Automatic segmentation of white matter hyperintensities and correlation analysis for cerebral small vessel disease
Cerebral white matter hyperintensity can lead to cerebral small vessel disease, MRI images in the brain are used to assess the degree of pathological changes in white matter regions. In this paper, we propose a framework for automatic 3D segmentation of brain white matter hyperintensity based on MRI...
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| Vydané v: | Frontiers in neurology Ročník 14; s. 1242685 |
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Switzerland
Frontiers Media S.A
27.07.2023
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| Abstract | Cerebral white matter hyperintensity can lead to cerebral small vessel disease, MRI images in the brain are used to assess the degree of pathological changes in white matter regions. In this paper, we propose a framework for automatic 3D segmentation of brain white matter hyperintensity based on MRI images to address the problems of low accuracy and segmentation inhomogeneity in 3D segmentation. We explored correlation analyses of cognitive assessment parameters and multiple comparison analyses to investigate differences in brain white matter hyperintensity volume among three cognitive states, Dementia, MCI and NCI. The study explored the correlation between cognitive assessment coefficients and brain white matter hyperintensity volume.
This paper proposes an automatic 3D segmentation framework for white matter hyperintensity using a deep multi-mapping encoder-decoder structure. The method introduces a 3D residual mapping structure for the encoder and decoder. Multi-layer Cross-connected Residual Mapping Module (MCRCM) is proposed in the encoding stage to enhance the expressiveness of model and perception of detailed features. Spatial Attention Weighted Enhanced Supervision Module (SAWESM) is proposed in the decoding stage to adjust the supervision strategy through a spatial attention weighting mechanism. This helps guide the decoder to perform feature reconstruction and detail recovery more effectively.
Experimental data was obtained from a privately owned independent brain white matter dataset. The results of the automatic 3D segmentation framework showed a higher segmentation accuracy compared to nnunet and nnunet-resnet, with a
-value of <0.001 for the two cognitive assessment parameters MMSE and MoCA. This indicates that larger brain white matter are associated with lower scores of MMSE and MoCA, which in turn indicates poorer cognitive function. The order of volume size of white matter hyperintensity in the three groups of cognitive states is dementia, MCI and NCI, respectively.
The paper proposes an automatic 3D segmentation framework for brain white matter that achieves high-precision segmentation. The experimental results show that larger volumes of segmented regions have a negative correlation with lower scoring coefficients of MMSE and MoCA. This correlation analysis provides promising treatment prospects for the treatment of cerebral small vessel diseases in the brain through 3D segmentation analysis of brain white matter. The differences in the volume of white matter hyperintensity regions in subjects with three different cognitive states can help to better understand the mechanism of cognitive decline in clinical research. |
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| AbstractList | Cerebral white matter hyperintensity can lead to cerebral small vessel disease, MRI images in the brain are used to assess the degree of pathological changes in white matter regions. In this paper, we propose a framework for automatic 3D segmentation of brain white matter hyperintensity based on MRI images to address the problems of low accuracy and segmentation inhomogeneity in 3D segmentation. We explored correlation analyses of cognitive assessment parameters and multiple comparison analyses to investigate differences in brain white matter hyperintensity volume among three cognitive states, Dementia, MCI and NCI. The study explored the correlation between cognitive assessment coefficients and brain white matter hyperintensity volume.
This paper proposes an automatic 3D segmentation framework for white matter hyperintensity using a deep multi-mapping encoder-decoder structure. The method introduces a 3D residual mapping structure for the encoder and decoder. Multi-layer Cross-connected Residual Mapping Module (MCRCM) is proposed in the encoding stage to enhance the expressiveness of model and perception of detailed features. Spatial Attention Weighted Enhanced Supervision Module (SAWESM) is proposed in the decoding stage to adjust the supervision strategy through a spatial attention weighting mechanism. This helps guide the decoder to perform feature reconstruction and detail recovery more effectively.
Experimental data was obtained from a privately owned independent brain white matter dataset. The results of the automatic 3D segmentation framework showed a higher segmentation accuracy compared to nnunet and nnunet-resnet, with a
-value of <0.001 for the two cognitive assessment parameters MMSE and MoCA. This indicates that larger brain white matter are associated with lower scores of MMSE and MoCA, which in turn indicates poorer cognitive function. The order of volume size of white matter hyperintensity in the three groups of cognitive states is dementia, MCI and NCI, respectively.
