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Hippocampus segmentation in magnetic resonance images of Alzheimer's patients using Deep machine learning [article]

Hadi Varmazyar, Hossein Yousefi-Banaem, Saber Malekzadeh, Nahideh Gharehaghaji
2021 arXiv   pre-print
Objective: The aim of this study was the segmentation of the hippocampus in magnetic resonance (MR) images of Alzheimers patients using deep machine learning method.  ...  Methods: U-Net architecture of convolutional neural network was proposed to segment the hippocampus in the real MRI data.  ...  Acknowledgment This study was carried out as an MSc thesis and financially supported by Molecular Medicine Research Center, Tabriz University of Medical Sciences (Grant number: IR.TBZMED.REC.1397.562  ... 
arXiv:2106.06743v1 fatcat:mynvqcyusjgvzlsvt6j4boy2ym

Semantic Segmentation of Hippocampal Subregions With U-Net Architecture

Soraya Nasser, Moulkheir Naoui, Ghalem Belalem, Saïd Mahmoudi
2021 International Journal of E-Health and Medical Communications (IJEHMC)  
The Automatic semantic segmentation of the hippocampus is an important area of research in which several convolutional neural networks (CNN) models have been used to detect the hippocampus from whole cerebral  ...  MRI.  ...  In addition the training data for this article is not used in machine learning 2009 ) for a manual segmentation, and in Yushkevich et al. (2015) who used a multi-atlas segmentation with joint label  ... 
doi:10.4018/ijehmc.20211101.oa4 fatcat:nbtwfq2e7bhmtov3st2urpdgy4

Automatic Localization and Discrete Volume Measurements of Hippocampi from MRI Data Using a Convolutional Neural Network

Abol Basher, Byeong C. Kim, Kun Ho Lee, Ho Yub Jung
2020 IEEE Access  
For more information, see https://creativecommons.org/licenses/by/4.0/ 91725 A. Basher et al.: Automatic Localization and Discrete Volume Measurements of Hippocampi From MRI Data FIGURE 1 .  ...  Slicer counts the number of pixels/voxels attributed in each slice for the axial, coronal and sagittal views.  ...  Because of the increasing resolution of MRI scans, automatic segmentation of the hippocampus with its subfields becomes possible.  ... 
doi:10.1109/access.2020.2994388 fatcat:lzts7gh7mnblzn27lxzz4flw2y

MRI Segmentation and Classification of Human Brain Using Deep Learning for Diagnosis of Alzheimer's Disease: A Survey

Nagaraj Yamanakkanavar, Jae Young Choi, Bumshik Lee
2020 Sensors  
We aim to provide an outline of current deep learning-based segmentation approaches for the quantitative analysis of brain MRI for the diagnosis of AD.  ...  Hence, deep learning methods are now preferred over state-of-the-art machine learning methods.  ...  Recently, deep learning approaches showed better performance for the automatic segmentation of the hippocampus and classification of AD.  ... 
doi:10.3390/s20113243 pmid:32517304 fatcat:bfd5dffy4vbktnsoroi5o2je2a

Coronal slice segmentation using a watershed method for early identification of people with Alzheimer's

Retno Supriyanti, Anugerah Kevin Marchel, Yogi Ramadhani, Haris Budi Widodo
2021 TELKOMNIKA (Telecommunication Computing Electronics and Control)  
We will use watershed method segmentation, because of this method able to segment the boundaries automatically, so that parts of the hippocampus and ventricles can be identified in an MRI image.  ...  However, for low-quality MRI, this is difficult to do.  ...  ACKNOWLEDGEMENTS We Thank the Open Access Series of Imaging Studies (OASIS) datasets and in the associated PubMed Central submission: P50 AG05681, P01 AG03991, R01 AG021910, P50 MH071616, U24 RR021382,  ... 
doi:10.12928/telkomnika.v19i1.15142 fatcat:vx2w4h6nujfapp7nvpxqedaolq

Combined Atlas and Convolutional Neural Network-Based Segmentation of the Hippocampus from MRI According to the ADNI Harmonized Protocol

