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Singh MK. Reproducibility and Reliability of Computing Models in Segmentation and Volumetric Measurement of Brain. Ann Neurosci 2023; 30:224-229. [PMID: 38020401 PMCID: PMC10662274 DOI: 10.1177/09727531231159959] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 02/10/2023] [Indexed: 12/01/2023] Open
Abstract
Background Segmentation and morphometric measurement of brain tissue and regions from non-invasive magnetic resonance images have clinical and research applications. Several software tools and models have been developed by different research groups which are increasingly used for segmentation and morphometric measurements. Variability in results has been observed in the imaging data processed with different neuroimaging pipelines which have increased the focus on standardization. Purpose The availability of several tools and models for brain morphometry poses challenges as an analysis done on the same set of data using different sets of tools and pipelines may result in different results and interpretations and there is a need for understanding the reliability and accuracy of such models. Methods T1-weighted (T1-w) brain volumes from the publicly available OASIS3 dataset have been analysed using recent versions of FreeSurfer, FSL-FAST, CAT12, and ANTs pipelines. grey matter (GM), white matter (WM), and estimated total intracranial volume (eTIV) have been extracted and compared for inter-method variability and accuracy. Results All four methods are consistent and strongly reproducible in their measurement across subjects however there is a significant degree of variability between these methods. Conclusion CAT12 and FreeSurfer methods have the highest degree of agreement in tissue class segmentation and are most reproducible compared to others.
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Affiliation(s)
- Mahender Kumar Singh
- National Brain Research Centre, Manesar, Gurugram, Haryana, India
- Starex University, Binola, Gurugram, Haryana, India
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2
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Zheng W, Liu H, Li Z, Li K, Wang Y, Hu B, Dong Q, Wang Z. Classification of Alzheimer's disease based on hippocampal multivariate morphometry statistics. CNS Neurosci Ther 2023; 29:2457-2468. [PMID: 37002795 PMCID: PMC10401169 DOI: 10.1111/cns.14189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 03/07/2023] [Accepted: 03/13/2023] [Indexed: 08/05/2023] Open
Abstract
BACKGROUND Alzheimer's disease (AD) is a neurodegenerative disease characterized by progressive cognitive decline, and mild cognitive impairment (MCI) is associated with a high risk of developing AD. Hippocampal morphometry analysis is believed to be the most robust magnetic resonance imaging (MRI) markers for AD and MCI. Multivariate morphometry statistics (MMS), a quantitative method of surface deformations analysis, is confirmed to have strong statistical power for evaluating hippocampus. AIMS We aimed to test whether surface deformation features in hippocampus can be employed for early classification of AD, MCI, and healthy controls (HC). METHODS We first explored the differences in hippocampus surface deformation among these three groups by using MMS analysis. Additionally, the hippocampal MMS features of selective patches and support vector machine (SVM) were used for the binary classification and triple classification. RESULTS By the results, we identified significant hippocampal deformation among the three groups, especially in hippocampal CA1. In addition, the binary classification of AD/HC, MCI/HC, AD/MCI showed good performances, and area under curve (AUC) of triple-classification model achieved 0.85. Finally, positive correlations were found between the hippocampus MMS features and cognitive performances. CONCLUSIONS The study revealed significant hippocampal deformation among AD, MCI, and HC. Additionally, we confirmed that hippocampal MMS can be used as a sensitive imaging biomarker for the early diagnosis of AD at the individual level.
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Affiliation(s)
- Weimin Zheng
- Department of Radiology, Aerospace Center Hospital, Beijing, China
| | - Honghong Liu
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
| | - Zhigang Li
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
| | - Kuncheng Li
- Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, Arizona, USA
| | - Bin Hu
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
| | - Qunxi Dong
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
| | - Zhiqun Wang
- Department of Radiology, Aerospace Center Hospital, Beijing, China
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3
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Valizadeh G, Babapour Mofrad F. A Comprehensive Survey on Two and Three-Dimensional Fourier Shape Descriptors: Biomedical Applications. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING 2022; 29:4643-4681. [DOI: 10.1007/s11831-022-09750-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 04/11/2022] [Indexed: 10/12/2024]
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4
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Wang G, Zhou W, Kong D, Qu Z, Ba M, Hao J, Yao T, Dong Q, Su Y, Reiman EM, Caselli RJ, Chen K, Wang Y. Studying APOE ɛ4 Allele Dose Effects with a Univariate Morphometry Biomarker. J Alzheimers Dis 2022; 85:1233-1250. [PMID: 34924383 PMCID: PMC10498787 DOI: 10.3233/jad-215149] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
BACKGROUND A univariate neurodegeneration biomarker (UNB) based on MRI with strong statistical discrimination power would be highly desirable for studying hippocampal surface morphological changes associated with APOE ɛ4 genetic risk for AD in the cognitively unimpaired (CU) population. However, existing UNB work either fails to model large group variances or does not capture AD induced changes. OBJECTIVE We proposed a subspace decomposition method capable of exploiting a UNB to represent the hippocampal morphological changes related to the APOE ɛ4 dose effects among the longitudinal APOE ɛ4 homozygotes (HM, N = 30), heterozygotes (HT, N = 49) and non-carriers (NC, N = 61). METHODS Rank minimization mechanism combined with sparse constraint considering the local continuity of the hippocampal atrophy regions is used to extract group common structures. Based on the group common structures of amyloid-β (Aβ) positive AD patients and Aβ negative CU subjects, we identified the regions-of-interest (ROI), which reflect significant morphometry changes caused by the AD development. Then univariate morphometry index (UMI) is constructed from these ROIs. RESULTS The proposed UMI demonstrates a more substantial statistical discrimination power to distinguish the longitudinal groups with different APOE ɛ4 genotypes than the hippocampal volume measurements. And different APOE ɛ4 allele load affects the shrinkage rate of the hippocampus, i.e., HM genotype will cause the largest atrophy rate, followed by HT, and the smallest is NC. CONCLUSION The UMIs may capture the APOE ɛ4 risk allele-induced brain morphometry abnormalities and reveal the dose effects of APOE ɛ4 on the hippocampal morphology in cognitively normal individuals.