The paper proposes an automatic 3D segmentation framework for brain white matter that achieves high-precision segmentation. The experimental results show that larger volumes of segmented regions have a negative correlation with lower scoring coefficients of MMSE and MoCA. This correlation analysis provides promising treatment prospects for the treatment of cerebral small vessel diseases in the brain through 3D segmentation analysis of brain white matter. The differences in the volume of white matter hyperintensity regions in subjects with three different cognitive states can help to better understand the mechanism of cognitive decline in clinical research. Cerebral white matter hyperintensity can lead to cerebral small vessel disease, MRI images in the brain are used to assess the degree of pathological changes in white matter regions. In this paper, we propose a framework for automatic 3D segmentation of brain white matter hyperintensity based on MRI images to address the problems of low accuracy and segmentation inhomogeneity in 3D segmentation. We explored correlation analyses of cognitive assessment parameters and multiple comparison analyses to investigate differences in brain white matter hyperintensity volume among three cognitive states, Dementia, MCI and NCI. The study explored the correlation between cognitive assessment coefficients and brain white matter hyperintensity volume.ObjectiveCerebral white matter hyperintensity can lead to cerebral small vessel disease, MRI images in the brain are used to assess the degree of pathological changes in white matter regions. In this paper, we propose a framework for automatic 3D segmentation of brain white matter hyperintensity based on MRI images to address the problems of low accuracy and segmentation inhomogeneity in 3D segmentation. We explored correlation analyses of cognitive assessment parameters and multiple comparison analyses to investigate differences in brain white matter hyperintensity volume among three cognitive states, Dementia, MCI and NCI. The study explored the correlation between cognitive assessment coefficients and brain white matter hyperintensity volume.This paper proposes an automatic 3D segmentation framework for white matter hyperintensity using a deep multi-mapping encoder-decoder structure. The method introduces a 3D residual mapping structure for the encoder and decoder. Multi-layer Cross-connected Residual Mapping Module (MCRCM) is proposed in the encoding stage to enhance the expressiveness of model and perception of detailed features. Spatial Attention Weighted Enhanced Supervision Module (SAWESM) is proposed in the decoding stage to adjust the supervision strategy through a spatial attention weighting mechanism. This helps guide the decoder to perform feature reconstruction and detail recovery more effectively.MethodsThis paper proposes an automatic 3D segmentation framework for white matter hyperintensity using a deep multi-mapping encoder-decoder structure. The method introduces a 3D residual mapping structure for the encoder and decoder. Multi-layer Cross-connected Residual Mapping Module (MCRCM) is proposed in the encoding stage to enhance the expressiveness of model and perception of detailed features. Spatial Attention Weighted Enhanced Supervision Module (SAWESM) is proposed in the decoding stage to adjust the supervision strategy through a spatial attention weighting mechanism. This helps guide the decoder to perform feature reconstruction and detail recovery more effectively.Experimental data was obtained from a privately owned independent brain white matter dataset. The results of the automatic 3D segmentation framework showed a higher segmentation accuracy compared to nnunet and nnunet-resnet, with a p-value of <0.001 for the two cognitive assessment parameters MMSE and MoCA. This indicates that larger brain white matter are associated with lower scores of MMSE and MoCA, which in turn indicates poorer cognitive function. The order of volume size of white matter hyperintensity in the three groups of cognitive states is dementia, MCI and NCI, respectively.ResultExperimental data was obtained from a privately owned independent brain white matter dataset. The results of the automatic 3D