Hippocampal Shape Modeling Based on a Progressive Template Surface Deformation and its Verification

AI-based deformable hippocampal mesh reflects hippocampal morphological characteristics in relation to cognition in healthy older adults

Magnetic resonance imaging (MRI)-derived hippocampus measurements have been associated with different cognitive domains. The knowledge of hippocampal structural deformations as we age has contributed to our understanding of the overall aging process. Different morphological hippocampal shape analysis methods have been developed, but it is unclear how their principles relate and how consistent are the published results in relation to cognition in the normal elderly in the light of the new deep-learning-based (DL) state-of-the-art modeling methods. We compared results from analyzing the hippocampal morphology using manually-generated binary masks and a Laplacian- based deformation shape analysis method, with those resulting from analyzing SynthSeg-generated hippocampal binary masks using a DL method based on the PointNet architecture, in relation to different cognitive domains. Whilst most previously reported statistically significant associations were also replicated, differences were also observed due to 1) differences in the binary masks and 2) differences in sensitivity between the methods. Differences in the template mesh, number of vertices of the template mesh, and their distribution did not impact the results.

A semi-supervised deep neuro-fuzzy iterative learning system for automatic segmentation of hippocampus brain MRI

The hippocampus is a small, yet intricate seahorse-shaped tiny structure located deep within the brain's medial temporal lobe. It is a crucial component of the limbic system, which is responsible for regulating emotions, memory, and spatial navigation. This research focuses on automatic hippocampus segmentation from Magnetic Resonance (MR) images of a human head with high accuracy and fewer false positive and false negative rates. This segmentation technique is significantly faster than the manual segmentation methods used in clinics. Unlike the existing approaches such as UNet and Convolutional Neural Networks (CNN), the proposed algorithm generates an image that is similar to a real image by learning the distribution much more quickly by the semi-supervised iterative learning algorithm of the Deep Neuro-Fuzzy (DNF) technique. To assess its effectiveness, the proposed segmentation technique was evaluated on a large dataset of 18,900 images from Kaggle, and the results were compared with those of existing methods. Based on the analysis of results reported in the experimental section, the proposed scheme in the Semi-Supervised Deep Neuro-Fuzzy Iterative Learning System (SS-DNFIL) achieved a 0.97 Dice coefficient, a 0.93 Jaccard coefficient, a 0.95 sensitivity (true positive rate), a 0.97 specificity (true negative rate), a false positive value of 0.09 and a 0.08 false negative value when compared to existing approaches. Thus, the proposed segmentation techniques outperform the existing techniques and produce the desired result so that an accurate diagnosis is made at the earliest stage to save human lives and to increase their life span.

Shape Reconstruction for Abdominal Organs based on a Graph Convolutional Network

Computed tomography and magnetic resonance imaging produce high-resolution images; however, during surgery or radiotherapy, only low-resolution cone-beam CT and low-dimensional X-ray images can be obtained. Furthermore, because the duodenum and stomach are filled with air, even in high-resolution CT images, it is hard to accurately segment their contours. In this paper, we propose a method that is based on a graph convolutional network (GCN) to reconstruct organs that are hard to detect in medical images. The method uses surrounding detectable-organ features to determine the shape and location of the target organ and learns mesh deformation parameters, which are applied to a target organ template. The role of the template is to establish an initial topological structure for the target organ. We conducted experiments with both single and multiple organ meshes to verify the performance of our proposed method.

Deformation analysis of surface and bronchial structures in intraoperative pneumothorax using deformable mesh registration

The positions of nodules can change because of intraoperative lung deflation, and the modeling of pneumothorax-associated deformation remains a challenging issue for intraoperative tumor localization. In this study, we introduce spatial and geometric analysis methods for inflated/deflated lungs and discuss heterogeneity in pneumothorax-associated lung deformation. Contrast-enhanced CT images simulating intraoperative conditions were acquired from live Beagle dogs. The images contain the overall shape of the lungs, including all lobes and internal bronchial structures, and were analyzed to provide a statistical deformation model that could be used as prior knowledge to predict pneumothorax. To address the difficulties of mapping pneumothorax CT images with topological changes and CT intensity shifts, we designed deformable mesh registration techniques for mixed data structures including the lobe surfaces and the bronchial centerlines. Three global-to-local registration steps were performed under the constraint that the deformation was spatially continuous and smooth, while matching visible bronchial tree structures as much as possible. The developed framework achieved stable registration with a Hausdorff distance of less than 1 mm and a target registration error of less than 5 mm, and visualized deformation fields that demonstrate per-lobe contractions and rotations with high variability between subjects. The deformation analysis results show that the strain of lung parenchyma was 35% higher than that of bronchi, and that deformation in the deflated lung is heterogeneous.

