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Holikova K, Selingerova I, Pospisil P, Bulik M, Hynkova L, Kolouskova I, Hnidakova L, Burkon P, Slavik M, Sana J, Holecek T, Vanicek J, Slampa P, Jancalek R, Kazda T. Hippocampal subfield volumetric changes after radiotherapy for brain metastases. Neurooncol Adv 2024; 6:vdae040. [PMID: 38645488 PMCID: PMC11032105 DOI: 10.1093/noajnl/vdae040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/23/2024] Open
Abstract
Background Changes in the hippocampus after brain metastases radiotherapy can significantly impact neurocognitive functions. Numerous studies document hippocampal atrophy correlating with the radiation dose. This study aims to elucidate volumetric changes in patients undergoing whole-brain radiotherapy (WBRT) or targeted stereotactic radiotherapy (SRT) and to explore volumetric changes in the individual subregions of the hippocampus. Method Ten patients indicated to WBRT and 18 to SRT underwent brain magnetic resonance before radiotherapy and after 4 months. A structural T1-weighted sequence was used for volumetric analysis, and the software FreeSurfer was employed as the tool for the volumetry evaluation of 19 individual hippocampal subregions. Results The volume of the whole hippocampus, segmented by the software, was larger than the volume outlined by the radiation oncologist. No significant differences in volume changes were observed in the right hippocampus. In the left hippocampus, the only subregion with a smaller volume after WBRT was the granular cells and molecular layers of the dentate gyrus (GC-ML-DG) region (median change -5 mm3, median volume 137 vs. 135 mm3; P = .027), the region of the presumed location of neuronal progenitors. Conclusions Our study enriches the theory that the loss of neural stem cells is involved in cognitive decline after radiotherapy, contributes to the understanding of cognitive impairment, and advocates for the need for SRT whenever possible to preserve cognitive functions in patients undergoing brain radiotherapy.
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Affiliation(s)
- Klara Holikova
- Department of Medical Imaging, St. Anne’s University Hospital Brno and Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Iveta Selingerova
- Research Center for Applied Molecular Oncology, Masaryk Memorial Cancer Institute, Brno, Czech Republic
| | - Petr Pospisil
- Department of Radiation Oncology, Masaryk Memorial Cancer Institute, Brno, Czech Republic
- Department of Radiation Oncology, Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Martin Bulik
- Department of Medical Imaging, St. Anne’s University Hospital Brno and Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Ludmila Hynkova
- Department of Radiation Oncology, Masaryk Memorial Cancer Institute, Brno, Czech Republic
- Department of Radiation Oncology, Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Ivana Kolouskova
- Department of Comprehensive Cancer Care, Masaryk Memorial Cancer Institute, Brno, Czech Republic
- Department of Comprehensive Cancer Care, Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Lucie Hnidakova
- Department of Radiation Oncology, Masaryk Memorial Cancer Institute, Brno, Czech Republic
- Department of Radiation Oncology, Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Petr Burkon
- Department of Radiation Oncology, Masaryk Memorial Cancer Institute, Brno, Czech Republic
- Department of Radiation Oncology, Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Marek Slavik
- Department of Radiation Oncology, Masaryk Memorial Cancer Institute, Brno, Czech Republic
- Department of Radiation Oncology, Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Jiri Sana
- Department of Comprehensive Cancer Care, Masaryk Memorial Cancer Institute, Brno, Czech Republic
- Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Tomas Holecek
- Department of Medical Imaging, St. Anne’s University Hospital Brno and Faculty of Medicine, Masaryk University, Brno, Czech Republic
- Research Center for Applied Molecular Oncology, Masaryk Memorial Cancer Institute, Brno, Czech Republic
- Department of Biomedical Engineering, Brno University of Technology, Brno, Czech Republic
| | - Jiri Vanicek
- Department of Medical Imaging, St. Anne’s University Hospital Brno and Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Pavel Slampa
- Department of Radiation Oncology, Masaryk Memorial Cancer Institute, Brno, Czech Republic
- Department of Radiation Oncology, Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Radim Jancalek
- Department of Neurosurgery, St. Anne’s University Hospital Brno, Brno, Czech Republic
- Department of Neurosurgery, St. Anne’s University Hospital Brno, Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Tomas Kazda
- Department of Radiation Oncology, Masaryk Memorial Cancer Institute, Brno, Czech Republic
- Department of Radiation Oncology, Faculty of Medicine, Masaryk University, Brno, Czech Republic