Samaneh Nobakht, Morgan Schaeffer, Nils Forkert, Sean Nestor, Sandra E. Black, Philip Barber, the Alzheimer's Disease Neuroimaging Initiative
2021 Sensors  
The aim of this work was to develop and evaluate an automatic segmentation tool (DeepHarp) for hippocampus delineation according to the ADNI harmonized hippocampal protocol (HarP).  ...  In conclusion, DeepHarp can automatically segment the hippocampus from T1-weighted MRI datasets according to the ADNI-HarP protocol with high accuracy and robustness, which can aid atrophy measurements  ...  ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s21072427 pmid:33915960 fatcat:hccky24mpndbjmkyd6vwiq5qmu

Segmentation algorithms of subcortical brain structures on MRI for radiotherapy and radiosurgery: A survey

J. Dolz, L. Massoptier, M. Vermandel
2015 IRBM  
More recently, the introduction of machine learning techniques, such as artificial neural networks or support vector machines, has helped the researchers to optimize the classification problem.  ...  Consequently, the development of segmentation algorithms that can deal with such lesions in the brain and still provide a good performance when segmenting subcortical structures is highly required in practice  ...  Machine learning methods Machine Learning (ML) techniques have been extensively used in the MRI analysis domain almost since its creation.  ... 
doi:10.1016/j.irbm.2015.06.001 fatcat:j2y2az4kgjbtdmm3roqutaytvy

A Semi-Automated Pipeline for the Segmentation of Rhesus Macaque Hippocampus: Validation across a Wide Age Range

Michael R. Hunsaker, David G. Amaral, Noam Harel
2014 PLoS ONE  
Any systematic errors in the normalization process are corrected using a machine-learning algorithm that has been trained by comparing manual and automated segmentations to identify systematic errors.  ...  This report outlines a neuroimaging pipeline that allows a robust, high-throughput, semi-automated, template-based protocol for segmenting the hippocampus in rhesus macaque (Macaca mulatta) monkeys ranging  ...  Goodrich-Hunsaker, Ph.D. for helpful conversations concerning the methods used in the present experiment. Author Contributions Conceived and designed the experiments: MRH DGA.  ... 
doi:10.1371/journal.pone.0089456 pmid:24586791 pmcid:PMC3933562 fatcat:by5u3y7cenh2hlj7s7q6xs6pqa

Front Matter: Volume 10137

2017 Medical Imaging 2017: Biomedical Applications in Molecular, Structural, and Functional Imaging  
Publication of record for individual papers is online in the SPIE Digital Library. SPIEDigitalLibrary.org Paper Numbering: Proceedings of SPIE follow an e-First publication model.  ...  Utilization of CIDs allows articles to be fully citable as soon as they are published online, and connects the same identifier to all online and print versions of the publication.  ...  1P Automatic segmentation of vessels in in-vivo ultrasound scans [10137-60] 10137 1Q Computerized method to compensate for breathing body motion in dynamic chest radiographs [10137-61] vi Proc  ... 
doi:10.1117/12.2277895 dblp:conf/mibam/X17 fatcat:hq5s7pahirdilkfy4pzali4fe4

Machine Learning Applications on Neuroimaging for Diagnosis and Prognosis of Epilepsy: A Review [article]

Jie Yuan, Xuming Ran, Keyin Liu, Chen Yao, Yi Yao, Haiyan Wu, Quanying Liu
2021 arXiv   pre-print
Machine learning is playing an increasingly important role in medical image analysis, spawning new advances in the clinical application of neuroimaging.  ...  In this review, we emphasize the interaction between neuroimaging and machine learning in the context of epilepsy diagnosis and prognosis.  ...  For instance, Kim et al. manipulated linear discrimination analysis (LDA) to classify the left-or right-sided seizure focus with manually extracted features after the segmentation of hippocampus in MRI  ... 
arXiv:2102.03336v3 fatcat:mryusowfbjfjldmx46zcwu6dja