Nonlinear interaction between APOE ε4 allele load and age in the hippocampal surface of cognitively intact individuals

The ε4 allele of the gene Apolipoprotein E is the major genetic risk factor for Alzheimer's Disease. APOE ε4 has been associated with changes in brain structure in cognitively impaired and unimpaired subjects, including atrophy of the hippocampus, which is one of the brain structures that is early affected by AD. In this work we analyzed the impact of APOE ε4 gene dose and its association with age, on hippocampal shape assessed with multivariate surface analysis, in a ε4-enriched cohort of n = 479 cognitively healthy individuals. Furthermore, we sought to replicate our findings on an independent dataset of n = 969 individuals covering the entire AD spectrum. We segmented the hippocampus of the subjects with a multi-atlas-based approach, obtaining high-dimensional meshes that can be analyzed in a multivariate way. We analyzed the effects of different factors including APOE, sex, and age (in both cohorts) as well as clinical diagnosis on the local 3D hippocampal surface changes. We found specific regions on the hippocampal surface where the effect is modulated by significant APOE ε4 linear and quadratic interactions with age. We compared between APOE and diagnosis effects from both cohorts, finding similarities between APOE ε4 and AD effects on specific regions, and suggesting that age may modulate the effect of APOE ε4 and AD in a similar way.

Alzheimer's diagnosis using deep learning in segmenting and classifying 3D brain MR images

BACKGROUND AND OBJECTIVES

Dementia is one of the brain diseases with serious symptoms such as memory loss, and thinking problems. According to the World Alzheimer Report 2016, in the world, there are 47 million people having dementia and it can be 131 million by 2050. There is no standard method to diagnose dementia, and consequently unable to access the treatment effectively. Hence, the computational diagnosis of the disease from brain Magnetic Resonance Image (MRI) scans plays an important role in supporting the early diagnosis. Alzheimer's Disease (AD), a common type of Dementia, includes problems related to disorientation, mood swings, not managing self-care, and behavioral issues. In this article, we present a new computational method to diagnosis Alzheimer's disease from 3D brain MR images.

METHODS

An efficient approach to diagnosis Alzheimer's disease from brain MRI scans is proposed comprising two phases: I) segmentation and II) classification, both based on deep learning. After the brain tissues are segmented by a model that combines Gaussian Mixture Model (GMM) and Convolutional Neural Network (CNN), a new model combining Extreme Gradient Boosting (XGBoost) and Support Vector Machine (SVM) is used to classify Alzheimer's disease based on the segmented tissues.

RESULTS

We present two evaluations for segmentation and classification. For comparison, the new method was evaluated using the AD-86 and AD-126 datasets leading to Dice 0.96 for segmentation in both datasets and accuracies 0.88, and 0.80 for classification, respectively.

CONCLUSION

Deep learning gives prominent results for segmentation and feature extraction in medical image processing. The combination of XGboost and SVM improves the results obtained.

X-ray2Shape: Reconstruction of 3D Liver Shape from a Single 2D Projection Image

Computed tomography (CT) and magnetic resonance imaging (MRI) scanners measure three-dimensional (3D) images of patients. However, only low-dimensional local two-dimensional (2D) images may be obtained during surgery or radiotherapy. Although computer vision techniques have shown that 3D shapes can be estimated from multiple 2D images, shape reconstruction from a single 2D image such as an endoscopic image or an X-ray image remains a challenge. In this study, we propose X-ray2Shape, which permits a deep learning-based 3D organ mesh to be reconstructed from a single 2D projection image. The method learns the mesh deformation from a mean template and deep features computed from the individual projection images. Experiments with organ meshes and digitally reconstructed radiograph (DRR) images of abdominal regions were performed to confirm the estimation performance of the methods.