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Affiliation(s)
- Gang Wang
- School of Ulsan Ship and Ocean College, Ludong University, Yantai, China
| | - Wenju Zhou
- School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China
| | - Deping Kong
- School of Information and Electrical Engineering, Ludong University, Yantai, China
| | - Zongshuai Qu
- School of Information and Electrical Engineering, Ludong University, Yantai, China
| | - Maowen Ba
- Department of Neurology, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, China
| | - Jinguang Hao
- School of Information and Electrical Engineering, Ludong University, Yantai, China
| | - Tao Yao
- School of Information and Electrical Engineering, Ludong University, Yantai, China
| | - Qunxi Dong
- Institute of Engineering Medicine, Beijing Institute of Technology, Beijing, China
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA
| | - Yi Su
- Banner Alzheimer’s Institute, 100 Washtenaw Avenue, Phoenix, AZ, USA
| | - Eric M Reiman
- Banner Alzheimer’s Institute, 100 Washtenaw Avenue, Phoenix, AZ, USA
| | | | - Kewei Chen
- Banner Alzheimer’s Institute, 100 Washtenaw Avenue, Phoenix, AZ, USA
| | - Yalin Wang
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA
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5
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Lee S, Kim TD, Kim RY, Joo Y, Chung YA, Lim SM, Lyoo IK, Kim J, Yoon S. Hippocampal subregional alterations and verbal fluency in the early stage of type 2 diabetes mellitus. Eur J Neurosci 2021; 54:7550-7559. [PMID: 34687097 DOI: 10.1111/ejn.15505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 10/16/2021] [Accepted: 10/21/2021] [Indexed: 11/30/2022]
Abstract
Growing evidence indicates that type 2 diabetes mellitus (T2DM)-related cognitive dysfunction may develop in the early stage of the disease and is often accompanied by hippocampal structural alterations. In the current study, we investigated volume and shape alterations of the hippocampus at a subregional level in patients with T2DM. With the use of high-resolution brain structural images that were obtained from 30 T2DM patients with less than 5 years of disease duration and 30 healthy individuals, volumetric and shape analyses were performed. We also assessed the relationship between T2DM-related hippocampal structural alterations and performance on verbal fluency. In volumetric analysis, total hippocampal volume was smaller in the T2DM group, relative to the control group. At a subregional level, T2DM patients showed significant inward deformation and volume reduction of the right dentate gyrus and cornu ammonis 2/3 subregions as compared with healthy individuals. In particular, T2DM patients with lower performance on verbal fluency had smaller right dentate gyrus volumes relative to those with higher performance. These findings suggest that the hippocampus may undergo atrophy at a subregional level even in the early stage of T2DM, and this subregion-specific atrophy may be associated with reduced performance on verbal fluency.
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Affiliation(s)
- Suji Lee
- Ewha Brain Institute, Ewha Womans University, Seoul, South Korea
| | - Tammy D Kim
- Ewha Brain Institute, Ewha Womans University, Seoul, South Korea
| | - Rye Young Kim
- Ewha Brain Institute, Ewha Womans University, Seoul, South Korea.,Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul, South Korea
| | - Yoonji Joo
- Ewha Brain Institute, Ewha Womans University, Seoul, South Korea.,Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul, South Korea
| | - Yong-An Chung
- Department of Radiology, Incheon St. Mary's Hospital, The Catholic University of Korea College of Medicine, Incheon, South Korea
| | - Soo Mee Lim
- Department of Radiology, College of Medicine, Ewha Womans University, Seoul, South Korea
| | - In Kyoon Lyoo
- Ewha Brain Institute, Ewha Womans University, Seoul, South Korea.,Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul, South Korea.,Department of Brain and Cognitive Sciences, Ewha Womans University, Seoul, South Korea.,The Brain Institute and Department of Psychiatry, University of Utah, Salt Lake City, Utah, USA
| | - Jungyoon Kim
- Ewha Brain Institute, Ewha Womans University, Seoul, South Korea.,Department of Brain and Cognitive Sciences, Ewha Womans University, Seoul, South Korea
| | - Sujung Yoon
- Ewha Brain Institute, Ewha Womans University, Seoul, South Korea.,Department of Brain and Cognitive Sciences, Ewha Womans University, Seoul, South Korea
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6
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Ren X, Wu Y, Cao Z. Hippocampus Segmentation Method Based on Subspace Patch-Sparsity Clustering in Noisy Brain MRI. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:3937222. [PMID: 34608408 PMCID: PMC8487389 DOI: 10.1155/2021/3937222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 09/10/2021] [Accepted: 09/16/2021] [Indexed: 11/17/2022]
Abstract
Since the hippocampus is of small size, low contrast, and irregular shape, a novel hippocampus segmentation method based on subspace patch-sparsity clustering in brain MRI is proposed to improve the segmentation accuracy, which requires that the representation coefficients in different subspaces should be as sparse as possible, while the representation coefficients in the same subspace should be as average as possible. By restraining the coefficient matrix with the patch-sparse constraint, the coefficient matrix contains a patch-sparse structure, which is helpful to the hippocampus segmentation. The experimental results show that our proposed method is effective in the noisy brain MRI data, which can well deal with hippocampus segmentation problem.
