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Hippocampus Segmentation on Epilepsy and Alzheimer's Disease Studies with Multiple Convolutional Neural Networks
[article]
2020
arXiv
pre-print
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Current state-of-the art methods train their methods on healthy or Alzheimer's disease patients from public datasets. ...
Hippocampus segmentation on magnetic resonance imaging (MRI) is of key importance for the diagnosis, treatment decision and investigation of neuropsychiatric disorders. ...
Data This study uses mainly two different datasets: one collected locally for an Epilepsy study, named HCUnicamp; and one public from the ADNI Alzheimer's study, HarP. ...
arXiv:2001.05058v1
fatcat:t4obo5dx6rhbplxduajrhvnh3i
Hippocampus segmentation on epilepsy and Alzheimer's disease studies with multiple convolutional neural networks
2021
Heliyon
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Most current state-of-the art hippocampus segmentation methods train their methods on healthy or Alzheimer's disease patients from public datasets. ...
The methodology was developed and validated using HarP, a public Alzheimer's disease hippocampus segmentation dataset. ...
Acknowledgements Alzheimer's disease data collection and sharing for this project was provided by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904 ...
doi:10.1016/j.heliyon.2021.e06226
pmid:33659748
pmcid:PMC7892928
fatcat:6cnel4fljbbfblvybpy4rg75me
Extended 2D Consensus Hippocampus Segmentation
[article]
2020
arXiv
pre-print
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Hippocampus segmentation plays a key role in diagnosing various brain disorders such as Alzheimer's disease, epilepsy, multiple sclerosis, cancer, depression and others. ...
A method for volumetric hippocampus segmentation is presented, based on the consensus of tri-planar U-Net inspired fully convolutional networks (FCNNs), with some modifications, including residual connections ...
The hippocampus book. Oxford University Press, 2007. ...
arXiv:1902.04487v5
fatcat:5lsyqrlulvckja2rn2tje2d7lm
Diagnosis of Alzheimer's Diseases from MRI Images using Image Processing and Machine Learning Approach
2021
International Journal of Computer Applications
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Alzheimer disease is an incurable, progressive neurological brain disorder. Earlier detection of Alzheimer's disease can help with proper treatment and prevent brain tissue damage. ...
First phase of workemphasized program specific applications to extract features.In the second phase the CNN multiple layers which are studied from lower level to the higher-level image characteristics. ...
Feature extraction and image classification Convolution neural network learning on multiple layers. Every layer has convolution and pooling method. ...
doi:10.5120/ijca2021921641
fatcat:rqli2nsuqfeinai3jwggnwobyy
Multi-view Learning for Benign Epilepsy with CentroTemporal Spikes
2018
IET Computer Vision
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Benign epilepsy with centrotemporal spikes (BECT) may be the most popular epilepsy to attack children. ...
The final result is obtained by passing through those predictions after a fusing neural network. ...
Another study related to Alzheimer's disease classification [42] uses a fully connected neural network to learn multiple features from MRI data and shows superior performance of the neural networks compared ...
doi:10.1049/iet-cvi.2018.5162
fatcat:fwobnoh4hnf6heysoamnbgzvsu
Hippocampal Segmentation in Brain MRI Images Using Machine Learning Methods: A Survey
2021
Chinese journal of electronics
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The hippocampus is closely related to many brain diseases, such as Alzheimer's disease. ...
Therefore, accurate segmentation of the hippocampus is of vital significance for the indepth study of many brain diseases. ...
Other researchers have used multiple networks to segment the hippocampus. Chen et al. [91] proposed a model composed of two fully convolutional neural networks. ...
doi:10.1049/cje.2021.06.002
fatcat:huj7qx4ajzghpj7jhwh7wzztoi
Combined Atlas and Convolutional Neural Network-Based Segmentation of the Hippocampus from MRI According to the ADNI Harmonized Protocol
2021
Sensors
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In the second step, a convolutional neural network trained using datasets with corresponding manual hippocampus annotations is used to segment the hippocampus from the cropped ROI. ...
The proposed method was developed and validated using 107 datasets with manually segmented hippocampi according to the ADNI-HarP standard as well as 114 multi-center datasets of patients with Alzheimer's ...
and patients with epilepsy (n = 50) [11] . ...
doi:10.3390/s21072427
pmid:33915960
fatcat:hccky24mpndbjmkyd6vwiq5qmu
Semantic Segmentation of Hippocampal Subregions With U-Net Architecture
2021
International Journal of E-Health and Medical Communications (IJEHMC)
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In this paper we present two convolutional neural networks the first network ( Hippocampus Segmentation Single Entity HSSE) segmented the hippocampus as a single entity and the second used to detect the ...
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 ...
The hippocampus is one of the first structures affected in Alzheimer's disease (Bobinski et al., 1999) , epilepsy and schizophrenia (Koolschijn et al., 2010) . ...
doi:10.4018/ijehmc.20211101.oa4
fatcat:nbtwfq2e7bhmtov3st2urpdgy4
Automatic Localization and Discrete Volume Measurements of Hippocampi from MRI Data Using a Convolutional Neural Network
2020
IEEE Access
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Three-channel 2-D patches with their corresponding segmented labels are generated for the volume measurement. ...