Performance evaluation of a newly developed three-dimensional model-based global-to-local registration in prostate cancer

We evaluated the performance of a newly developed three-dimensional (3D) model-based global-to-local registration of multiple organs, by comparing it with a 3D model-based global registration in the prostate region. This study included 220 prostate cancer patients who underwent intensity-modulated radiotherapy or volumetric-modulated arc therapy. Our registration proceeded sequentially, i.e. global registration including affine and piece-wise affine transformation followed by local registration. As a local registration, Laplacian-based and finite element method-based registration was implemented in Algorithm A and B, respectively. Algorithm C was for global registration alone. The template models for the prostate, seminal vesicles, rectum and bladder were constructed from the first 20 patients, and then three different registrations were performed on these organs for the remaining 200 patients, to assess registration accuracy. The 75th percentile Hausdorff distance was <1 mm in Algorithm A; it was >1 mm in Algorithm B, except for the prostate; and 3.9 mm for the prostate and >7.8 mm for other organs in Algorithm C. The median computation time to complete registration was <101, 30 and 16 s in Algorithms A, B and C, respectively. Analysis of variance revealed significant differences among Algorithms A-C in the Hausdorff distance and computation time. In addition, no significant difference was observed in the difference of Hausdorff distance between Algorithm A and B with Tukey's multiple comparison test. The 3D model-based global-to-local registration, especially that implementing Laplacian-based registration, completed surface registration rapidly and provided sufficient registration accuracy in the prostate region.

Longitudinal Neuroimaging Hippocampal Markers for Diagnosing Alzheimer's Disease

Hippocampal atrophy measures from magnetic resonance imaging (MRI) are powerful tools for monitoring Alzheimer's disease (AD) progression. In this paper, we introduce a longitudinal image analysis framework based on robust registration and simultaneous hippocampal segmentation and longitudinal marker classification of brain MRI of an arbitrary number of time points. The framework comprises two innovative parts: a longitudinal segmentation and a longitudinal classification step. The results show that both steps of the longitudinal pipeline improved the reliability and the accuracy of the discrimination between clinical groups. We introduce a novel approach to the joint segmentation of the hippocampus across multiple time points; this approach is based on graph cuts of longitudinal MRI scans with constraints on hippocampal atrophy and supported by atlases. Furthermore, we use linear mixed effect (LME) modeling for differential diagnosis between clinical groups. The classifiers are trained from the average residue between the longitudinal marker of the subjects and the LME model. In our experiments, we analyzed MRI-derived longitudinal hippocampal markers from two publicly available datasets (Alzheimer's Disease Neuroimaging Initiative, ADNI and Minimal Interval Resonance Imaging in Alzheimer's Disease, MIRIAD). In test/retest reliability experiments, the proposed method yielded lower volume errors and significantly higher dice overlaps than the cross-sectional approach (volume errors: 1.55% vs 0.8%; dice overlaps: 0.945 vs 0.975). To diagnose AD, the discrimination ability of our proposal gave an area under the receiver operating characteristic (ROC) curve (AUC) [Formula: see text] 0.947 for the control vs AD, AUC [Formula: see text] 0.720 for mild cognitive impairment (MCI) vs AD, and AUC [Formula: see text] 0.805 for the control vs MCI.

Surface deformation analysis of collapsed lungs using model-based shape matching

Purpose

To facilitate intraoperative localization of lung nodules, this study used model-based shape matching techniques to analyze the inter-subject three-dimensional surface deformation induced by pneumothorax. Methods: Contrast- enhanced computed tomography (CT) images of the left lungs of 11 live beagle dogs were acquired at two bronchial pressures (14 and 2 cm\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\,\hbox {H}_2\hbox {O}$$\end{document}H2O). To address shape matching problems for largely deformed lung images with pixel intensity shift, a complete Laplacian-based shape matching solution that optimizes the differential displacement field was introduced.