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Wang C, Wei Y, Li J, Li X, Liu Y, Hu Q, Wang Y. Asymmetry-enhanced attention network for Alzheimer's diagnosis with structural Magnetic Resonance Imaging. Comput Biol Med 2022; 151:106282. [PMID: 36413817 DOI: 10.1016/j.compbiomed.2022.106282] [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: 05/07/2022] [Revised: 10/25/2022] [Accepted: 10/30/2022] [Indexed: 11/11/2022]
Abstract
BACKGROUND AND OBJECTIVE With the aging of the global population becoming severe, Alzheimer's disease (AD) has become one of the world's most common senile diseases. Many studies have suggested that the brain's left-right asymmetry is one of the possible diagnostic landmarks for AD. However, most published approaches to classification problems may not adequately explore the asymmetry between the left and right hemispheres. At the same time, the relationship between asymmetry traits and other classifier features remains understudied. METHODS In this paper, we proposed an asymmetry enhanced attention network (ASEAN) for AD diagnosis that effectively combines the anatomical asymmetry characteristics of the brain to enhance the accuracy and stability of classification tasks. First, we proposed a multi-scale asymmetry feature extraction module (MSAF) that can extract the asymmetry features of the brain from various scales. Second, we proposed an asymmetry refinement module (ARM) that considers the dependency between feature maps to suppress the irrelevant regions of the asymmetric feature maps. In addition, a parameter-free attention module was introduced to infer 4D attention weights and improve the network's representation capability. RESULTS The proposed method achieved performance improvements on two databases: Alzheimer's Disease Neuroimaging Initiative (ADNI) and Australian Imaging, Biomarkers and Lifestyle (AIBL). For the classification tasks on ADNI, the proposed method achieves 92.1% accuracy, 96.2% sensitivity, and 91.3% specificity on the AD vs. CN (Cognitively Normal) task. Compared with state-of-the-art methods, the proposed method could achieve comparable results. CONCLUSION The proposed model can extract long-range left-right brain similarity as complementary information and improve the model's diagnostic performance. A large number of experiments also support the model's validity. At the same time, this work provides a valuable reference for other neurological diseases, particularly those that exhibit left-right brain asymmetry during development.
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Affiliation(s)
- Chuyuan Wang
- College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
| | - Ying Wei
- College of Information Science and Engineering, Northeastern University, Shenyang 110819, China; Information Technology R&D Innovation Center of Peking University, Shaoxing, China; Changsha Hisense Intelligent System Research Institute Co., Ltd., China.
| | - Jiaguang Li
- College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
| | - Xiang Li
- College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
| | - Yue Liu
- College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
| | - Qian Hu
- College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
| | - Yuefeng Wang
- College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
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Zheng G, Zhang Y, Zhao Z, Wang Y, Liu X, Shang Y, Cong Z, Dimitriadis S, Yao Z, Hu B. A Transformer-based Multi-features Fusion Model for Prediction of Conversion in Mild Cognitive Impairment. Methods 2022; 204:241-248. [PMID: 35487442 DOI: 10.1016/j.ymeth.2022.04.015] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 03/26/2022] [Accepted: 04/24/2022] [Indexed: 12/21/2022] Open
Abstract
Mild cognitive impairment (MCI) is usually considered the early stage of Alzheimer's disease (AD). Therefore, the accurate identification of MCI individuals with high risk in converting to AD is essential for the potential prevention and treatment of AD. Recently, the great success of deep learning has sparked interest in applying deep learning to neuroimaging field. However, deep learning techniques are prone to overfitting since available neuroimaging datasets are not sufficiently large. Therefore, we proposed a deep learning model fusing cortical features to address the issue of fusion and classification blocks. To validate the effectiveness of the proposed model, we compared seven different models on the same dataset in the literature. The results show that our proposed model outperformed the competing models in the prediction of MCI conversion with an accuracy of 83.3% in the testing dataset. Subsequently, we used deep learning to characterize the contribution of brain regions and different cortical features to MCI progression. The results revealed that the caudal anterior cingulate and pars orbitalis contributed most to the classification task, and our model pays more attention to volume features and cortical thickness features.