Similarly, from segmented label MRI scans, using the same voxel location and corresponding 3-D patches (size: 64 × 64 × 64) were generated. ...
CONVOLUTIONAL NEURAL NETWORK The proposed convolutional neural network (CNN) models [47] , [48] consist of a number of layers that conduct operations on the input data (I size ). ...
doi:10.1109/access.2020.2994388
fatcat:lzts7gh7mnblzn27lxzz4flw2y
MULTIMODAL 2.5D CONVOLUTIONAL NEURAL NETWORK FOR DIAGNOSIS OF ALZHEIMER'S DISEASE WITH MAGNETIC RESONANCE IMAGING AND POSITRON EMISSION TOMOGRAPHY
2021
Electromagnetic Waves
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To avoid the computational complexity of the 3D image and expand training samples, this study designed an AD diagnosis framework based on a 2.5D convolutional neural network (CNN) to fuse multimodal data ...
First, MRI and PET were preprocessed with skull stripping and registration. After that, multiple 2.5D patches were extracted within the hippocampus regions from both MRI and PET. ...
Multimodal 2.5D Convolutional Neural Network The multimodal 2.5D convolutional neural network (CNN) built in this study is shown in Figure 5 . ...
doi:10.2528/pier21051102
fatcat:mllmvlrturdfjawqpdpewbso44
Volumetric Feature-Based Alzheimer's Disease Diagnosis From sMRI Data Using a Convolutional Neural Network and a Deep Neural Network
2021
IEEE Access
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The proposed method is an aggregation of a convolutional neural network (CNN) model with a deep neural network (DNN) model. ...
Moreover, hippocampal volume atrophy is known to be linked with Alzheimer's disease. ...
Convolutional neural network (CNN) is widely used in the research communities for image-based problem solving. ...
doi:10.1109/access.2021.3059658
fatcat:ywkbw3fbhjen5mdvtxkmrrxhli
Comparative Evaluation Of Three Methods Of Automatic Segmentation Of Brain Structures Using 426 Cases
[article]
2020
arXiv
pre-print
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Four evaluation measures are used to assess agreement between automatic and manual segmentation of the hippocampus. ...
Among brain structures, the hippocampus presents a challenging segmentation task due to its irregular shape, small size, and unclear edges. ...
CONCLUSION Manual segmentation of the hippocampus remains the gold standard for its accuracy in undertaking any metrics related to aging or disease. It is, however, a very time-consuming process. ...
arXiv:2008.03387v1
fatcat:4ey4fdziv5dmhptgcb4gk5qco4
Brain Morphometry Estimation: From Hours to Seconds Using Deep Learning
2020
Frontiers in Neurology
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Materials and Methods: An anonymized dataset of 574 subjects (443 healthy controls and 131 patients with epilepsy) was used for the supervised training of a convolutional neural network (CNN). ...
A statistical test to dichotomize patients with epilepsy from healthy controls revealed similar effect sizes for structures affecting all subtypes as reported in a large-scale epilepsy study. ...
Calculations were partially performed on UBELIX, the HPC cluster at the University of Bern. ...
doi:10.3389/fneur.2020.00244
pmid:32322235
pmcid:PMC7156625
fatcat:x2d4ouztqbdjhp3pztlr3epppi
2020 Index IEEE Journal of Biomedical and Health Informatics Vol. 24
2020
IEEE journal of biomedical and health informatics
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., and Inan, O.T., A Globalized Model for Mapping Wearable Seismocardiogram Signals to Whole-Body Ballistocardiogram Signals Based on Deep Learning; JBHI May 2020 1296-1309 Herskovic, V., see Saint-Pierre ...
Rubin, D. 2570-2579 Jiang, D., see 2473-2480 Jiang, H., see 2798-2805 Jiang, H., Yang, M., Chen, X., Li, M., Li, Y., and Wang, J., miRTMC: A miRNA Target Prediction Method Based on Matrix Completion ...
., +, JBHI Feb. 2020 387-395 Epilepsy Seizure Prediction on EEG Using Common Spatial Pattern and Convolutional Neural Network. ...
doi:10.1109/jbhi.2020.3048808
fatcat:iifrkwtzazdmboabdqii7x5ukm
The Effect of Deep Learning-Based QSM Magnetic Resonance Imaging on the Subthalamic Nucleus
2021
Journal of Healthcare Engineering
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A 2.5D Attention U-Net Network based on multiple input and multiple output, a method for segmenting RN, SN, and STN regions in high-resolution QSM images is proposed, and deep learning realizes accurate ...
In order to study the influence of quantitative magnetic susceptibility mapping (QSM) on them. ...
Authors' Contributions Yuanqin Liu and Qinglu Zhang contributed equally to this work and should be considered co-first authors. ...
doi:10.1155/2021/8554182
pmid:34567489
pmcid:PMC8457984
fatcat:upybfzzeljc4vhunivbzwswvye
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