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Affiliation(s)
- Xiaogang Ren
- Changshu Hospital of Chinese Medicine, Changshu 215516, Jiangsu, China
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China
| | - Yue Wu
- The Affiliated Changshu Hospital of Soochow University (Changshu No. 1 People's Hospital), Suzhou, Jiangsu 215500, China
| | - Zhiying Cao
- The Affiliated Changshu Hospital of Soochow University (Changshu No. 1 People's Hospital), Suzhou, Jiangsu 215500, China
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7
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An N, Fu Y, Shi J, Guo HN, Yang ZW, Li YC, Li S, Wang Y, Yao ZJ, Hu B. Synergistic Effects of APOE and CLU May Increase the Risk of Alzheimer's Disease: Acceleration of Atrophy in the Volumes and Shapes of the Hippocampus and Amygdala. J Alzheimers Dis 2021; 80:1311-1327. [PMID: 33682707 DOI: 10.3233/jad-201162] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
BACKGROUND The volume loss of the hippocampus and amygdala in non-demented individuals has been reported to increase the risk of developing Alzheimer's disease (AD). Many neuroimaging genetics studies mainly focused on the individual effects of APOE and CLU on neuroimaging to understand their neural mechanisms, whereas their synergistic effects have been rarely studied. OBJECTIVE To assess whether APOE and CLU have synergetic effects, we investigated the epistatic interaction and combined effects of the two genetic variants on morphological degeneration of hippocampus and amygdala in the non-demented elderly at baseline and 2-year follow-up. METHODS Besides the widely-used volume indicator, the surface-based morphometry method was also adopted in this study to evaluate shape alterations. RESULTS Our results showed a synergistic effect of homozygosity for the CLU risk allele C in rs11136000 and APOEɛ4 on the hippocampal and amygdalar volumes during a 2-year follow-up. Moreover, the combined effects of APOEɛ4 and CLU C were stronger than either of the individual effects in the atrophy progress of the amygdala. CONCLUSION These findings indicate that brain morphological changes are caused by more than one gene variant, which may help us to better understand the complex endogenous mechanism of AD.
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Affiliation(s)
- Na An
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Yu Fu
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Jie Shi
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Han-Ning Guo
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Zheng-Wu Yang
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Yong-Chao Li
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Shan Li
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Yin Wang
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Zhi-Jun Yao
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Bin Hu
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China.,Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.,Beijing Institute for Brain Disorders, Capital Medical University, Beijing, China
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8
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Wu J, Dong Q, Gui J, Zhang J, Su Y, Chen K, Thompson PM, Caselli RJ, Reiman EM, Ye J, Wang Y. Predicting Brain Amyloid Using Multivariate Morphometry Statistics, Sparse Coding, and Correntropy: Validation in 1,101 Individuals From the ADNI and OASIS Databases. Front Neurosci 2021; 15:669595. [PMID: 34421510 PMCID: PMC8377280 DOI: 10.3389/fnins.2021.669595] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Accepted: 07/15/2021] [Indexed: 01/04/2023] Open
Abstract
Biomarker assisted preclinical/early detection and intervention in Alzheimer’s disease (AD) may be the key to therapeutic breakthroughs. One of the presymptomatic hallmarks of AD is the accumulation of beta-amyloid (Aβ) plaques in the human brain. However, current methods to detect Aβ pathology are either invasive (lumbar puncture) or quite costly and not widely available (amyloid PET). Our prior studies show that magnetic resonance imaging (MRI)-based hippocampal multivariate morphometry statistics (MMS) are an effective neurodegenerative biomarker for preclinical AD. Here we attempt to use MRI-MMS to make inferences regarding brain Aβ burden at the individual subject level. As MMS data has a larger dimension than the sample size, we propose a sparse coding algorithm, Patch Analysis-based Surface Correntropy-induced Sparse-coding and Max-Pooling (PASCS-MP), to generate a low-dimensional representation of hippocampal morphometry for each individual subject. Then we apply these individual representations and a binary random forest classifier to predict brain Aβ positivity for each person. We test our method in two independent cohorts, 841 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and 260 subjects from the Open Access Series of Imaging Studies (OASIS). Experimental results suggest that our proposed PASCS-MP method and MMS can discriminate Aβ positivity in people with mild cognitive impairment (MCI) [Accuracy (ACC) = 0.89 (ADNI)] and in cognitively unimpaired (CU) individuals [ACC = 0.79 (ADNI) and ACC = 0.81 (OASIS)]. These results compare favorably relative to measures derived from traditional algorithms, including hippocampal volume and surface area, shape measures based on spherical harmonics (SPHARM) and our prior Patch Analysis-based Surface Sparse-coding and Max-Pooling (PASS-MP) methods.
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Affiliation(s)
- Jianfeng Wu
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, United States
| | - Qunxi Dong
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, United States.,Institute of Engineering Medicine, Beijing Institute of Technology, Beijing, China
| | - Jie Gui
- School of Cyber Science and Engineering, Southeast University, Nanjing, China
| | - Jie Zhang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, United States
| | - Yi Su
- Banner Alzheimer's Institute, Phoenix, AZ, United States
| | - Kewei Chen
- Banner Alzheimer's Institute, Phoenix, AZ, United States
| | - Paul M Thompson
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, University of Southern California, Marina del Rey, CA, United States
| | - Richard J Caselli
- Department of Neurology, Mayo Clinic Arizona, Scottsdale, AZ, United States
| | - Eric M Reiman
- Banner Alzheimer's Institute, Phoenix, AZ, United States
| | - Jieping Ye
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, United States
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Singh MK, Singh KK. A Review of Publicly Available Automatic Brain Segmentation Methodologies, Machine Learning Models, Recent Advancements, and Their Comparison. Ann Neurosci 2021; 28:82-93. [PMID: 34733059 PMCID: PMC8558983 DOI: 10.1177/0972753121990175] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Accepted: 01/04/2021] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND The noninvasive study of the structure and functions of the brain using neuroimaging techniques is increasingly being used for its clinical and research perspective. The morphological and volumetric changes in several regions and structures of brains are associated with the prognosis of neurological disorders such as Alzheimer's disease, epilepsy, schizophrenia, etc. and the early identification of such changes can have huge clinical significance. The accurate segmentation of three-dimensional brain magnetic resonance images into tissue types (i.e., grey matter, white matter, cerebrospinal fluid) and brain structures, thus, has huge importance as they can act as early biomarkers. The manual segmentation though considered the "gold standard" is time-consuming, subjective, and not suitable for bigger neuroimaging studies. Several automatic segmentation tools and algorithms have been developed over the years; the machine learning models particularly those using deep convolutional neural network (CNN) architecture are increasingly being applied to improve the accuracy of automatic methods. PURPOSE The purpose of the study is to understand the current and emerging state of automatic segmentation tools, their comparison, machine learning models, their reliability, and shortcomings with an intent to focus on the development of improved methods and algorithms. METHODS The study focuses on the review of publicly available neuroimaging tools, their comparison, and emerging machine learning models particularly those based on CNN architecture developed and published during the last five years. CONCLUSION Several software tools developed by various research groups and made publicly available for automatic segmentation of the brain show variability in their results in several comparison studies and have not attained the level of reliability required for clinical studies. The machine learning models particularly three dimensional fully convolutional network models can provide a robust and efficient alternative with relation to publicly available tools but perform poorly on unseen datasets. The challenges related to training, computation cost, reproducibility, and validation across distinct scanning modalities for machine learning models need to be addressed.