Results

Experiments were performed to confirm the methods’ registration accuracy using CT images of lungs. Shape similarity and target displacement errors in the registered models were improved compared with those from existing shape matching methods. Spatial displacement of the whole lung’s surface was visualized with an average error of within 5 mm.

Conclusion

The proposed methods address problems with the matching of surfaces with large curvatures and deformations and achieved smaller registration errors than existing shape matching methods, even at the tip and ridge regions. The findings and inter-subject statistical representation are directly available for further research on pneumothorax deformation modeling.

Electronic supplementary material

The online version of this article (10.1007/s11548-019-02013-0) contains supplementary material, which is available to authorized users.

Discriminating Alzheimer's disease progression using a new hippocampal marker from T1-weighted MRI: The local surface roughness

Hippocampal atrophy is one of the main hallmarks of Alzheimer's disease (AD). However, there is still controversy about whether this sign is a robust finding during the early stages of the disease, such as in mild cognitive impairment (MCI) and subjective cognitive decline (SCD). Considering this background, we proposed a new marker for assessing hippocampal atrophy: the local surface roughness (LSR). We tested this marker in a sample of 307 subjects (normal control (NC) = 70, SCD = 87, MCI = 137, AD = 13). In addition, 97 patients with MCI were followed-up over a 3-year period and classified as stable MCI (sMCI) (n = 61) or progressive MCI (pMCI) (n = 36). We did not find significant differences using traditional markers, such as normalized hippocampal volumes (NHV), between the NC and SCD groups or between the sMCI and pMCI groups. However, with LSR we found significant differences between the sMCI and pMCI groups and a better ability to discriminate between NC and SCD. The classification accuracy of the LSR for NC and SCD was 68.2%, while NHV had a 57.2% accuracy. In addition, the classification accuracy of the LSR for sMCI and pMCI was 74.3%, and NHV had a 68.3% accuracy. Cox proportional hazards models adjusted for age, sex, and education were used to estimate the relative hazard of progression from MCI to AD based on hippocampal markers and conversion times. The LSR marker showed better prediction of conversion to AD than NHV. These results suggest the relevance of considering the LSR as a new hippocampal marker for the AD continuum.

3-D Shape Matching and Non-Rigid Correspondence for Hippocampi Based on Markov Random Fields

The purpose of this paper is to recover dense correspondence between non-rigid shapes for anatomical objects, which is a key element of disease diagnosis and analysis. We proposed a shape matching framework based on Markov random fields to obtain non-rigid correspondence. We constructed an energy function by summing up two terms where one was a unary term and the other was a binary term. By using this formulation, shape matching was represented as an energy function minimisation problem. Loopy belief propagation (LBP) was then used to minimize the energy function. We adopted a new sparse update technique for LBP update to increase computational efficiency. At the same time, we also proposed to use a novel clamping technique, an expectation-maximization (EM) like approach, to enhance matching accuracy. Experiments with the hippocampal data from OASIS and PATH showed that the sparse update was 160 times faster than standard BP. By iteratively running the EM-like clamping procedure, we were able to obtain high quality non-rigid correspondence results to achieve 97% matching rate between two hippocampi. Our shape matching based approach overcomes the flip problem of first-order ellipsoid and does not assume pre-alignment unlike iterative closest point.

Associations between hippocampal morphology, diffusion characteristics, and salivary cortisol in older men

High, unabated glucocorticoid (GC) levels are thought to selectively damage certain tissue types. The hippocampus is thought to be particularly susceptible to such effects, and though findings from animal models and human patients provide some support for this hypothesis, evidence for associations between elevated GCs and lower hippocampal volumes in older age (when GC levels are at greater risk of dysregulation) is inconclusive. To address the possibility that the effects of GCs in non-pathological ageing may be too subtle for gross volumetry to reliably detect, we analyse associations between salivary cortisol (diurnal and reactive measures), hippocampal morphology and diffusion characteristics in 88 males, aged ∼73 years. However, our results provide only weak support for this hypothesis. Though nominally significant peaks in morphology were found in both hippocampi across all salivary cortisol measures (standardised β magnitudes<0.518, p_uncorrected>0.0000003), associations were both positive and negative, and none survived false discovery rate correction. We found one single significant association (out of 12 comparisons) between a general measure of hippocampal diffusion and reactive cortisol slope (β=0.290, p=0.008) which appeared to be driven predominantly by mean diffusivity but did not survive correction for multiple testing. The current data therefore do not clearly support the hypothesis that elevated cortisol levels are associated with subtle variations in hippocampal shape or microstructure in non-pathological older age.