Hippocampus Localization Using a Two-Stage Ensemble Hough Convolutional Neural Network

Abol Basher, Kyu Yeong Choi, Jang Jae Lee, Bumshik Lee, Byeong C. Kim, Kun Ho Lee, Ho Yub Jung
2019 IEEE Access  
Furthermore, for segmentation and registration of anatomical structures, exact localization is desired.  ...  In this paper, we present a two-stage ensemble-based approach to localize the anatomical structure of interest from magnetic resonance imaging (MRI) scans.  ...  Deep learning-based detection strategies have achieved many breakthroughs in different disciplines using computer vision and machine learning-based algorithms [13] , [24] , [42] . Gall et al.  ... 
doi:10.1109/access.2019.2920005 fatcat:ilwyc3h34be7vp3zee4rgagkou

Diagnostic Classification and Biomarker Identification of Alzheimer's Disease with Random Forest Algorithm

Minseok Song, Hyeyoom Jung, Seungyong Lee, Donghyeon Kim, Minkyu Ahn
2021 Brain Sciences  
In this study, we tested RF and various machine learning models with regional volumes from 2250 brain MRIs: 687 normal controls (NC), 1094 mild cognitive impairment (MCI), and 469 AD that ADNI (Alzheimer's  ...  Despite these benefits, RF is not used actively to predict Alzheimer's disease (AD) with brain MRIs.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/brainsci11040453 pmid:33918453 fatcat:d5h4vybao5aq5gz5f5kfdktc4a

Fusion of Multi-Size Candidate Regions Enhances Two-stage Hippocampus Segmentation

Ping Cao, Qiuyang Sheng, Siqi Fang, Xinyi Li, Gangmin Ning, Qing Pan
2020 IEEE Access  
Conventional automatic segmentation methods poorly achieve satisfactory performance because of the irregular shape and small volume of the hippocampus.  ...  The experimental results achieved Dice similarity coefficients of 92.48 ± 0.61% and 92.90 ± 0.51% for the left and right hippocampus, respectively, outperforming state-of-the-art studies in hippocampus  ...  Automatic segmentation shall bring substantial gains to this field and is thereby highly necessary. The hippocampus occupies a small volume on the MRI scans.  ... 
doi:10.1109/access.2020.2984661 fatcat:l3iqjb42r5b4djjf3jv4a556gi

Machine Learning-Based Framework for Differential Diagnosis Between Vascular Dementia and Alzheimer's Disease Using Structural MRI Features

Yineng Zheng, Haoming Guo, Lijuan Zhang, Jiahui Wu, Qi Li, Fajin Lv
2019 Frontiers in Neurology  
Conclusions: The proposed computer-aided diagnosis method highlights the potential of combining structural MRI and machine learning to support clinical decision making in distinction of VaD vs. AD.  ...  This study aims to assess whether multi-parameter features derived from structural MRI can serve as the informative biomarker for differential diagnosis between VaD and AD using machine learning.  ...  The project was funded by the National Natural Science Foundation of China (Grant Nos. 31800823 and 31570003) and the Incubation Foundation of the First Affiliated Hospital of Chongqing Medical University  ... 
doi:10.3389/fneur.2019.01097 pmid:31708854 pmcid:PMC6823227 fatcat:re5ufp3uxfhqrkg6mqvca4uaou

Combining residual attention mechanisms and generative adversarial networks for hippocampus segmentation

Hongxia Deng, Yuefang Zhang, Ran Li, Chunxiang Hu, Zijian Feng, Haifang Li
2022 Tsinghua Science and Technology  
Results showed that the network model could achieve an efficient automatic segmentation of the hippocampus and thus has practical relevance for the correct diagnosis of diseases, such as Alzheimer's disease  ...  This research discussed a deep learning method based on an improved generative adversarial network to segment the hippocampus.  ...  Haifang Li recived the PhD degree from Taiyuan University of Technology in 2009.  ... 
doi:10.26599/tst.2020.9010056 fatcat:4ivsu3us7rfgvfebid2ufa2g6a
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