Deep learning segmentation of the choroid plexus from structural magnetic resonance imaging (MRI): validation and normative ranges across the adult lifespan
Abstract Background The choroid plexus functions as the blood-cerebrospinal fluid (CSF) barrier, plays an important role in CSF production and circulation, and has gained increased attention in light of the recent elucidation of CSF circulation dysfunction in neurodegenerative conditions. However, m...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2024-02-01
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| Series: | Fluids and Barriers of the CNS |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12987-024-00525-9 |
| _version_ | 1797273558954541056 |
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| author | Jarrod J. Eisma Colin D. McKnight Kilian Hett Jason Elenberger Caleb J. Han Alexander K. Song Ciaran Considine Daniel O. Claassen Manus J. Donahue |
| author_facet | Jarrod J. Eisma Colin D. McKnight Kilian Hett Jason Elenberger Caleb J. Han Alexander K. Song Ciaran Considine Daniel O. Claassen Manus J. Donahue |
| author_sort | Jarrod J. Eisma |
| collection | DOAJ |
| description | Abstract Background The choroid plexus functions as the blood-cerebrospinal fluid (CSF) barrier, plays an important role in CSF production and circulation, and has gained increased attention in light of the recent elucidation of CSF circulation dysfunction in neurodegenerative conditions. However, methods for routinely quantifying choroid plexus volume are suboptimal and require technical improvements and validation. Here, we propose three deep learning models that can segment the choroid plexus from commonly-acquired anatomical MRI data and report performance metrics and changes across the adult lifespan. Methods Fully convolutional neural networks were trained from 3D T1-weighted, 3D T2-weighted, and 2D T2-weighted FLAIR MRI using gold-standard manual segmentations in control and neurodegenerative participants across the lifespan (n = 50; age = 21–85 years). Dice coefficients, 95% Hausdorff distances, and area-under-curve (AUCs) were calculated for each model and compared to segmentations from FreeSurfer using two-tailed Wilcoxon tests (significance criteria: p < 0.05 after false discovery rate multiple comparisons correction). Metrics were regressed against lateral ventricular volume using generalized linear models to assess model performance for varying levels of atrophy. Finally, models were applied to an expanded cohort of adult controls (n = 98; age = 21–89 years) to provide an exemplar of choroid plexus volumetry values across the lifespan. Results Deep learning results yielded Dice coefficient = 0.72, Hausdorff distance = 1.97 mm, AUC = 0.87 for T1-weighted MRI, Dice coefficient = 0.72, Hausdorff distance = 2.22 mm, AUC = 0.87 for T2-weighted MRI, and Dice coefficient = 0.74, Hausdorff distance = 1.69 mm, AUC = 0.87 for T2-weighted FLAIR MRI; values did not differ significantly between MRI sequences and were statistically improved compared to current commercially-available algorithms (p < 0.001). The intraclass coefficients were 0.95, 0.95, and 0.96 between T1-weighted and T2-weighted FLAIR, T1-weighted and T2-weighted, and T2-weighted and T2-weighted FLAIR models, respectively. Mean lateral ventricle choroid plexus volume across all participants was 3.20 ± 1.4 cm3; a significant, positive relationship (R2 = 0.54-0.60) was observed between participant age and choroid plexus volume for all MRI sequences (p < 0.001). Conclusions Findings support comparable performance in choroid plexus delineation between standard, clinically available, non-contrasted anatomical MRI sequences. The software embedding the evaluated models is freely available online and should provide a useful tool for the growing number of studies that desire to quantitatively evaluate choroid plexus structure and function ( https://github.com/hettk/chp_seg ). |
| first_indexed | 2024-03-07T14:46:06Z |
| format | Article |
| id | doaj.art-d19b182c1aa74dad8a522a5900eb7c3c |
| institution | Directory Open Access Journal |
| issn | 2045-8118 |
| language | English |
| last_indexed | 2024-03-07T14:46:06Z |
| publishDate | 2024-02-01 |
| publisher | BMC |
| record_format | Article |
| series | Fluids and Barriers of the CNS |