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Affiliation(s)
| | - Krishna Kumar Singh
- Symbiosis Centre for Information
Technology, Hinjawadi, Pune, Maharashtra, India
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10
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Abstract
OBJECTIVES With an increasing aging population, it is important to understand biological markers of aging. Subcortical volume is known to differ with age; additionally considering shape-related characteristics may provide a better index of age-related differences. Fractal dimensionality is more sensitive to age-related differences, but is borne out of mathematical principles, rather than neurobiological relevance. We considered four distinct measures of shape and how they relate to aging and fractal dimensionality: surface-to-volume ratio, sphericity, long-axis curvature, and surface texture. METHODS Structural MRIs from a combined sample of over 600 healthy adults were used to measure age-related differences in the structure of the thalamus, putamen, caudate, and hippocampus. For each, volume and fractal dimensionality were calculated, as well as four distinct shape measures. These measures were examined for their utility in explaining age-related variability in brain structure. RESULTS The four shape measures were able to account for 80%-90% of the variance in fractal dimensionality. Of the distinct shape measures, surface-to-volume ratio was the most sensitive biomarker. CONCLUSION Though volume is often used to characterize inter-individual differences in subcortical structures, our results demonstrate that additional measures can be useful complements. Our results indicate that shape characteristics are useful biological markers of aging.
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Affiliation(s)
- Christopher R Madan
- a School of Psychology , University of Nottingham , Nottingham , UK.,b Department of Psychology , Boston College , Chestnut Hill , MA , USA
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11
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Dong Q, Zhang W, Wu J, Li B, Schron EH, McMahon T, Shi J, Gutman BA, Chen K, Baxter LC, Thompson PM, Reiman EM, Caselli RJ, Wang Y. Applying surface-based hippocampal morphometry to study APOE-E4 allele dose effects in cognitively unimpaired subjects. NEUROIMAGE-CLINICAL 2019; 22:101744. [PMID: 30852398 PMCID: PMC6411498 DOI: 10.1016/j.nicl.2019.101744] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2018] [Revised: 02/02/2019] [Accepted: 03/02/2019] [Indexed: 11/30/2022]
Abstract
Apolipoprotein E (APOE) e4 is the major genetic risk factor for late-onset Alzheimer's disease (AD). The dose-dependent impact of this allele on hippocampal volumes has been documented, but its influence on general hippocampal morphology in cognitively unimpaired individuals is still elusive. Capitalizing on the study of a large number of cognitively unimpaired late middle aged and older adults with two, one and no APOE-e4 alleles, the current study aims to characterize the ability of our automated surface-based hippocampal morphometry algorithm to distinguish between these three levels of genetic risk for AD and demonstrate its superiority to a commonly used hippocampal volume measurement. We examined the APOE-e4 dose effect on cross-sectional hippocampal morphology analysis in a magnetic resonance imaging (MRI) database of 117 cognitively unimpaired subjects aged between 50 and 85 years (mean = 57.4, SD = 6.3), including 36 heterozygotes (e3/e4), 37 homozygotes (e4/e4) and 44 non-carriers (e3/e3). The proposed automated framework includes hippocampal surface segmentation and reconstruction, higher-order hippocampal surface correspondence computation, and hippocampal surface deformation analysis with multivariate statistics. In our experiments, the surface-based method identified APOE-e4 dose effects on the left hippocampal morphology. Compared to the widely-used hippocampal volume measure, our hippocampal morphometry statistics showed greater statistical power by distinguishing cognitively unimpaired subjects with two, one, and no APOE-e4 alleles. Our findings mirrored previous studies showing that APOE-e4 has a dose effect on the acceleration of brain structure deformities. The results indicated that the proposed surface-based hippocampal morphometry measure is a potential preclinical AD imaging biomarker for cognitively unimpaired individuals. Applied surface-based hippocampal morphometry on cognitively unimpaired subjects. Our study identified APOE-e4 dose effects on cognitively unimpaired subjects. Surface-based hippocampal morphometry outperformed the hippocampal volume measure. Surface-based hippocampal morphometry may be a potential preclinical AD biomarker.
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Affiliation(s)
- Qunxi Dong
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Wen Zhang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Jianfeng Wu
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Bolun Li
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | | | - Travis McMahon
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Jie Shi
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Boris A Gutman
- Armour College of Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Kewei Chen
- Banner Alzheimer's Institute, Phoenix, AZ, USA
| | - Leslie C Baxter
- Human Brain Imaging Laboratory, Barrow Neurological Institute, Phoenix, AZ, USA
| | - Paul M Thompson
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
| | | | | | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.