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Affiliation(s)
- Guowei Zheng
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Yu Zhang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Ziyang Zhao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Yin Wang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Xia Liu
- School of Computer Science, Qinghai Normal University, Xining, China
| | - Yingying Shang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Zhaoyang Cong
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Stavros Dimitriadis
- Integrative Neuroimaging Lab, 55133, Thessaloniki (Makedonia), Greece; Neuroinformatics Group, Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, -College of Biomedical and Life Sciences, Cardiff, Wales, United Kingdom; 1st Department of Neurology, G.H. "AHEPA " School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki (AUTH), Thessaloniki, Greece; Greek Association of Alzheimer's Disease and Related Disorders, Thessaloniki, Makedonia, Greece; Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Cardiff, Wales, United Kingdom; Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, Wales, United Kingdom; School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Cardiff, Wales, United Kingdom; Neuroscience and Mental Health Research Institute, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, Wales, United Kingdom; MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, Wales, United Kingdom
| | - Zhijun Yao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China.
| | - Bin Hu
- 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; Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University & Institute of Semiconductors, Chinese Academy of Sciences, Lanzhou, China; Engineering Research Center of Open Source Software and Real-Time System (Lanzhou University), Ministry of Education, Lanzhou, China
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Zhu Q, Wang Y, Zhuo C, Xu Q, Yao Y, Liu Z, Li Y, Sun Z, Wang J, Lv M, Wu Q, Wang D. Classification of Alzheimer’s Disease Based on Abnormal Hippocampal Functional Connectivity and Machine Learning. Front Aging Neurosci 2022; 14:754334. [PMID: 35273489 PMCID: PMC8902140 DOI: 10.3389/fnagi.2022.754334] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Accepted: 01/12/2022] [Indexed: 01/29/2023] Open
Abstract
Objective Alzheimer’s disease (AD) is a neurodegenerative disease characterized by progressive deterioration of memory and cognition. Mild cognitive impairment (MCI) has been implicated as a prodromal phase of AD. Although abnormal functional connectivity (FC) has been demonstrated in AD and MCI, the clinical differentiation of AD, MCI, and normal aging remains difficult, and the distinction between MCI and normal aging is especially problematic. We hypothesized that FC between the hippocampus and other brain structures is altered in AD and MCI, and that measurement of abnormal FC could have diagnostic utility for the classification of different AD stages. Methods Elderly adults aged 60–85 years were assigned to AD, MCI, or normal control (NC) groups based on clinical criteria. Functional magnetic resonance scanning was completed by 119 subjects. Five dimension reduction/classification methods were applied, using hippocampus-derived FC strengths as input features. Classification performance of the five dimensionality reduction methods was compared between AD, MCI, and NC groups. Results FCs between the hippocampus and left insula, left thalamus, cerebellum, right lingual gyrus, posterior cingulate cortex, and precuneus were significantly reduced in AD and MCI. Support vector machine learning coupled with sparse principal component analysis demonstrated the best discriminative performance, yielding classification accuracies of 82.02% (AD vs. NC), 81.33% (MCI vs. NC), and 81.08% (AD vs. MCI). Conclusion Hippocampus-seed-based FCs were significantly different between AD, MCI, and NC groups. FC assessment combined with widely used machine learning methods can improve AD differential diagnosis, and may be especially useful to distinguish MCI from normal aging.
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Affiliation(s)
- Qixiao Zhu
- School of Information Science and Engineering, Shandong University, Qingdao, China
| | - Yonghui Wang
- Department of Physical Medicine and Rehabilitation, Qilu Hospital of Shandong University, Jinan, China
| | - Chuanjun Zhuo
- Key Laboratory of Real Time Brain Circuits Tracing (RTBNP_Lab), Tianjin Fourth Center Hospital, Tianjin Fourth Hospital Affiliated to Nankai University, Tianjin, China
- Department of Psychiatry, Tianjin Medical University, Tianjin, China
| | - Qunxing Xu
- Department of Health Management Center, Qilu Hospital of Shandong University, Jinan, China
| | - Yuan Yao
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, China
| | - Zhuyun Liu
- Department of Radiology, The Second People’s Hospital of Rizhao City, Rizhao, China
| | - Yi Li
- Department of Neurology, Qilu Hospital of Shangdong University, Jinan, China
| | - Zhao Sun
- Shandong Chenze AI Research Institute Co. Ltd., Jinan, China
| | - Jian Wang
- Shandong Key Laboratory of Brain Function Remodeling, Department of Neurosurgery, Qilu Hospital of Shandong University, Jinan, China
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, China
| | - Ming Lv
- Department of Clinical Epidemiology, Qilu Hospital of Shandong University, Jinan, China
- Department of Epidemiology and Health Statistics, School of Public Health, Shandong University, Jinan, China
| | - Qiang Wu
- School of Information Science and Engineering, Shandong University, Qingdao, China