segmentation framework showed a higher segmentation accuracy compared to nnunet and nnunet-resnet, with a p-value of <0.001 for the two cognitive assessment parameters MMSE and MoCA. This indicates that larger brain white matter are associated with lower scores of MMSE and MoCA, which in turn indicates poorer cognitive function. The order of volume size of white matter hyperintensity in the three groups of cognitive states is dementia, MCI and NCI, respectively.The paper proposes an automatic 3D segmentation framework for brain white matter that achieves high-precision segmentation. The experimental results show that larger volumes of segmented regions have a negative correlation with lower scoring coefficients of MMSE and MoCA. This correlation analysis provides promising treatment prospects for the treatment of cerebral small vessel diseases in the brain through 3D segmentation analysis of brain white matter. The differences in the volume of white matter hyperintensity regions in subjects with three different cognitive states can help to better understand the mechanism of cognitive decline in clinical research.ConclusionThe paper proposes an automatic 3D segmentation framework for brain white matter that achieves high-precision segmentation. The experimental results show that larger volumes of segmented regions have a negative correlation with lower scoring coefficients of MMSE and MoCA. This correlation analysis provides promising treatment prospects for the treatment of cerebral small vessel diseases in the brain through 3D segmentation analysis of brain white matter. The differences in the volume of white matter hyperintensity regions in subjects with three different cognitive states can help to better understand the mechanism of cognitive decline in clinical research. ObjectiveCerebral white matter hyperintensity can lead to cerebral small vessel disease, MRI images in the brain are used to assess the degree of pathological changes in white matter regions. In this paper, we propose a framework for automatic 3D segmentation of brain white matter hyperintensity based on MRI images to address the problems of low accuracy and segmentation inhomogeneity in 3D segmentation. We explored correlation analyses of cognitive assessment parameters and multiple comparison analyses to investigate differences in brain white matter hyperintensity volume among three cognitive states, Dementia, MCI and NCI. The study explored the correlation between cognitive assessment coefficients and brain white matter hyperintensity volume.MethodsThis paper proposes an automatic 3D segmentation framework for white matter hyperintensity using a deep multi-mapping encoder-decoder structure. The method introduces a 3D residual mapping structure for the encoder and decoder. Multi-layer Cross-connected Residual Mapping Module (MCRCM) is proposed in the encoding stage to enhance the expressiveness of model and perception of detailed features. Spatial Attention Weighted Enhanced Supervision Module (SAWESM) is proposed in the decoding stage to adjust the supervision strategy through a spatial attention weighting mechanism. This helps guide the decoder to perform feature reconstruction and detail recovery more effectively.ResultExperimental data was obtained from a privately owned independent brain white matter dataset. The results of the automatic 3D segmentation framework showed a higher segmentation accuracy compared to nnunet and nnunet-resnet, with a p-value of <0.001 for the two cognitive assessment parameters MMSE and MoCA. This indicates that larger brain white matter are associated with lower scores of MMSE and MoCA, which in turn indicates poorer cognitive function. The order of volume size of white matter hyperintensity in the three groups of cognitive states is dementia, MCI and NCI, respectively.ConclusionThe paper proposes an automatic 3D segmentation framework for brain white matter that achieves high-precision segmentation. The experimental results show that larger volumes of segmented regions have a negative correlation with lower scoring coefficients of MMSE and MoCA. This correlation analysis provides promising treatment prospects for the treatment of cerebral small vessel diseases in the brain through 3D segmentation analysis of brain white matter. The differences in the volume of white matter hyperintensity regions in subjects with three different cognitive states can help to better understand the mechanism of cognitive decline in clinical research. |
