0000000000505771
AUTHOR
Kaikai Shen
IC‐P‐024: Localization of hippocampal atrophy in Alzheimer's disease
The hippocampus presents the highest rate of atrophy in the early stage of Alzheimer's disease (AD), with more pronounced neuron loss reported in CA1 and subiculum. The aim of this study is to increase the discrimination power of hippocampal shape analysis between AD and normal controls (NC) by focusing on the subregions with atrophy associated with AD and describing the localized shape changes using statistical shape models (SSMs).
Segmentation automatique et analyse de forme d'hippocampes humains dans l'étude de la maladie d'Alzheimer
The aim of this thesis is to investigate the shape change in hippocampus due to the atrophy in Alzheimer’s disease (AD). To this end, specific algorithms and methodologies were developed to segment the hippocampus from structural magnetic resonance (MR) images and model variations in its shape. We use a multi-atlas based segmentation propagation approach for the segmentation of hippocampus which has been shown to obtain accurate parcellation of brain structures. We developed a supervised method to build a population specific atlas database, by propagating the parcellations from a smaller generic atlas database. Well segmented images are inspected and added to the set of atlases, such that t…
Atlas selection strategy using least angle regression in multi-atlas segmentation propagation
International audience; In multi-atlas based segmentation propagation, segmentations from multiple atlases are propagated to the target image and combined to produce the segmentation result. Local weighted voting (LWV) method is a classifier fusion method which combines the propagated atlases weighted by local image similarity. We demonstrate that the segmentation accuracy using LWV improves as the number of atlases increases. Under this context, we show that introducing diversity in addition to image similarity by using least-angle regression (LAR) criteria is a more efficient way to rank and select atlases. The accuracy of multi-atlas segmentation converges faster when the atlases are sel…
Detecting global and local hippocampal shape changes in Alzheimer's disease using statistical shape models.
Item does not contain fulltext The hippocampus is affected at an early stage in the development of Alzheimer's disease (AD). With the use of structural magnetic resonance (MR) imaging, we can investigate the effect of AD on the morphology of the hippocampus. The hippocampal shape variations among a population can be usually described using statistical shape models (SSMs). Conventional SSMs model the modes of variations among the population via principal component analysis (PCA). Although these modes are representative of variations within the training data, they are not necessarily discriminative on labeled data or relevant to the differences between the subpopulations. We use the shape des…
LOCALIZATION OF HIPPOCAMPAL ATROPHY IN ALZHEIMER'S DISEASE
International audience; The hippocampus presents the highest rate of atrophy in the early stage of Alzheimer's disease (AD), with more pronounced neuron loss reported in CA1 and subiculum. The aim of this study is to increase the discrimination power of hippocampal shape analysis between AD and normal controls (NC) by focusing on the subregions with atrophy associated with AD and describing the localized shape changes using statistical shape models (SSMs).
Increasing power to predict mild cognitive impairment conversion to Alzheimer's disease using hippocampal atrophy rate and statistical shape models
Identifying mild cognitive impairment (MCI) subjects who will convert to clinical Alzheimer's disease (AD) is important for therapeutic decisions, patient counselling and clinical trials. Hippocampal volume and rate of atrophy predict clinical decline at the MCI stage and progression to AD. In this paper, we create p-maps from the differences in the shape of the hippocampus between 60 normal controls and 60 AD subjects using statistical shape models, and generate different regions of interest (ROI) by thresholding the p-maps at different significance levels. We demonstrate increased statistical power to classify 86 MCI converters and 128 MCI stable subjects using the hippocampal atrophy rat…