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, China
- *Correspondence: Qiang Wu,
| | - Dawei Wang
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, China
- Department of Epidemiology and Health Statistics, School of Public Health, Shandong University, Jinan, China
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, China
- Dawei Wang,
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da Silveira Souza B, Poloni KM, Ferrari RJ. Detector of 3-D salient points based on the dual-tree complex wavelet transform for the positioning of hippocampi meshes in magnetic resonance images. J Neurosci Methods 2020; 341:108789. [PMID: 32512218 DOI: 10.1016/j.jneumeth.2020.108789] [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: 11/14/2019] [Revised: 05/15/2020] [Accepted: 05/15/2020] [Indexed: 10/24/2022]
Abstract
BACKGROUND Brain Magnetic Resonance (MR) image segmentation methods based on deformable models depend on the initial positioning to maximize the chances of successful segmentation. To minimize this limitation, salient-point based registration is used to perform the initial positioning of brain structure meshes close to their image target representation. The analysis of brain structures (such as the hippocampus) can help in the diagnosis and follow-up of neurodegenerative diseases like Alzheimer's. METHODS We present a technique for detection and description of 3-D salient points, which combines filter response maps estimated for different scales and orientations of the dual-tree complex wavelet transform (DT-CWT). We apply our technique to detect salient points in volumetric brain MR images and use the detected points in a positioning methodology. To illustrate the applicability, we applied our method for the positioning of hippocampi meshes in 3-D brain MR images and assessed the results by overlapping the positioned meshes with manual annotations made by medical specialists. RESULTS Our method yielded mean values of normalized Dice Similarity Coefficient (nDSC) of 0.74/0.68 and Hausdorff Average Distance (HAD) of 0.73/0.75 for the left and right hippocampus, respectively. COMPARISON WITH OTHER METHODS The mean nDSC and HAD results of our detector were significantly better than the ones achieved by an Affine and a Phase Congruency (PC) guided positioning. CONCLUSIONS The detection via DT-CWT decomposition is computationally less demanding than the detection via PC and represents a robust alternative for the positioning of mesh models.
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Affiliation(s)
- Breno da Silveira Souza
- Department of Computing, Federal University of São Carlos, Rod. Washington Luis, Km 235, 13565-905, São Carlos, SP, Brazil
| | - Katia M Poloni
- Department of Computing, Federal University of São Carlos, Rod. Washington Luis, Km 235, 13565-905, São Carlos, SP, Brazil
| | - Ricardo J Ferrari
- Department of Computing, Federal University of São Carlos, Rod. Washington Luis, Km 235, 13565-905, São Carlos, SP, Brazil.
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Liu M, Li F, Yan H, Wang K, Ma Y, Shen L, Xu M. A multi-model deep convolutional neural network for automatic hippocampus segmentation and classification in Alzheimer's disease. Neuroimage 2019; 208:116459. [PMID: 31837471 DOI: 10.1016/j.neuroimage.2019.116459] [Citation(s) in RCA: 183] [Impact Index Per Article: 36.6] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2018] [Revised: 12/09/2019] [Accepted: 12/10/2019] [Indexed: 01/22/2023] Open
Abstract
Alzheimer's disease (AD) is a progressive and irreversible brain degenerative disorder. Mild cognitive impairment (MCI) is a clinical precursor of AD. Although some treatments can delay its progression, no effective cures are available for AD. Accurate early-stage diagnosis of AD is vital for the prevention and intervention of the disease progression. Hippocampus is one of the first affected brain regions in AD. To help AD diagnosis, the shape and volume of the hippocampus are often measured using structural magnetic resonance imaging (MRI). However, these features encode limited information and may suffer from segmentation errors. Additionally, the extraction of these features is independent of the classification model, which could result in sub-optimal performance. In this study, we propose a multi-model deep learning framework based on convolutional neural network (CNN) for joint automatic hippocampal segmentation and AD classification using structural MRI data. Firstly, a multi-task deep CNN model is constructed for jointly learning hippocampal segmentation and disease classification. Then, we construct a 3D Densely Connected Convolutional Networks (3D DenseNet) to learn features of the 3D patches extracted based on the hippocampal segmentation results for the classification task. Finally, the learned features from the multi-task CNN and DenseNet models are combined to classify disease status. Our method is evaluated on the baseline T1-weighted structural MRI data collected from 97 AD, 233 MCI, 119 Normal Control (NC) subjects in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The proposed method achieves a dice similarity coefficient of 87.0% for hippocampal segmentation. In addition, the proposed method achieves an accuracy of 88.9% and an AUC (area under the ROC curve) of 92.5% for classifying AD vs. NC subjects, and an accuracy of 76.2% and an AUC of 77.5% for classifying MCI vs. NC subjects. Our empirical study also demonstrates that the proposed multi-model method outperforms the single-model methods and several other competing methods.
