el indicador exhaustivo y tiene el menor número de parámetros. DenseNet264 obtuvo mejores resultados que los demás en términos de matrices de evaluación, pero al ser el que tiene más parámetros, su entrenamiento es más costoso. Para la tarea AD vs. CN de DenseNet264, conseguimos una accuracy del 100% en el entrenamiento y del 99,56% en las pruebas. Sin embargo, la accuracy de la clasificación fue sólo del 82,5% para la tarea AD vs. sMCI. En el segundo paso, se aplica la fusión del aprendizaje por transferencia (TL) con E2EL para entrenar la EfficientNet-B0 para la tarea AD vs. sMCI, que alcanzó una accuracy del 95,29% en el entrenamiento y del 93,10% en las pruebas. Además, también hemos implementado EfficientNet-B0 para la tarea de clasificación multiclase AD vs. CN vs. sMCI con E2EL para su uso en conjuntos de modelos y hemos obtenido una accuracy de entrenamiento del 85,66% y una precisión de prueba del 87,38%. Para evaluar la solidez del modelo, los neurorradiólogos deben validar el modelo implementado. Como resultado, el tercer objetivo de esta disertación es crear una herramienta que los neurorradiólogos puedan utilizar a su conveniencia. Para lograr este objetivo, esta disertación propone una aplicación basada en web (DEEP-AD) que ha sido creada haciendo un ensemble de Efficient-Net B0 y DenseNet 264 (basado en la contribución del objetivo 2). La accuracy del prototipo DEEP-AD ha sido sometida a repetidas evaluaciones y mejoras. En primer lugar, validamos 41 sujetos de conjuntos de datos de MRI españoles (adquiridos de HT Medica, Madrid, España), logrando una accuracy del 82,90%, que posteriormente fue verificada por neurorradiólogos. Los resultados de estos estudios de evaluación mostraron el cumplimiento de dichos objetivos y las direcciones relevantes para futuras investigaciones en DL, aplicada en la detección precoz de la AD en entornos clínicos.Escuela de DoctoradoDoctorado en Tecnologías de la Información y las Telecomunicacione

Heritability and reliability of automatically segmented human hippocampal formation subregions

The human hippocampal formation can be divided into a set of cytoarchitecturally and functionally distinct subregions, involved in different aspects of memory formation. Neuroanatomical disruptions within these subregions are associated with several debilitating brain disorders including Alzheimer's disease, major depression, schizophrenia, and bipolar disorder. Multi-center brain imaging consortia, such as the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) consortium, are interested in studying disease effects on these subregions, and in the genetic factors that affect them. For large-scale studies, automated extraction and subsequent genomic association studies of these hippocampal subregion measures may provide additional insight. Here, we evaluated the test-retest reliability and transplatform reliability (1.5 T versus 3 T) of the subregion segmentation module in the FreeSurfer software package using three independent cohorts of healthy adults, one young (Queensland Twins Imaging Study, N=39), another elderly (Alzheimer's Disease Neuroimaging Initiative, ADNI-2, N=163) and another mixed cohort of healthy and depressed participants (Max Planck Institute, MPIP, N=598). We also investigated agreement between the most recent version of this algorithm (v6.0) and an older version (v5.3), again using the ADNI-2 and MPIP cohorts in addition to a sample from the Netherlands Study for Depression and Anxiety (NESDA) (N=221). Finally, we estimated the heritability (h(2)) of the segmented subregion volumes using the full sample of young, healthy QTIM twins (N=728). Test-retest reliability was high for all twelve subregions in the 3 T ADNI-2 sample (intraclass correlation coefficient (ICC)=0.70-0.97) and moderate-to-high in the 4 TQTIM sample (ICC=0.5-0.89). Transplatform reliability was strong for eleven of the twelve subregions (ICC=0.66-0.96); however, the hippocampal fissure was not consistently reconstructed across 1.5 T and 3 T field strengths (ICC=0.47-0.57). Between-version agreement was moderate for the hippocampal tail, subiculum and presubiculum (ICC=0.78-0.84; Dice Similarity Coefficient (DSC)=0.55-0.70), and poor for all other subregions (ICC=0.34-0.81; DSC=0.28-0.51). All hippocampal subregion volumes were highly heritable (h(2)=0.67-0.91). Our findings indicate that eleven of the twelve human hippocampal subregions segmented using FreeSurfer version 6.0 may serve as reliable and informative quantitative phenotypes for future multi-site imaging genetics initiatives such as those of the ENIGMA consortium. (C) 2016 The Authors. Published by Elsevier Inc

Scoring by nonlocal image patch estimator for early detection of 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).Detection of Alzheimer's disease (AD) at the first stages of the pathology is an important task to accelerate the development of new therapies and improve treatment. Compared to AD detection, the prediction of AD using structural MRI at the mild cognitive impairment (MCI) or pre-MCI stage is more complex because the associated anatomical changes are more subtle. In this study, we analyzed the capability of a recently proposed method, SNIPE (Scoring by Nonlocal Image Patch Estimator), to predict AD by analyzing entorhinal cortex (EC) and hippocampus (HC) scoring over the entire ADNI database (834 scans). Detection (AD vs. CN) and prediction (progressive - pMCI vs. stable - sMCI) efficiency of SNIPE were studied using volumetric and grading biomarkers. First, our results indicate that grading-based biomarkers are more relevant for prediction than volume-based biomarkers. Second, we show that HC-based biomarkers are more important than EC-based biomarkers for prediction. Third, we demonstrate that the results obtained by SNIPE are similar to or better than results obtained in an independent study using HC volume, cortical thickness, and tensor-based morphometry, individually and in combination. Fourth, a comparison of new patch-based methods shows that the nonlocal redundancy strategy involved in SNIPE obtained similar results to a new local sparse-based approach. Finally, we present the first results of patch-based morphometry to illustrate the progression of the pathology.We wish to thank Dr. Robin Wolz for providing the list of ADNI subjects used in his study, which allowed us to perform the presented method comparison. We also want to thank the Canadian Institutes of Health Research (MOP-111169) and the Fonds de la recherche en sante du Quebec. Data collection and sharing for this project were funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904). The ADNI is funded by the National Institute on Aging and 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 NV, Johnson & Johnson, Eli Lilly and Co., Medpace, Inc., Merck and Co., Inc., Novartis AG, Pfizer Inc., F. Hoffmann-La Roche, Schering-Plough, Synarc Inc., as well as nonprofit 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 the 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 was 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 and also by the Spanish grant TIN2011-26727 from the Ministerio de Ciencia e Innovacion.Coupé, P.; Eskildsen, SF.; Manjón Herrera, JV.; Fonov, VS.; Pruessner, JC.; Allard, M.; Collins, LD. (2012). Scoring by nonlocal image patch estimator for early detection of Alzheimer's disease. NeuroImage: Clinical. 1(1):141-152. https://doi.org/10.1016/j.nicl.2012.10.002S1411521