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12
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Naceur MB, Saouli R, Akil M, Kachouri R. Fully Automatic Brain Tumor Segmentation using End-To-End Incremental Deep Neural Networks in MRI images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 166:39-49. [PMID: 30415717 DOI: 10.1016/j.cmpb.2018.09.007] [Citation(s) in RCA: 70] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2018] [Revised: 09/16/2018] [Accepted: 09/18/2018] [Indexed: 06/09/2023]
Abstract
BACKGROUND AND OBJECTIVE Nowadays, getting an efficient Brain Tumor Segmentation in Multi-Sequence MR images as soon as possible, gives an early clinical diagnosis, treatment and follow-up. The aim of this study is to develop a new deep learning model for the segmentation of brain tumors. The proposed models are used to segment the brain tumors of Glioblastomas (with both high and low grade). Glioblastomas have four properties: different sizes, shapes, contrasts, in addition, Glioblastomas appear anywhere in the brain. METHODS In this paper, we propose three end-to-end Incremental Deep Convolutional Neural Networks models for fully automatic Brain Tumor Segmentation. Our proposed models are different from the other CNNs-based models that follow the technique of trial and error process which does not use any guided approach to get the suitable hyper-parameters. Moreover, we adopt the technique of Ensemble Learning to design a more efficient model. For solving the problem of training CNNs model, we propose a new training strategy which takes into account the most influencing hyper-parameters by bounding and setting a roof to these hyper-parameters to accelerate the training. RESULTS Our experiment results reported on BRATS-2017 dataset. The proposed deep learning models achieve the state-of-the-art performance without any post-processing operations. Indeed, our models achieve in average 0.88 Dice score over the complete region. Moreover, the efficient design with the advantage of GPU implementation, allows our three deep learning models to achieve brain segmentation results in average 20.87 s. CONCLUSIONS The proposed deep learning models are effective for the segmentation of brain tumors and allow to obtain high accurate results. Moreover, the proposed models could help the physician experts to reduce the time of diagnostic.
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Affiliation(s)
- Mostefa Ben Naceur
- Smart Computer Sciences Laboratory, Department of Computer Sciences, University of Biskra, Biskra, Algeria; Gaspard Monge Computer Science Laboratory, ESIEE-Paris, University Paris-Est Marne-la-Vallée, France.
| | - Rachida Saouli
- Smart Computer Sciences Laboratory, Department of Computer Sciences, University of Biskra, Biskra, Algeria.
| | - Mohamed Akil
- Gaspard Monge Computer Science Laboratory, ESIEE-Paris, University Paris-Est Marne-la-Vallée, France.
| | - Rostom Kachouri
- Gaspard Monge Computer Science Laboratory, ESIEE-Paris, University Paris-Est Marne-la-Vallée, France.
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Madan CR, Kensinger EA. Test-retest reliability of brain morphology estimates. Brain Inform 2017; 4:107-121. [PMID: 28054317 PMCID: PMC5413592 DOI: 10.1007/s40708-016-0060-4] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2016] [Accepted: 12/26/2016] [Indexed: 12/17/2022] Open
Abstract
Metrics of brain morphology are increasingly being used to examine inter-individual differences, making it important to evaluate the reliability of these structural measures. Here we used two open-access datasets to assess the intersession reliability of three cortical measures (thickness, gyrification, and fractal dimensionality) and two subcortical measures (volume and fractal dimensionality). Reliability was generally good, particularly with the gyrification and fractal dimensionality measures. One dataset used a sequence previously optimized for brain morphology analyses and had particularly high reliability. Examining the reliability of morphological measures is critical before the measures can be validly used to investigate inter-individual differences.
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Affiliation(s)
- Christopher R Madan
- Department of Psychology, Boston College, McGuinn 300, 140 Commonwealth Ave., Chestnut Hill, MA, 02467, USA.
| | - Elizabeth A Kensinger
- Department of Psychology, Boston College, McGuinn 300, 140 Commonwealth Ave., Chestnut Hill, MA, 02467, USA
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Hong-Seng G, Sayuti KA, Karim AHA. Investigation of random walks knee cartilage segmentation model using inter-observer reproducibility: Data from the osteoarthritis initiative. Biomed Mater Eng 2017; 28:75-85. [PMID: 28372262 DOI: 10.3233/bme-171658] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
BACKGROUND Existing knee cartilage segmentation methods have reported several technical drawbacks. In essence, graph cuts remains highly susceptible to image noise despite extended research interest; active shape model is often constraint by the selection of training data while shortest path have demonstrated shortcut problem in the presence of weak boundary, which is a common problem in medical images. OBJECTIVES The aims of this study is to investigate the capability of random walks as knee cartilage segmentation method. METHODS Experts would scribble on knee cartilage image to initialize random walks segmentation. Then, reproducibility of the method is assessed against manual segmentation by using Dice Similarity Index. The evaluation consists of normal cartilage and diseased cartilage sections which is divided into whole and single cartilage categories. RESULTS A total of 15 normal images and 10 osteoarthritic images were included. The results showed that random walks method has demonstrated high reproducibility in both normal cartilage (observer 1: 0.83±0.028 and observer 2: 0.82±0.026) and osteoarthritic cartilage (observer 1: 0.80±0.069 and observer 2: 0.83±0.029). Besides, results from both experts were found to be consistent with each other, suggesting the inter-observer variation is insignificant (Normal: P=0.21; Diseased: P=0.15). CONCLUSION The proposed segmentation model has overcame technical problems reported by existing semi-automated techniques and demonstrated highly reproducible and consistent results against manual segmentation method.