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Affiliation(s)
- Manhua Liu
- MoE Key Lab of Artificial Intelligence, Artificial Intelligence Institute, Shanghai Jiao Tong University, Shanghai, China; Department of Instrument Science and Engineering, School of EIEE, Shanghai Jiao Tong University, Shanghai, China.
| | - Fan Li
- Department of Instrument Science and Engineering, School of EIEE, Shanghai Jiao Tong University, Shanghai, China
| | - Hao Yan
- Department of Instrument Science and Engineering, School of EIEE, Shanghai Jiao Tong University, Shanghai, China
| | - Kundong Wang
- Department of Instrument Science and Engineering, School of EIEE, Shanghai Jiao Tong University, Shanghai, China
| | - Yixin Ma
- Department of Instrument Science and Engineering, School of EIEE, Shanghai Jiao Tong University, Shanghai, China
| | | | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Mingqing Xu
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Shanghai Jiao Tong University, Shanghai, China; Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
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7
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Cui R, Liu M. Hippocampus Analysis by Combination of 3-D DenseNet and Shapes for Alzheimer's Disease Diagnosis. IEEE J Biomed Health Inform 2019; 23:2099-2107. [DOI: 10.1109/jbhi.2018.2882392] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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8
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Xie L, Shinohara RT, Ittyerah R, Kuijf HJ, Pluta JB, Blom K, Kooistra M, Reijmer YD, Koek HL, Zwanenburg JJM, Wang H, Luijten PR, Geerlings MI, Das SR, Biessels GJ, Wolk DA, Yushkevich PA, Wisse LEM. Automated Multi-Atlas Segmentation of Hippocampal and Extrahippocampal Subregions in Alzheimer's Disease at 3T and 7T: What Atlas Composition Works Best? J Alzheimers Dis 2019; 63:217-225. [PMID: 29614654 DOI: 10.3233/jad-170932] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
BACKGROUND Multi-atlas segmentation, a popular technique implemented in the Automated Segmentation of Hippocampal Subfields (ASHS) software, utilizes multiple expert-labelled images ("atlases") to delineate medial temporal lobe substructures. This multi-atlas method is increasingly being employed in early Alzheimer's disease (AD) research, it is therefore becoming important to know how the construction of the atlas set in terms of proportions of controls and patients with mild cognitive impairment (MCI) and/or AD affects segmentation accuracy. OBJECTIVE To evaluate whether the proportion of controls in the training sets affects the segmentation accuracy of both controls and patients with MCI and/or early AD at 3T and 7T. METHODS We performed cross-validation experiments varying the proportion of control subjects in the training set, ranging from a patient-only to a control-only set. Segmentation accuracy of the test set was evaluated by the Dice similarity coeffiecient (DSC). A two-stage statistical analysis was applied to determine whether atlas composition is linked to segmentation accuracy in control subjects and patients, for 3T and 7T. RESULTS The different atlas compositions did not significantly affect segmentation accuracy at 3T and for patients at 7T. For controls at 7T, including more control subjects in the training set significantly improves the segmentation accuracy, but only marginally, with the maximum of 0.0003 DSC improvement per percent increment of control subject in the training set. CONCLUSION ASHS is robust in this study, and the results indicate that future studies investigating hippocampal subfields in early AD populations can be flexible in the selection of their atlas compositions.
