Abstract
Performing an accurate segmentation of the hippocampus from brain magnetic resonance images is a crucial task in neuroimaging research, since its structural integrity is strongly related to several neurodegenerative disorders, including Alzheimer’s disease (AD). Some automatic segmentation tools are already being used, but, in recent years, new deep learning (DL)-based methods have been proven to be much more accurate in various medical image segmentation tasks. In this work, we propose a DL-based hippocampus segmentation framework that embeds statistical shape of the hippocampus as context information into the deep neural network (DNN). The inclusion of shape information is achieved with three main steps: (1) a U-Net-based segmentation, (2) a shape model estimation, and (3) a second U-Net-based segmentation which uses both the original input data and the fitted shape model. The trained DL architectures were tested on image data of three diagnostic groups [AD patients, subjects with mild cognitive impairment (MCI) and controls] from two cohorts (ADNI and AddNeuroMed). Both intra-cohort validation and cross-cohort validation were performed and compared with the conventional U-net architecture and some variations with other types of context information (i.e., autocontext and tissue-class context). Our results suggest that adding shape information can improve the segmentation accuracy in cross-cohort validation, i.e., when DNNs are trained on one cohort and applied to another. However, no significant benefit is observed in intra-cohort validation, i.e., training and testing DNNs on images from the same cohort. Moreover, compared to other types of context information, the use of shape context was shown to be the most successful in increasing the accuracy, while keeping the computational time in the order of a few minutes.
Highlights
Alzheimer’s disease (AD) is a chronic progressive neurodegenerative disorder that constitutes approximately 60–70% of all dementia cases (Burns and Iliffe, 2009)
We investigate whether the integration of shape information can improve the accuracy in the context of hippocampal segmentation
The primary goal of Alzheimer’s Disease Neuroimaging Initiative (ADNI) has been to test whether serial magnetic resonance imaging (MRI), positron emission tomography (PET), other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of mild cognitive impairment (MCI) and early AD
Summary
Alzheimer’s disease (AD) is a chronic progressive neurodegenerative disorder that constitutes approximately 60–70% of all dementia cases (Burns and Iliffe, 2009). It is important to study imaging biomarkers that could allow early identification of subjects at risk of developing the disorder, as well as quantitatively reflect the disease’s level of progression. Such biomarkers should be able to distinguish AD both from the healthy state and from mild cognitive impairment (MCI). MCI subjects constitute a relevant study group for the early identification of the disease, since several MCI cases, especially when presenting memory dysfunction, have a high probability of later evolving toward AD (Vinters, 2015)
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