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Affiliation(s)
- Gan Hong-Seng
- Medical Engineering Technology Section, Universiti Kuala Lumpur, British Malaysian Institute, 53100 Gombak, Selangor, Malaysia
| | - Khairil Amir Sayuti
- Department of Radiology, School of Medical Science, Universiti Sains Malaysia, 16150 Kubang Kerian, Kelantan, Malaysia
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15
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Influence of APOE Genotype on Hippocampal Atrophy over Time - An N=1925 Surface-Based ADNI Study. PLoS One 2016; 11:e0152901. [PMID: 27065111 PMCID: PMC4827849 DOI: 10.1371/journal.pone.0152901] [Citation(s) in RCA: 49] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2015] [Accepted: 03/21/2016] [Indexed: 11/25/2022] Open
Abstract
The apolipoprotein E (APOE) e4 genotype is a powerful risk factor for late-onset Alzheimer’s disease (AD). In the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort, we previously reported significant baseline structural differences in APOE e4 carriers relative to non-carriers, involving the left hippocampus more than the right—a difference more pronounced in e4 homozygotes than heterozygotes. We now examine the longitudinal effects of APOE genotype on hippocampal morphometry at 6-, 12- and 24-months, in the ADNI cohort. We employed a new automated surface registration system based on conformal geometry and tensor-based morphometry. Among different hippocampal surfaces, we computed high-order correspondences, using a novel inverse-consistent surface-based fluid registration method and multivariate statistics consisting of multivariate tensor-based morphometry (mTBM) and radial distance. At each time point, using Hotelling’s T2 test, we found significant morphological deformation in APOE e4 carriers relative to non-carriers in the full cohort as well as in the non-demented (pooled MCI and control) subjects at each follow-up interval. In the complete ADNI cohort, we found greater atrophy of the left hippocampus than the right, and this asymmetry was more pronounced in e4 homozygotes than heterozygotes. These findings, combined with our earlier investigations, demonstrate an e4 dose effect on accelerated hippocampal atrophy, and support the enrichment of prevention trial cohorts with e4 carriers.
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Tu L, Yang D, Vicory J, Zhang X, Pizer SM, Styner M. Fitting Skeletal Object Models Using Spherical Harmonics Based Template Warping. IEEE SIGNAL PROCESSING LETTERS 2015; 22:2269-2273. [PMID: 31402834 PMCID: PMC6688764 DOI: 10.1109/lsp.2015.2476366] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
We present a scheme that propagates a reference skeletal model (s-rep) into a particular case of an object, thereby propagating the initial shape-related layout of the skeleton-to-boundary vectors, called spokes. The scheme represents the surfaces of the template as well as the target objects by spherical harmonics and computes a warp between these via a thin plate spline. To form the propagated s-rep, it applies the warp to the spokes of the template s-rep and then statistically refines. This automatic approach promises to make s-rep fitting robust for complicated objects, which allows s-rep based statistics to be available to all. The improvement in fitting and statistics is significant compared with the previous methods and in statistics compared with a state-of-the-art boundary based method.
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Affiliation(s)
- Liyun Tu
- College of Computer Science, Chongqing University, Chongqing 400044 China, and also with the Department of Computer Science, University of North Carolina at Chapel Hill, NC 27599 USA
| | - Dan Yang
- College of Computer Science, Chongqing University, Chongqing 400044 China
| | - Jared Vicory
- Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599 USA
| | - Xiaohong Zhang
- School of Software Engineering, Chongqing University, Chongqing 400044 China, and also with the Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education, Chongqing 400044 China
| | - Stephen M Pizer
- Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599 USA
| | - Martin Styner
- Department of Computer Science and the Department of Psychiatry, University of North Carolina, Chapel Hill, NC 27599 USA
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17
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Lee JK, Nordahl CW, Amaral DG, Lee A, Solomon M, Ghetti S. Assessing hippocampal development and language in early childhood: Evidence from a new application of the Automatic Segmentation Adapter Tool. Hum Brain Mapp 2015; 36:4483-96. [PMID: 26279309 DOI: 10.1002/hbm.22931] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2015] [Revised: 07/23/2015] [Accepted: 07/28/2015] [Indexed: 01/13/2023] Open
Abstract
Volumetric assessments of the hippocampus and other brain structures during childhood provide useful indices of brain development and correlates of cognitive functioning in typically and atypically developing children. Automated methods such as FreeSurfer promise efficient and replicable segmentation, but may include errors which are avoided by trained manual tracers. A recently devised automated correction tool that uses a machine learning algorithm to remove systematic errors, the Automatic Segmentation Adapter Tool (ASAT), was capable of substantially improving the accuracy of FreeSurfer segmentations in an adult sample [Wang et al., 2011], but the utility of ASAT has not been examined in pediatric samples. In Study 1, the validity of FreeSurfer and ASAT corrected hippocampal segmentations were examined in 20 typically developing children and 20 children with autism spectrum disorder aged 2 and 3 years. We showed that while neither FreeSurfer nor ASAT accuracy differed by disorder or age, the accuracy of ASAT corrected segmentations were substantially better than FreeSurfer segmentations in every case, using as few as 10 training examples. In Study 2, we applied ASAT to 89 typically developing children aged 2 to 4 years to examine relations between hippocampal volume, age, sex, and expressive language. Girls had smaller hippocampi overall, and in left hippocampus this difference was larger in older than younger girls. Expressive language ability was greater in older children, and this difference was larger in those with larger hippocampi, bilaterally. Overall, this research shows that ASAT is highly reliable and useful to examinations relating behavior to hippocampal structure.