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Affiliation(s)
- Long Xie
- Department of Radiology, Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia, PA, USA.,Department of Neurology, Penn Memory Center, University of Pennsylvania, Philadelphia, PA, USA
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Ranjit Ittyerah
- Department of Radiology, Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia, PA, USA
| | - Hugo J Kuijf
- Image Sciences Institute, UMC Utrecht, Utrecht, The Netherlands
| | - John B Pluta
- Department of Radiology, Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia, PA, USA.,Department of Neurology, Penn Memory Center, University of Pennsylvania, Philadelphia, PA, USA
| | - Kim Blom
- Julius Center for Health Sciences and Primary Care, UMC Utrecht, Utrecht, The Netherlands
| | - Minke Kooistra
- Julius Center for Health Sciences and Primary Care, UMC Utrecht, Utrecht, The Netherlands.,Department of Neurology, Brain Center Rudolf Magnus, UMC Utrecht, Utrecht, The Netherlands
| | - Yael D Reijmer
- Department of Neurology, Brain Center Rudolf Magnus, UMC Utrecht, Utrecht, The Netherlands
| | | | | | - Hongzhi Wang
- Almaden Research Center, IBM Research, Almaden, CA, USA
| | - Peter R Luijten
- Department of Radiology, UMC Utrecht, Utrecht, The Netherlands
| | - Mirjam I Geerlings
- Julius Center for Health Sciences and Primary Care, UMC Utrecht, Utrecht, The Netherlands
| | - Sandhitsu R Das
- Department of Radiology, Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia, PA, USA.,Department of Neurology, Penn Memory Center, University of Pennsylvania, Philadelphia, PA, USA
| | - Geert Jan Biessels
- Department of Neurology, Brain Center Rudolf Magnus, UMC Utrecht, Utrecht, The Netherlands
| | - David A Wolk
- Department of Neurology, Penn Memory Center, University of Pennsylvania, Philadelphia, PA, USA
| | - Paul A Yushkevich
- Department of Radiology, Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia, PA, USA
| | - Laura E M Wisse
- Department of Radiology, Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia, PA, USA.,Department of Neurology, Penn Memory Center, University of Pennsylvania, Philadelphia, PA, USA
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Abstract
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.
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10
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Xie L, Wisse LEM, Pluta J, de Flores R, Piskin V, Manjón JV, Wang H, Das SR, Ding S, Wolk DA, Yushkevich PA. Automated segmentation of medial temporal lobe subregions on in vivo T1-weighted MRI in early stages of Alzheimer's disease. Hum Brain Mapp 2019; 40:3431-3451. [PMID: 31034738 PMCID: PMC6697377 DOI: 10.1002/hbm.24607] [Citation(s) in RCA: 63] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2019] [Accepted: 04/15/2019] [Indexed: 12/14/2022] Open
Abstract
Medial temporal lobe (MTL) substructures are the earliest regions affected by neurofibrillary tangle pathology-and thus are promising biomarkers for Alzheimer's disease (AD). However, automatic segmentation of the MTL using only T1-weighted (T1w) magnetic resonance imaging (MRI) is challenging due to the large anatomical variability of the MTL cortex and the confound of the dura mater, which is commonly segmented as gray matter by state-of-the-art algorithms because they have similar intensity in T1w MRI. To address these challenges, we developed a novel atlas set, consisting of 15 cognitively normal older adults and 14 patients with mild cognitive impairment with a label explicitly assigned to the dura, that can be used by the multiatlas automated pipeline (Automatic Segmentation of Hippocampal Subfields [ASHS-T1]) for the segmentation of MTL subregions, including anterior/posterior hippocampus, entorhinal cortex (ERC), Brodmann areas (BA) 35 and 36, and parahippocampal cortex on T1w MRI. Cross-validation experiments indicated good segmentation accuracy of ASHS-T1 and that the dura can be reliably separated from the cortex (6.5% mislabeled as gray matter). Conversely, FreeSurfer segmented majority of the dura mater (62.4%) as gray matter and the degree of dura mislabeling decreased with increasing disease severity. To evaluate its clinical utility, we applied the pipeline to T1w images of 663 ADNI subjects and significant volume/thickness loss is observed in BA35, ERC, and posterior hippocampus in early prodromal AD and all subregions at later stages. As such, the publicly available new atlas and ASHS-T1 could have important utility in the early diagnosis and monitoring of AD and enhancing brain-behavior studies of these regions.