| Author | Xu, Bin Zhang, Doudou Tian, Congyu Zhang, Xiaofeng Yan, Wei Wang, Yuanqing Liao, Xiangyun Cai, Xiaodong |
| AuthorAffiliation | 2 Shenzhen University School of Medicine, Shenzhen , Guangdong , China 3 Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences , Shenzhen , China 4 Brain Cognition and Brain Disease Institute, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences , Shenzhen , China 1 Department of Neurosurgery, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen , Guangdong , China |
| AuthorAffiliation_xml | – name: 3 Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences , Shenzhen , China – name: 1 Department of Neurosurgery, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen , Guangdong , China – name: 4 Brain Cognition and Brain Disease Institute, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences , Shenzhen , China – name: 2 Shenzhen University School of Medicine, Shenzhen , Guangdong , China |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/37576013$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_7759_cureus_54808 crossref_primary_10_1002_brb3_70313 crossref_primary_10_3389_fneur_2025_1593402 |
| Cites_doi | 10.3389/fnbot.2020.00049 10.1016/j.future.2020.02.054 10.1016/j.nicl.2019.102151 10.1007/s00530-022-00977-9 10.3390/app10196838 10.1016/j.compbiomed.2022.106033 10.1007/s00521-022-07859-1 10.1016/j.nicl.2020.102449 10.3389/fpubh.2022.879639 10.1016/j.cmpb.2021.106534 10.1016/j.patrec.2018.07.026 10.1016/j.compbiomed.2020.104097 10.3390/app12083764 10.1016/j.compbiomed.2021.104410 10.1002/mp.15956 10.1016/j.nicl.2023.103354 10.1007/s00521-021-06134-z 10.3389/fnins.2020.00350 10.1016/j.compbiomed.2022.105667 10.1002/mp.15846 10.1109/TMI.2019.2937271 10.1016/j.patrec.2019.08.003 10.3390/brainsci12060797 10.1109/TMI.2020.3035555 10.3389/fmed.2021.794969 10.1016/j.artmed.2020.101980 10.1109/TMI.2020.3034995 10.1007/s10278-021-00526-2 10.1016/j.brainres.2023.148318 10.1007/s12565-023-00715-9 10.1109/JBHI.2020.3038847 10.3389/fnagi.2020.00250 10.1177/0271678X19831016 10.1109/ACCESS.2020.3024277 |
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| Copyright | Copyright © 2023 Xu, Zhang, Tian, Yan, Wang, Zhang, Liao and Cai. Copyright © 2023 Xu, Zhang, Tian, Yan, Wang, Zhang, Liao and Cai. 2023 Xu, Zhang, Tian, Yan, Wang, Zhang, Liao and Cai |
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| Keywords | correlation analysis cerebral small vessel disease deep encoder-decoder structure medical 3D segmentation white matter hyperintensity |
| Language | English |
| License | Copyright © 2023 Xu, Zhang, Tian, Yan, Wang, Zhang, Liao and Cai. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
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| References | Cerri (B2) 2023; 38 Xu (B31) 2020; 14 Ding (B3) 2020; 25 Zhang (B20) 2020; 40 Qayyum (B12) 2020; 127 Wu (B14) 2022; 147 Wu (B21) 2023; 35 Li (B33) 2022; 213 Zhang (B5) 2023; 1807 Hassanzadeh (B22) 2021; 34 Andica (B6) 2023; 98 Jiang (B28) 2022; 12 Wang (B11) 2020; 108 Kaur (B8) 2021; 33 Gao (B32) 2022; 10 Sun (B30) 2020; 14 Alzaid (B4) 2020; 12 Yang (B9) 2023; 29 Shabani (B10) 2022; 149 Saleem (B13) 2021; 133 Bitarafan (B16) 2020; 25 Li (B1) 2020; 40 Liu (B25) 2022; 12 Middlebrooks (B7) 2020; 28 Bennai (B34) 2020; 110 Qayyum (B27) 2020; 8 He (B29) 2022; 8 Rui-Qiang (B18) 2023; 50 Hassanzadeh (B35) 2020; 40 Ou (B19) 2022; 49 Liu (B17) 2019 AlZu'bi (B15) 2020; 130 Sun (B23) 2019; 39 Li (B24) 2020; 10 Indraswari (B26) 2019; 125 |