Morphological and Microstructural Changes of the Hippocampus in Early MCI: A Study Utilizing the Alzheimer's Disease Neuroimaging Initiative Database

Background and Purpose

With the aim of facilitating the early detection of Alzheimer's disease, the Alzheimer's Disease Neuroimaging Initiative proposed two stages based on the memory performance: early mild cognitive impairment (EMCI) and late mild cognitive impairment (LMCI). The current study was designed to investigate structural differences in terms of surface atrophy and microstructural changes of the hippocampus in EMCI and LMCI.

Methods

Hippocampal shape modeling based on progressive template surface deformation was performed on T1-weighted MRI images obtained from 20 cognitive normal (CN) subjects, 17 EMCI patients, and 20 LMCI patients. A template surface in CN was used as a region of interest for diffusion-tensor imaging (DTI) voxel-based morphometry (VBM) analysis. Cluster-wise group comparison was performed based on DTI indices within the hippocampus. Linear regression was performed to identify correlations between DTI metrics and clinical scores.

Results

The hippocampal surface analysis showed significant atrophies in bilateral CA1 regions and the right ventral subiculum in EMCI, in contrast to widespread atrophy in LMCI. DTI VBM analysis showed increased diffusivity in the CA2–CA4 regions in EMCI and additionally in the subiculum region in LMCI. Hippocampal diffusivity was significantly correlated with scores both for the Mini Mental State Examination and on the Modified Alzheimer Disease Assessment Scale cognitive subscale. However, the hippocampal diffusivity did not vary significantly with the fractional anisotropy.

Conclusions

EMCI showed hippocampal surface changes mainly in the CA1 region and ventral subiculum. Diffusivity increased mainly in the CA2–CA4 regions in EMCI, while it decreased throughout the hippocampus in LMCI. Although axial diffusivity showed prominent changes in the right hippocampus in EMCI, future studies need to confirm the presence of this laterality difference. In addition, diffusivity is strongly correlated with the cognitive performance, indicating the possibility of using diffusivity as a biomarker for disease progression.

Hippocampal morphology and cognitive functions in community-dwelling older people: the Lothian Birth Cohort 1936

Structural measures of the hippocampus have been linked to a variety of memory processes and also to broader cognitive abilities. Gross volumetry has been widely used, yet the hippocampus has a complex formation, comprising distinct subfields which may be differentially sensitive to the deleterious effects of age, and to different aspects of cognitive performance. However, a comprehensive analysis of multidomain cognitive associations with hippocampal deformations among a large group of cognitively normal older adults is currently lacking. In 654 participants of the Lothian Birth Cohort 1936 (mean age = 72.5, SD = 0.71 years), we examined associations between the morphology of the hippocampus and a variety of memory tests (spatial span, letter-number sequencing, verbal recall, and digit backwards), as well as broader cognitive domains (latent measures of speed, fluid intelligence, and memory). Following correction for age, sex, and vascular risk factors, analysis of memory subtests revealed that only right hippocampal associations in relation to spatial memory survived type 1 error correction in subiculum and in CA1 at the head (β = 0.201, p = 5.843 × 10^-4, outward), and in the ventral tail section of CA1 (β = -0.272, p = 1.347 × 10^-5, inward). With respect to latent measures of cognitive domains, only deformations associated with processing speed survived type 1 error correction in bilateral subiculum (β_absolute ≤ 0.247, p < 1.369 × 10^-4, outward), bilaterally in the ventral tail section of CA1 (β_absolute ≤ 0.242, p < 3.451 × 10^-6, inward), and a cluster at the left anterior-to-dorsal region of the head (β = 0.199, p = 5.220 × 10^-6, outward). Overall, our results indicate that a complex pattern of both inward and outward hippocampal deformations are associated with better processing speed and spatial memory in older age, suggesting that complex shape-based hippocampal analyses may provide valuable information beyond gross volumetry.