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Affiliation(s)
- Long Xie
- Penn Image Computing and Science Laboratory (PICSL), Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvania
- Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvania
| | - Laura E. M. Wisse
- Penn Image Computing and Science Laboratory (PICSL), Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvania
- Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvania
- Penn Memory CenterUniversity of PennsylvaniaPhiladelphiaPennsylvania
| | - John Pluta
- Penn Image Computing and Science Laboratory (PICSL), Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvania
- Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvania
| | - Robin de Flores
- Penn Memory CenterUniversity of PennsylvaniaPhiladelphiaPennsylvania
- Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvania
| | - Virgine Piskin
- Penn Image Computing and Science Laboratory (PICSL), Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvania
| | - Jose V. Manjón
- Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA)Universidad Politécnica de ValenciaValenciaSpain
| | | | - Sandhitsu R. Das
- Penn Image Computing and Science Laboratory (PICSL), Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvania
- Penn Memory CenterUniversity of PennsylvaniaPhiladelphiaPennsylvania
- Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvania
| | - Song‐Lin Ding
- Allen Institute for Brain ScienceSeattleWashington
- Institute of Neuroscience, School of Basic Medical SciencesGuangzhou Medical UniversityGuangzhouPeople's Republic of China
| | - David A. Wolk
- Penn Memory CenterUniversity of PennsylvaniaPhiladelphiaPennsylvania
- Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvania
| | - Paul A. Yushkevich
- Penn Image Computing and Science Laboratory (PICSL), Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvania
- Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvania
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Li F, Liu M. A hybrid Convolutional and Recurrent Neural Network for Hippocampus Analysis in Alzheimer's Disease. J Neurosci Methods 2019; 323:108-118. [DOI: 10.1016/j.jneumeth.2019.05.006] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Revised: 04/22/2019] [Accepted: 05/15/2019] [Indexed: 01/29/2023]
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12
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Dill V, Klein PC, Franco AR, Pinho MS. Atlas selection for hippocampus segmentation: Relevance evaluation of three meta-information parameters. Comput Biol Med 2018; 95:90-98. [DOI: 10.1016/j.compbiomed.2018.02.005] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2017] [Revised: 02/07/2018] [Accepted: 02/08/2018] [Indexed: 10/18/2022]
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13
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Schmidt MF, Storrs JM, Freeman KB, Jack CR, Turner ST, Griswold ME, Mosley TH. A comparison of manual tracing and FreeSurfer for estimating hippocampal volume over the adult lifespan. Hum Brain Mapp 2018; 39:2500-2513. [PMID: 29468773 DOI: 10.1002/hbm.24017] [Citation(s) in RCA: 66] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2017] [Revised: 02/11/2018] [Accepted: 02/13/2018] [Indexed: 11/08/2022] Open
Abstract
MRI has become an indispensable tool for brain volumetric studies, with the hippocampus an important region of interest. Automation of the MRI segmentation process has helped advance the field by facilitating the volumetric analysis of larger cohorts and more studies. FreeSurfer has emerged as the de facto standard tool for these analyses, but studies validating its output are all based on older versions. To characterize FreeSurfer's validity, we compare several versions of FreeSurfer software with traditional hand-tracing. Using MRI images of 262 males and 402 females aged 38 to 84, we directly compare estimates of hippocampal volume from multiple versions of FreeSurfer, its hippocampal subfield routines, and our manual tracing protocol. We then use those estimates to assess asymmetry and atrophy, comparing performance of different estimators with each other and with brain atrophy measures. FreeSurfer consistently reports larger volumes than manual tracing. This difference is smaller in larger hippocampi or older people, with these biases weaker in version 6.0.0 than prior versions. All methods tested agree qualitatively on rightward asymmetry and increasing atrophy in older people. FreeSurfer saves time and money, and approximates the same atrophy measures as manual tracing, but it introduces biases that could require statistical adjustments in some studies.