| References_xml | – volume: 14 start-page: 49 year: 2020 ident: B30 article-title: Autopath: image-specific inference for 3D segmentation publication-title: Front Neurorobot doi: 10.3389/fnbot.2020.00049 – volume: 108 start-page: 135 year: 2020 ident: B11 article-title: A deep learning based medical image segmentation technique in Internet-of-Medical-Things domain publication-title: Fut Generat Comp Syst doi: 10.1016/j.future.2020.02.054 – volume: 25 start-page: 102151 year: 2020 ident: B3 article-title: An improved algorithm of white matter hyperintensity detection in elderly adults publication-title: NeuroImageClin doi: 10.1016/j.nicl.2019.102151 – volume: 29 start-page: 33 year: 2023 ident: B9 article-title: VoxSeP: semi-positive voxels assist self-supervised 3D medical segmentation publication-title: Multimedia Syst doi: 10.1007/s00530-022-00977-9 – volume: 10 start-page: 6838 year: 2020 ident: B24 article-title: Point-sampling method based on 3D U-net architecture to reduce the influence of false positive and solve boundary blur problem in 3D CT image segmentation publication-title: Appl Sci doi: 10.3390/app10196838 – volume: 149 start-page: 106033 year: 2022 ident: B10 article-title: Self-supervised region-aware segmentation of COVID-19 CT images using 3D GAN and contrastive learning publication-title: Comput Biol Med doi: 10.1016/j.compbiomed.2022.106033 – volume: 35 start-page: 1931 year: 2023 ident: B21 article-title: D-former: a u-shaped dilated transformer for 3d medical image segmentation publication-title: Neural Comput Appl doi: 10.1007/s00521-022-07859-1 – volume: 28 start-page: 102449 year: 2020 ident: B7 article-title: Improved detection of focal cortical dysplasia using a novel 3D imaging sequence: Edge-Enhancing Gradient Echo (3D-EDGE) MRI publication-title: NeuroImage Clin doi: 10.1016/j.nicl.2020.102449 – volume: 10 start-page: 879639 year: 2022 ident: B32 article-title: Medical image segmentation algorithm for three-dimensional multimodal using deep reinforcement learning and big data analytics publication-title: Front Public Health doi: 10.3389/fpubh.2022.879639 – volume: 213 start-page: 106534 year: 2022 ident: B33 article-title: An efficient interactive multi-label segmentation tool for 2D and 3D medical images using fully connected conditional random field publication-title: Comput Methods Progr Biomed doi: 10.1016/j.cmpb.2021.106534 – start-page: p. 81 year: 2019 ident: B17 article-title: Multipath densely connected convolutional neural network for brain tumor segmentation – volume: 130 start-page: 312 year: 2020 ident: B15 article-title: Parallel implementation for 3d medical volume fuzzy segmentation doi: 10.1016/j.patrec.2018.07.026 – volume: 127 start-page: 104097 year: 2020 ident: B12 article-title: Automatic segmentation of tumors and affected organs in the abdomen using a 3D hybrid model for computed tomography imaging publication-title: Comput Biol Med doi: 10.1016/j.compbiomed.2020.104097 – volume: 12 start-page: 3764 year: 2022 ident: B25 article-title: Attention V-Net: a modified V-Net architecture for left atrial segmentation publication-title: Appl Sci doi: 10.3390/app12083764 – volume: 133 start-page: 104410 year: 2021 ident: B13 article-title: Visual interpretability in 3D brain tumor segmentation network publication-title: Comput Biol Med doi: 10.1016/j.compbiomed.2021.104410 – volume: 50 start-page: 50 year: 2023 ident: B18 article-title: Automatic localization of target point for subthalamic nucleus-deep brain stimulation via hierarchical attention-UNet based MRI segmentation publication-title: Med Phys doi: 10.1002/mp.15956 – volume: 38 start-page: 103354 year: 2023 ident: B2 article-title: An open-source tool for longitudinal whole-brain and white matter lesion segmentation publication-title: NeuroImage Clin doi: 10.1016/j.nicl.2023.103354 – volume: 33 start-page: 14991 year: 2021 ident: B8 article-title: GA-UNet: UNet-based framework for segmentation of 2D and 3D medical images applicable on heterogeneous datasets publication-title: Neural Comp Appl doi: 10.1007/s00521-021-06134-z – volume: 14 start-page: 350 year: 2020 ident: B31 article-title: 3d-boxsup: Positive-unlabeled learning of brain tumor segmentation networks from 3d bounding boxes publication-title: Front Neurosci doi: 10.3389/fnins.2020.00350 – volume: 147 start-page: 105667 year: 2022 ident: B14 article-title: Slice imputation: multiple intermediate slices interpolation for anisotropic 3D medical image segmentation publication-title: Comput Biol Med doi: 10.1016/j.compbiomed.2022.105667 – volume: 49 start-page: 7038 year: 2022 ident: B19 article-title: A deep learning-based automatic system for intracranial aneurysms diagnosis on