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Affiliation(s)
- Joshua K Lee
- Department of Psychology, University of California, Davis, California.,Center for Mind and Brain, University of California, Davis, California
| | | | - David G Amaral
- MIND Institute, University of California, Davis, California
| | - Aaron Lee
- MIND Institute, University of California, Davis, California
| | | | - Simona Ghetti
- Department of Psychology, University of California, Davis, California.,Center for Mind and Brain, University of California, Davis, California
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Shi J, Leporé N, Gutman BA, Thompson PM, Baxter LC, Caselli RJ, Wang Y. Genetic influence of apolipoprotein E4 genotype on hippocampal morphometry: An N = 725 surface-based Alzheimer's disease neuroimaging initiative study. Hum Brain Mapp 2014; 35:3903-18. [PMID: 24453132 PMCID: PMC4269525 DOI: 10.1002/hbm.22447] [Citation(s) in RCA: 54] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2013] [Revised: 11/23/2013] [Accepted: 11/26/2013] [Indexed: 01/12/2023] Open
Abstract
The apolipoprotein E (APOE) e4 allele is the most prevalent genetic risk factor for Alzheimer's disease (AD). Hippocampal volumes are generally smaller in AD patients carrying the e4 allele compared to e4 noncarriers. Here we examined the effect of APOE e4 on hippocampal morphometry in a large imaging database-the Alzheimer's Disease Neuroimaging Initiative (ADNI). We automatically segmented and constructed hippocampal surfaces from the baseline MR images of 725 subjects with known APOE genotype information including 167 with AD, 354 with mild cognitive impairment (MCI), and 204 normal controls. High-order correspondences between hippocampal surfaces were enforced across subjects with a novel inverse consistent surface fluid registration method. Multivariate statistics consisting of multivariate tensor-based morphometry (mTBM) and radial distance were computed for surface deformation analysis. Using Hotelling's T(2) test, we found significant morphological deformation in APOE e4 carriers relative to noncarriers in the entire cohort as well as in the nondemented (pooled MCI and control) subjects, affecting the left hippocampus more than the right, and this effect was more pronounced in e4 homozygotes than heterozygotes. Our findings are consistent with previous studies that showed e4 carriers exhibit accelerated hippocampal atrophy; we extend these findings to a novel measure of hippocampal morphometry. Hippocampal morphometry has significant potential as an imaging biomarker of early stage AD.
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Affiliation(s)
- Jie Shi
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State UniversityTempeArizona
| | - Natasha Leporé
- Department of RadiologyChildren's Hospital Los AngelesLos AngelesCalifornia
| | - Boris A. Gutman
- Imaging Genetics CenterInstitute for Neuroimaging and InformaticsUniversity of Southern CaliforniaLos AngelesCalifornia
| | - Paul M. Thompson
- Department of NeurologyImaging Genetics CenterLaboratory of Neuro ImagingUCLA School of MedicineLos AngelesCalifornia
- Department of Psychiatry and Biobehavioral SciencesSemel Institute, UCLA School of MedicineLos AngelesCalifornia
| | - Leslie C. Baxter
- Human Brain Imaging Laboratory, Barrow Neurological InstitutePhoenixArizona
| | | | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State UniversityTempeArizona
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Royle N, Hernández MV, Maniega SM, Arabisala B, Bastin M, Deary I, Wardlaw J. Influence of thickening of the inner skull table on intracranial volume measurement in older people. Magn Reson Imaging 2013; 31:918-22. [PMID: 23453763 PMCID: PMC3682185 DOI: 10.1016/j.mri.2013.01.012] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2012] [Revised: 01/15/2013] [Accepted: 01/17/2013] [Indexed: 11/19/2022]
Abstract
Introduction It is generally assumed that intracranial volume (ICV) remains constant after peaking in early adulthood. Thus ICV is used as a ‘proxy’ for original brain size when trying to estimate brain atrophy in older people in neuroimaging studies. However, physiological changes in the skull, such as thickening of the frontal inner table, are relatively common in older age and will reduce ICV. The potential influence that inner table skull thickening may have on ICV measurement in old age has yet to be investigated. Methods We selected 60 (31 males, 29 females) representative older adults aged 71.1–74.3 years from a community-dwelling ageing cohort, the Lothian Birth Cohort 1936. A semi-automatically derived current ICV measurement obtained from high resolution T1-weighted volume scans was compared to the estimated original ICV by excluding inner skull table thickening using expert manual image processing. Results Inner table skull thickening reduced ICV from an estimated original 1480.0 ml to a current 1409.1 ml, a median decrease of 7.3% (Z = − 6.334; p < 0.001), and this reduction was more prominent in women than men (median decrease 114.6 vs. 101.9 ml respectively). This led to potential significant underestimations of brain atrophy in this sample by 5.3% (p < 0.001) and obscured potential gender differences. Conclusions The effects of skull thickening are important to consider when conducting research in ageing, as they can obscure gender differences and result in underestimation of brain atrophy. Research into reliable methods of determining the estimated original ICV is required for research into brain ageing.
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Affiliation(s)
- N.A. Royle
- Brain Research Imaging Centre, Neuroimaging Sciences, University of Edinburgh, Edinburgh, EH4 2XU, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Department of Clinical Neurosciences, The University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - M.C. Valdés Hernández
- Brain Research Imaging Centre, Neuroimaging Sciences, University of Edinburgh, Edinburgh, EH4 2XU, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Department of Clinical Neurosciences, The University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - S. Muñoz Maniega
- Brain Research Imaging Centre, Neuroimaging Sciences, University of Edinburgh, Edinburgh, EH4 2XU, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Department of Clinical Neurosciences, The University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - B.S. Arabisala
- Brain Research Imaging Centre, Neuroimaging Sciences, University of Edinburgh, Edinburgh, EH4 2XU, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Department of Clinical Neurosciences, The University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - M.E. Bastin
- Brain Research Imaging Centre, Neuroimaging Sciences, University of Edinburgh, Edinburgh, EH4 2XU, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Department of Clinical Neurosciences, The University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - I.J. Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
- Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - J.M. Wardlaw
- Brain Research Imaging Centre, Neuroimaging Sciences, University of Edinburgh, Edinburgh, EH4 2XU, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Department of Clinical Neurosciences, The University of Edinburgh, Edinburgh, EH4 2XU, UK
- Corresponding author. Brain Research Imaging Centre (BRIC), Neuroimaging Sciences, Western General Hospital, Edinburgh, EH4 2XU, UK. Tel.: + 44 131 537 2943.