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Affiliation(s)
- Mike F Schmidt
- Program in Neuroscience, University of Mississippi Medical Center, Jackson, Mississippi.,Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi
| | - Judd M Storrs
- Department of Radiology, University of Mississippi Medical Center, Jackson, Mississippi
| | - Kevin B Freeman
- Department of Psychiatry and Human Behavior, University of Mississippi Medical Center, Jackson, Mississippi
| | | | - Stephen T Turner
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota
| | - Michael E Griswold
- Department of Data Science, University of Mississippi Medical Center, Jackson, Mississippi
| | - Thomas H Mosley
- Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi
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Alzheimer's disease diagnosis based on the Hippocampal Unified Multi-Atlas Network (HUMAN) algorithm. Biomed Eng Online 2018; 17:6. [PMID: 29357893 PMCID: PMC5778685 DOI: 10.1186/s12938-018-0439-y] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2017] [Accepted: 01/10/2018] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Hippocampal atrophy is a supportive feature for the diagnosis of probable Alzheimer's disease (AD). However, even for an expert neuroradiologist, tracing the hippocampus and measuring its volume is a time consuming and extremely challenging task. Accordingly, the development of reliable fully-automated segmentation algorithms is of paramount importance. MATERIALS AND METHODS The present study evaluates (i) the precision and the robustness of the novel Hippocampal Unified Multi-Atlas Network (HUMAN) segmentation algorithm and (ii) its clinical reliability for AD diagnosis. For these purposes, we used a mixed cohort of 456 subjects and their T1 weighted magnetic resonance imaging (MRI) brain scans. The cohort included 145 controls (CTRL), 217 mild cognitive impairment (MCI) subjects and 94 AD patients from Alzheimer's Disease Neuroimaging Initiative (ADNI). For each subject the baseline, repeat, 12 and 24 month follow-up scans were available. RESULTS HUMAN provides hippocampal volumes with a 3% precision; volume measurements effectively reveal AD, with an area under the curve (AUC) AUC1 = 0.08 ± 0.02. Segmented volumes can also reveal the subtler effects present in MCI subjects, AUC2 = 0.76 ± 0.05. The algorithm is stable and reproducible over time, even for 24 month follow-up scans. CONCLUSIONS The experimental results demonstrate HUMAN is a precise segmentation algorithm, besides hippocampal volumes, provided by HUMAN, can effectively support the diagnosis of Alzheimer's disease and become a useful tool for other neuroimaging applications.
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Donnelly-Kehoe PA, Pascariello GO, Gómez JC. Looking for Alzheimer's Disease morphometric signatures using machine learning techniques. J Neurosci Methods 2017; 302:24-34. [PMID: 29174020 DOI: 10.1016/j.jneumeth.2017.11.013] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2017] [Revised: 11/17/2017] [Accepted: 11/19/2017] [Indexed: 02/08/2023]
Abstract
BACKGROUND We present our results in the International challenge for automated prediction of MCI from MRI data. We evaluate the performance of MRI-based neuromorphometrics features (nMF) in the classification of Healthy Controls (HC), Mild Cognitive Impairment (MCI), converters MCI (cMCI) and Alzheimer's Disease (AD) patients. NEW METHODS We propose to segregate participants in three groups according to Mini Mental State Examination score (MMSEs), searching for the main nMF in each group. Then we use them to develop a Multi Classifier System (MCS). We compare the MCS against a single classifier scheme using both MMSEs+nMF and nMF only. We repeat this comparison using three state-of-the-art classification algorithms. RESULTS The MCS showed the best performance on both Accuracy and Area Under the Receiver Operating Curve (AUC) in comparison with single classifiers. The multiclass AUC for the MCS classification on Test Dataset were 0.83 for HC, 0.76 for cMCI, 0.65 for MCI and 0.95 for AD. Furthermore, MCS's optimum accuracy on Neurodegenerative Disease (ND) detection (AD+cMCI vs MCI+HC) was 81.0% (AUC=0.88), while the single classifiers got 71.3% (AUC=0.86) and 63.1% (AUC=0.79) for MMSEs+nMF and only nMF respectively. COMPARISON WITH EXISTING METHOD The proposed MCS showed a better performance than using all nMF into a single state-of-the-art classifier. CONCLUSIONS These findings suggest that using cognitive scoring, e.g. MMSEs, in the design of a Multi Classifier System improves performance by allowing a better selection of MRI-based features.
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Affiliation(s)
- Patricio Andres Donnelly-Kehoe
- Multimedia Signal Processing Group - Neuroimage Division, French-Argentine International Center for Information and Systems Sciences (CIFASIS) - National Scientific and Technical Research Council (CONICET), 27 de Febrero 210 bis, Rosario, Argentina.
| | - Guido Orlando Pascariello
- Multimedia Signal Processing Group - Neuroimage Division, French-Argentine International Center for Information and Systems Sciences (CIFASIS) - National Scientific and Technical Research Council (CONICET), 27 de Febrero 210 bis, Rosario, Argentina
| | - Juan Carlos Gómez
- Multimedia Signal Processing Group - Neuroimage Division, French-Argentine International Center for Information and Systems Sciences (CIFASIS) - National Scientific and Technical Research Council (CONICET), 27 de Febrero 210 bis, Rosario, Argentina
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- Multimedia Signal Processing Group - Neuroimage Division, French-Argentine International Center for Information and Systems Sciences (CIFASIS) - National Scientific and Technical Research Council (CONICET), 27 de Febrero 210 bis, Rosario, Argentina
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