Hippocampus In Magnetic Resonance Imaging Research Articles

Alzheimer Hastalığını Hafif Bilişsel Bozukluktan Ayırmak İçin Basit Bir MRI-Tabanlı Görsel Kılavuz

Objective: To distinguish between mild cognitive impairment (MCI) and Alzheimer’s disease (AD) by visual assessment of the length of the hippocampus in magnetic resonance imaging (MRI).
 Method: Consecutive patients diagnosed with MCI and AD were searched on the system retrospectively. MRI was rated for hippocampal atrophy defining with and without loss of hippocampal length. Patients with loss of hippocampal height were classified as having AD by the clinical investigator, and the diagnosis of the patients was checked on the system.
 Results: A total of 56 memory clinic patients with AD (n=14) and MCI (n=42) were included in the study. AD patients had significantly more hippocampal atrophy than MCI patients (𝜒2=6.222, df=0.13, 𝑝=0.013).
 Conclusion: There is a complex issue in the differential diagnosis between MCI and AD. A simple glace to the MRI may give a brief opinion to the physician in the clinic routine.

Deep integration model: A robust autonomous segmentation technique for hippocampus in MRI images of human head

Segmentation of hippocampus in Magnetic Resonance Images (MRI) is a vital process in neuro imaging studies towards analyzing and extracting the structure of hippocampus. In existing, many automatic segmentation algorithms such as region based segmentation; boundary based Segmentation has been applied to segment the Hippocampus (HC) as it produces the higher accuracy and less mean squared error. Despite several advantages, the model is time consuming and it generates less accuracy on the volumetric changes of the diverse dataset. However, due to the existence of imaging artifacts, changes in anatomy, contrast variability, and poor registration, most of these mechanisms do not produce suitable results. In order to resolve those issues, a novel framework named as Deep Integration Model has been proposed using Sparse Principle Component Analysis, Affinity Propagation and Linear Convolution Neural Network to segment the MRI Images. The model segments the Hippocampus and its subfields in the MRI images. Affinity Propagation identifies the modalities and pathologically altered tissues and represented it as features set. Sparse Principle Component Analysis extracts the sparse information and its interrelationship on the feature set which is represented as max pooled data.

The impact of transfer learning on 3D deep learning convolutional neural network segmentation of the hippocampus in mild cognitive impairment and Alzheimer disease subjects.

Research on segmentation of the hippocampus in magnetic resonance images through deep learning convolutional neural networks (CNNs) shows promising results, suggesting that these methods can identify small structural abnormalities of the hippocampus, which are among the earliest and most frequent brain changes associated with Alzheimer disease (AD). However, CNNs typically achieve the highest accuracy on datasets acquired from the same domain as the training dataset. Transfer learning allows domain adaptation through further training on a limited dataset. In this study, we applied transfer learning on a network called spatial warping network segmentation (SWANS), developed and trained in a previous study. We used MR images of patients with clinical diagnoses of mild cognitive impairment (MCI) and AD, segmented by two different raters. By using transfer learning techniques, we developed four new models, using different training methods. Testing was performed using 26% of the original dataset, which was excluded from training as a hold‐out test set. In addition, 10% of the overall training dataset was used as a hold‐out validation set. Results showed that all the new models achieved better hippocampal segmentation quality than the baseline SWANS model (p s < .001), with high similarity to the manual segmentations (mean dice [best model] = 0.878 ± 0.003). The best model was chosen based on visual assessment and volume percentage error (VPE). The increased precision in estimating hippocampal volumes allows the detection of small hippocampal abnormalities already present in the MCI phase (SD = [3.9 ± 0.6]%), which may be crucial for early diagnosis.

A Combined Deep-Learning and Lattice Boltzmann Model for Segmentation of the Hippocampus in MRI.

Segmentation of the hippocampus (HC) in magnetic resonance imaging (MRI) is an essential step for diagnosis and monitoring of several clinical situations such as Alzheimer’s disease (AD), schizophrenia and epilepsy. Automatic segmentation of HC structures is challenging due to their small volume, complex shape, low contrast and discontinuous boundaries. The active contour model (ACM) with a statistical shape prior is robust. However, it is difficult to build a shape prior that is general enough to cover all possible shapes of the HC and that suffers the problems of complicated registration of the shape prior and the target object and of low efficiency. In this paper, we propose a semi-automatic model that combines a deep belief network (DBN) and the lattice Boltzmann (LB) method for the segmentation of HC. The training process of DBN consists of unsupervised bottom-up training and supervised training of a top restricted Boltzmann machine (RBM). Given an input image, the trained DBN is utilized to infer the patient-specific shape prior of the HC. The specific shape prior is not only used to determine the initial contour, but is also introduced into the LB model as part of the external force to refine the segmentation. We used a subset of OASIS-1 as the training set and the preliminary release of EADC-ADNI as the testing set. The segmentation results of our method have good correlation and consistency with the manual segmentation results.