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Towards automated detection of depression from brain structural magnetic resonance images. Neuroradiology 2013; 55:567-84. [PMID: 23338839 DOI: 10.1007/s00234-013-1139-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2012] [Accepted: 01/07/2013] [Indexed: 01/06/2023]
Abstract
INTRODUCTION Depression is a major issue worldwide and is seen as a significant health problem. Stigma and patient denial, clinical experience, time limitations, and reliability of psychometrics are barriers to the clinical diagnoses of depression. Thus, the establishment of an automated system that could detect such abnormalities would assist medical experts in their decision-making process. This paper reviews existing methods for the automated detection of depression from brain structural magnetic resonance images (sMRI). METHODS Relevant sources were identified from various databases and online sites using a combination of keywords and terms including depression, major depressive disorder, detection, classification, and MRI databases. Reference lists of chosen articles were further reviewed for associated publications. RESULTS The paper introduces a generic structure for representing and describing the methods developed for the detection of depression from sMRI of the brain. It consists of a number of components including acquisition and preprocessing, feature extraction, feature selection, and classification. CONCLUSION Automated sMRI-based detection methods have the potential to provide an objective measure of depression, hence improving the confidence level in the diagnosis and prognosis of depression.
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Maksimovich IV. The tomography dementia rating scale (TDR)—The rating scale of Alzheimer’s disease stages. Health (London) 2012. [DOI: 10.4236/health.2012.429111] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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Comparison of manual and automated determination of hippocampal volumes in MCI and early AD. Brain Imaging Behav 2010; 4:86-95. [PMID: 20454594 DOI: 10.1007/s11682-010-9088-x] [Citation(s) in RCA: 63] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
MRI-based hippocampal volume analysis has been extensively employed given its potential as a biomarker for brain disorders such as Alzheimer's disease (AD), and accurate and efficient determination of hippocampal volumes from brain images is still a challenging issue. We compared an automated method, FreeSurfer (V4), with a published manual protocol for the determination of hippocampal volumes from T1-weighted MRI scans. Our study included MRI data from 125 older adult subjects: healthy controls with no significant cognitive complaints or deficits (HC, n=38), euthymic individuals with cognitive complaints (CC, n=39) but intact neuropsychological performance, and patients with amnestic mild cognitive impairment (MCI, n=37) or a clinical diagnosis of probable AD (AD, n=11). Pearson correlations and intraclass correlation coefficients (ICCs) were calculated to evaluate the relationship between results of the manual tracing and FreeSurfer methods and to estimate their agreement. Results indicated that these two methods derived highly correlated results with strong agreement. After controlling for the age, sex and intracranial volume in statistical group analysis, both the manual tracing and FreeSurfer methods yield similar patterns: both the MCI group and the AD group showed hippocampal volume reduction compared to both the HC group and the CC group, and the HC and CC groups did not differ. These comparisons suggest that FreeSurfer has the potential to be used in automated determination of hippocampal volumes for large-scale MCI/AD-related MRI studies, where manual methods are inefficient or not feasible.
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Bigler ED, Abildskov TJ, Wilde EA, McCauley SR, Li X, Merkley TL, Fearing MA, Newsome MR, Scheibel RS, Hunter JV, Chu Z, Levin HS. Diffuse damage in pediatric traumatic brain injury: A comparison of automated versus operator-controlled quantification methods. Neuroimage 2010; 50:1017-26. [DOI: 10.1016/j.neuroimage.2010.01.003] [Citation(s) in RCA: 66] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2009] [Revised: 12/05/2009] [Accepted: 01/01/2010] [Indexed: 11/17/2022] Open
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Dewey J, Hana G, Russell T, Price J, McCaffrey D, Harezlak J, Sem E, Anyanwu JC, Guttmann CR, Navia B, Cohen R, Tate DF. Reliability and validity of MRI-based automated volumetry software relative to auto-assisted manual measurement of subcortical structures in HIV-infected patients from a multisite study. Neuroimage 2010; 51:1334-44. [PMID: 20338250 DOI: 10.1016/j.neuroimage.2010.03.033] [Citation(s) in RCA: 106] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2009] [Revised: 03/04/2010] [Accepted: 03/11/2010] [Indexed: 11/15/2022] Open
Abstract
The automated volumetric output of FreeSurfer and Individual Brain Atlases using Statistical Parametric Mapping (IBASPM), two widely used and well published software packages, was examined for accuracy and consistency relative to auto-assisted manual (AAM) tracings (i.e., manual correction of automated output) when measuring the caudate, putamen, amygdala, and hippocampus in the baseline scans of 120 HIV-infected patients (86.7% male, 47.3+/-6.3y.o., mean HIV duration 12.0+/-6.3years) from the NIH-funded HIV Neuroimaging Consortium (HIVNC) cohort. The data was examined for accuracy and consistency relative to auto-assisted manual tracing, and construct validity was assessed by correlating automated and AAM volumetric measures with relevant clinical measures of HIV progression. When results were averaged across all patients in the eight structures examined, FreeSurfer achieved lower absolute volume difference in five, higher sensitivity in seven, and higher spatial overlap in all eight structures. Additionally, FreeSurfer results exhibited less variability in all measures. Output from both methods identified discrepant correlations with clinical measures of HIV progression relative to AAM segmented data. Overall, FreeSurfer proved more effective in the context of subcortical volumetry in HIV-patients, particularly in a multisite cohort study such as this. These findings emphasize that regardless of the automated method used, visual inspection of segmentation output, along with manual correction if necessary, remains critical to ensuring the validity of reported results.
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Affiliation(s)
- Jeffrey Dewey
- Center for Neurological Imaging, Brigham and Women's Hospital, Boston, MA, USA
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