three-dimensional digital subtraction angiographic images publication-title: Med Phys doi: 10.1002/mp.15846 – volume: 39 start-page: 898 year: 2019 ident: B23 article-title: 3D spatially weighted network for segmentation of brain tissue from MRI publication-title: IEEE Trans Med Imaging doi: 10.1109/TMI.2019.2937271 – volume: 125 start-page: 791 year: 2019 ident: B26 article-title: Multi-projection deep learning network for segmentation of 3D medical images publication-title: Pattern Recognit Lett doi: 10.1016/j.patrec.2019.08.003 – volume: 12 start-page: 797 year: 2022 ident: B28 article-title: SwinBTS: a method for 3D multimodal brain tumor segmentation using swin transformer publication-title: Brain Sci doi: 10.3390/brainsci12060797 – volume: 40 start-page: 712 year: 2020 ident: B35 article-title: 2D to 3D evolutionary deep convolutional neural networks for medical image segmentation publication-title: IEEE Trans Med Imaging doi: 10.1109/TMI.2020.3035555 – volume: 8 start-page: 794969 year: 2022 ident: B29 article-title: Three-dimensional liver image segmentation using generative adversarial networks based on feature restoration publication-title: Front Med doi: 10.3389/fmed.2021.794969 – volume: 110 start-page: 101980 year: 2020 ident: B34 article-title: A stochastic multi-agent approach for medical-image segmentation: application to tumor segmentation in brain MR images publication-title: Artif Intell Med doi: 10.1016/j.artmed.2020.101980 – volume: 40 start-page: 661 year: 2020 ident: B20 article-title: Inter-slice context residual learning for 3D medical image segmentation publication-title: IEEE Trans Med Imaging doi: 10.1109/TMI.2020.3034995 – volume: 34 start-page: 1387 year: 2021 ident: B22 article-title: Evolutionary deep attention convolutional neural networks for 2D and 3D medical image segmentation publication-title: J Digit Imaging doi: 10.1007/s10278-021-00526-2 – volume: 1807 start-page: 148318 year: 2023 ident: B5 article-title: YKL-40 as a novel biomarker related to white matter damage and cognitive impairment in patients with cerebral small vessel disease publication-title: Brain Res doi: 10.1016/j.brainres.2023.148318 – volume: 98 start-page: 318 year: 2023 ident: B6 article-title: Automated three-dimensional major white matter bundle segmentation using diffusion magnetic resonance imaging publication-title: Anat Sci Int doi: 10.1007/s12565-023-00715-9 – volume: 25 start-page: 2665 year: 2020 ident: B16 article-title: 3D image segmentation with sparse annotation by self-training and internal registration publication-title: IEEE J Biomed Health Informat doi: 10.1109/JBHI.2020.3038847 – volume: 12 start-page: 250 year: 2020 ident: B4 article-title: Distinct relationship between cognitive flexibility and white matter integrity in individuals at risk of Parkinson's disease publication-title: Front Aging Neurosci doi: 10.3389/fnagi.2020.00250 – volume: 40 start-page: 501 year: 2020 ident: B1 article-title: Two-photon microscopic imaging of capillary red blood cell flux in mouse brain reveals vulnerability of cerebral white matter to hypoperfusion publication-title: J Cereb Blood Flow Metab doi: 10.1177/0271678X19831016 – volume: 8 start-page: 169794 year: 2020 ident: B27 article-title: Automatic segmentation using a hybrid dense network integrated with an 3D-atrous spatial pyramid pooling module for computed tomography (CT) imaging publication-title: IEEE Access doi: 10.1109/ACCESS.2020.3024277 |
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| Snippet | Cerebral white matter hyperintensity can lead to cerebral small vessel disease, MRI images in the brain are used to assess the degree of pathological changes... ObjectiveCerebral white matter hyperintensity can lead to cerebral small vessel disease, MRI images in the brain are used to assess the degree of pathological... |
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| SubjectTerms | cerebral small vessel disease correlation analysis deep encoder-decoder structure medical 3D segmentation Neurology white matter hyperintensity |
| Title | Automatic segmentation of white matter hyperintensities and correlation analysis for cerebral small vessel disease |
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