Multi-target Interactive Neural Network for Automated Segmentation of the Hippocampus in Magnetic Resonance Imaging

The hippocampus has been recognized as an important biomarker for the diagnosis and assessment of neurological diseases. Convenient and accurate automated segmentation of the hippocampus facilitates the analysis of large-scale neuroimaging studies. This work describes a novel technique for hippocampus segmentation in magnetic resonance images, in which interactive neural network (Inter-Net) is based on 3D convolutional operations. Inter-Net achieves the interaction through two aspects: one is the compartments, which builds an exponential ensemble network that integrates numerous short networks together when forward propagation. The other is the pathways, which realizes inter-connection between feature extraction and restoration. In addition, a multi-target architecture is proposed by designing multiple objective functions in terms of evaluation index, information theory, and data distribution. The proposed architecture is validated in fivefold cross-validation on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, where the mean Dice similarity indices of 0.919 (± 0.023) and precision of 0.926 (± 0.032) for the hippocampus segmentation. The running time is approximately 42.1 s from reading the image to outputting the segmentation result in our computer configuration. We compare the experimental results of a variety of methods to prove the effectiveness of the Inter-Net and contrast integrated architectures with different objective functions to illustrate the robustness of the fusion. The proposed framework is general and can be easily extended to numerous tissue segmentation tasks while it is tailored for the hippocampus.

Contrast-enhanced Bias-corrected Distance-regularized Level Set Method Applied to Hippocampus Segmentation

Recently, the level set has become a popular method in many research fields. The main reason is that it can be modified into many variants. One such case is our proposed method. We describe a contrast-enhancement method to segment the hippocampal region from the background. However, the hippocampus region has quite similar intensities to the neighboring pixel intensities. In addition, to handle the inhomogeneous intensities of the hippocampus, we used a bias correction before hippocampal segmentation. Thus, we developed a contrast-enhanced bias-corrected distance-regularized level set (CBDLS) to segment the hippocampus in magnetic resonance imaging (MRI). It shows better performance than the distance-regularized level set evolution (DLS) and bias-corrected distance-regularized level set (BDLS) methods in 33 MRI images of one normal patient. Segmentation after contrast enhancement and bias correction can be done more accurately than segmentation while not using a bias-correction method and without contrast enhancement.

Combined use of biochemical and volumetric biomarkers to assess the risk of conversion of mild cognitive impairment to Alzheimer’s disease

The aim of our study was to evaluate the usefulness of several biomarkers in predicting the conversion of mild cognitive impairment (MCI) to Alzheimer's disease (AD): β-amyloid and tau proteins in cerebrospinal fluid and the volumetric evaluation of brain structures including the hippocampus in magnetic resonance imaging (MRI). MRI of the brain with the volumetric assessment of hippocampus, entorhinal cortex, posterior cingulate gyrus, parahippocampal gyrus, superior, medial and inferior temporal gyri was performed in 40 patients diagnosed with mild cognitive impairment. Each patient had a lumbar puncture to evaluate β-amyloid and tau protein (total and phosphorylated) levels in the cerebrospinal fluid. The observation period was 2 years. Amongst 40 patients with MCI, 9 (22.5%) converted to AD within 2 years of observation. Discriminant analysis was conducted and sensitivity for MCI conversion to AD on the basis of volumetric measurements was 88.9% and specificity 90.3%; on the basis of β-amyloid and total tau, sensitivity was 77.8% and specificity 83.9%. The combined use of the results of volumetric measurements with the results of proteins in the cerebrospinal fluid did not increase the sensitivity (88.9%) but increased specificity to 96.8% and the percentage of correct classification to 95%.

Automated methods for hippocampus segmentation: the evolution and a review of the state of the art.

The segmentation of the hippocampus in Magnetic Resonance Imaging (MRI) has been an important procedure to diagnose and monitor several clinical situations. The precise delineation of the borders of this brain structure makes it possible to obtain a measure of the volume and estimate its shape, which can be used to diagnose some diseases, such as Alzheimer's disease, schizophrenia and epilepsy. As the manual segmentation procedure in three-dimensional images is highly time consuming and the reproducibility is low, automated methods introduce substantial gains. On the other hand, the implementation of those methods is a challenge because of the low contrast of this structure in relation to the neighboring areas of the brain. Within this context, this research presents a review of the evolution of automatized methods for the segmentation of the hippocampus in MRI. Many proposed methods for segmentation of the hippocampus have been published in leading journals in the medical image processing area. This paper describes these methods presenting the techniques used and quantitatively comparing the methods based on Dice Similarity Coefficient. Finally, we present an evaluation of those methods considering the degree of user intervention, computational cost, segmentation accuracy and feasibility of application in a clinical routine.

Measuring Size and Shape of the Hippocampus in MR Images Using a Deformable Shape Model

A method for segmentation and quantification of the shape and size of the hippocampus is proposed, based on an automated image analysis algorithm. The algorithm uses a deformable shape model to locate the hippocampus in magnetic resonance images and to determine a geometric representation of its boundary. The deformable model combines three types of information. First, it employs information about the geometric properties of the hippocampal boundary, from a local and relatively finer scale to a more global and relatively coarser scale. Second, the model includes a statistical characterization of normal shape variation across individuals, serving as prior knowledge to the algorithm. Third, the algorithm utilizes a number of manually defined boundary points, which can help guide the model deformation to the appropriate boundaries, wherever these boundaries are weak or not clearly defined in MR images. Excellent agreement is demonstrated between the algorithm and manual segmentations by well-trained raters, with a correlation coefficient equal to 0.97 and algorithm/rater differences statistically equivalent to interrater differences for manual definitions.