3D shape analysis of the brain's third ventricle using a midplane encoded symmetric template model

BACKGROUND

Structural changes of the brain's third ventricle have been acknowledged as an indicative measure of the brain atrophy progression in neurodegenerative and endocrinal diseases. To investigate the ventricular enlargement in relation to the atrophy of the surrounding structures, shape analysis is a promising approach. However, there are hurdles in modeling the third ventricle shape. First, it has topological variations across individuals due to the inter-thalamic adhesion. In addition, as an interhemispheric structure, it needs to be aligned to the midsagittal plane to assess its asymmetric and regional deformation.

METHOD

To address these issues, we propose a model-based shape assessment. Our template model of the third ventricle consists of a midplane and a symmetric mesh of generic shape. By mapping the template's midplane to the individuals' brain midsagittal plane, we align the symmetric mesh on the midline of the brain before quantifying the third ventricle shape. To build the vertex-wise correspondence between the individual third ventricle and the template mesh, we employ a minimal-distortion surface deformation framework. In addition, to account for topological variations, we implement geometric constraints guiding the template mesh to have zero width where the inter-thalamic adhesion passes through, preventing vertices crossing between left and right walls of the third ventricle. The individual shapes are compared using a vertex-wise deformity from the symmetric template.

RESULTS

Experiments on imaging and demographic data from a study of aging showed that our model was sensitive in assessing morphological differences between individuals in relation to brain volume (i.e. proxy for general brain atrophy), gender and the fluid intelligence at age 72. It also revealed that the proposed method can detect the regional and asymmetrical deformation unlike the conventional measures: volume (median 1.95ml, IQR 0.96ml) and width of the third ventricle. Similarity measures between binary masks and the shape model showed that the latter reconstructed shape details with high accuracy (Dice coefficient ≥0.9, mean distance 0.5mm and Hausdorff distance 2.7mm).

CONCLUSIONS

We have demonstrated that our approach is suitable to morphometrical analyses of the third ventricle, providing high accuracy and inter-subject consistency in the shape quantification. This shape modeling method with geometric constraints based on anatomical landmarks could be extended to other brain structures which require a consistent measurement basis in the morphometry.

Rationale, design and methodology of the image analysis protocol for studies of patients with cerebral small vessel disease and mild stroke

RATIONALE

Cerebral small vessel disease (SVD) is common in ageing and patients with dementia and stroke. Its manifestations on magnetic resonance imaging (MRI) include white matter hyperintensities, lacunes, microbleeds, perivascular spaces, small subcortical infarcts, and brain atrophy. Many studies focus only on one of these manifestations. A protocol for the differential assessment of all these features is, therefore, needed.

AIMS

To identify ways of quantifying imaging markers in research of patients with SVD and operationalize the recommendations from the STandards for ReportIng Vascular changes on nEuroimaging guidelines. Here, we report the rationale, design, and methodology of a brain image analysis protocol based on our experience from observational longitudinal studies of patients with nondisabling stroke.

DESIGN

The MRI analysis protocol is designed to provide quantitative and qualitative measures of disease evolution including: acute and old stroke lesions, lacunes, tissue loss due to stroke, perivascular spaces, microbleeds, macrohemorrhages, iron deposition in basal ganglia, substantia nigra and brain stem, brain atrophy, and white matter hyperintensities, with the latter separated into intense and less intense. Quantitative measures of tissue integrity such as diffusion fractional anisotropy, mean diffusivity, and the longitudinal relaxation time are assessed in regions of interest manually placed in anatomically and functionally relevant locations, and in others derived from feature extraction pipelines and tissue segmentation methods. Morphological changes that relate to cognitive deficits after stroke, analyzed through shape models of subcortical structures, complete the multiparametric image analysis protocol.

OUTCOMES

Final outcomes include guidance for identifying ways to minimize bias and confounds in the assessment of SVD and stroke imaging biomarkers. It is intended that this information will inform the design of studies to examine the underlying pathophysiology of SVD and stroke, and to provide reliable, quantitative outcomes in trials of new therapies and preventative strategies.