| spelling | doaj.art-d19b182c1aa74dad8a522a5900eb7c3c2024-03-05T20:00:43ZengBMCFluids and Barriers of the CNS2045-81182024-02-0121111310.1186/s12987-024-00525-9Deep learning segmentation of the choroid plexus from structural magnetic resonance imaging (MRI): validation and normative ranges across the adult lifespanJarrod J. Eisma0Colin D. McKnight1Kilian Hett2Jason Elenberger3Caleb J. Han4Alexander K. Song5Ciaran Considine6Daniel O. Claassen7Manus J. Donahue8Department of Neurology, Behavioral and Cognitive Neurology, Vanderbilt University Medical CenterDepartment of Radiology and Radiological Sciences, Vanderbilt University Medical CenterDepartment of Neurology, Behavioral and Cognitive Neurology, Vanderbilt University Medical CenterDepartment of Neurology, Behavioral and Cognitive Neurology, Vanderbilt University Medical CenterDepartment of Neurology, Behavioral and Cognitive Neurology, Vanderbilt University Medical CenterDepartment of Neurology, Behavioral and Cognitive Neurology, Vanderbilt University Medical CenterDepartment of Neurology, Behavioral and Cognitive Neurology, Vanderbilt University Medical CenterDepartment of Neurology, Behavioral and Cognitive Neurology, Vanderbilt University Medical CenterDepartment of Neurology, Behavioral and Cognitive Neurology, Vanderbilt University Medical CenterAbstract Background The choroid plexus functions as the blood-cerebrospinal fluid (CSF) barrier, plays an important role in CSF production and circulation, and has gained increased attention in light of the recent elucidation of CSF circulation dysfunction in neurodegenerative conditions. However, methods for routinely quantifying choroid plexus volume are suboptimal and require technical improvements and validation. Here, we propose three deep learning models that can segment the choroid plexus from commonly-acquired anatomical MRI data and report performance metrics and changes across the adult lifespan. Methods Fully convolutional neural networks were trained from 3D T1-weighted, 3D T2-weighted, and 2D T2-weighted FLAIR MRI using gold-standard manual segmentations in control and neurodegenerative participants across the lifespan (n = 50; age = 21–85 years). Dice coefficients, 95% Hausdorff distances, and area-under-curve (AUCs) were calculated for each model and compared to segmentations from FreeSurfer using two-tailed Wilcoxon tests (significance criteria: p < 0.05 after false discovery rate multiple comparisons correction). Metrics were regressed against lateral ventricular volume using generalized linear models to assess model performance for varying levels of atrophy. Finally, models were applied to an expanded cohort of adult controls (n = 98; age = 21–89 years) to provide an exemplar of choroid plexus volumetry values across the lifespan. Results Deep learning results yielded Dice coefficient = 0.72, Hausdorff distance = 1.97 mm, AUC = 0.87 for T1-weighted MRI, Dice coefficient = 0.72, Hausdorff distance = 2.22 mm, AUC = 0.87 for T2-weighted MRI, and Dice coefficient = 0.74, Hausdorff distance = 1.69 mm, AUC = 0.87 for T2-weighted FLAIR MRI; values did not differ significantly between MRI sequences and were statistically improved compared to current commercially-available algorithms (p < 0.001). The intraclass coefficients were 0.95, 0.95, and 0.96 between T1-weighted and T2-weighted FLAIR, T1-weighted and T2-weighted, and T2-weighted and T2-weighted FLAIR models, respectively. Mean lateral ventricle choroid plexus volume across all participants was 3.20 ± 1.4 cm3; a significant, positive relationship (R2 = 0.54-0.60) was observed between participant age and choroid plexus volume for all MRI sequences (p < 0.001). Conclusions Findings support comparable performance in choroid plexus delineation between standard, clinically available, non-contrasted anatomical MRI sequences. The software embedding the evaluated models is freely available online and should provide a useful tool for the growing number of studies that desire to quantitatively evaluate choroid plexus structure and function ( https://github.com/hettk/chp_seg ).https://doi.org/10.1186/s12987-024-00525-9Choroid plexusDeep learningGlymphaticSegmentationCerebrospinal fluidNeurofluids |
| spellingShingle | Jarrod J. Eisma Colin D. McKnight Kilian Hett Jason Elenberger Caleb J. Han Alexander K. Song Ciaran Considine Daniel O. Claassen Manus J. Donahue Deep learning segmentation of the choroid plexus from structural magnetic resonance imaging (MRI): validation and normative ranges across the adult lifespan Fluids and Barriers of the CNS Choroid plexus Deep learning Glymphatic Segmentation Cerebrospinal fluid Neurofluids |
| title | Deep learning segmentation of the choroid plexus from structural magnetic resonance imaging (MRI): validation and normative ranges across the adult lifespan |
| title_full | Deep learning segmentation of the choroid plexus from structural magnetic resonance imaging (MRI): validation and normative ranges across the adult lifespan |
| title_fullStr | Deep learning segmentation of the choroid plexus from structural magnetic resonance imaging (MRI): validation and normative ranges across the adult lifespan |
| title_full_unstemmed | Deep learning segmentation of the choroid plexus from structural magnetic resonance imaging (MRI): validation and normative ranges across the adult lifespan |
| title_short | Deep learning segmentation of the choroid plexus from structural magnetic resonance imaging (MRI): validation and normative ranges across the adult lifespan |
| title_sort | deep learning segmentation of the choroid plexus from structural magnetic resonance imaging mri validation and normative ranges across the adult lifespan |
| topic | Choroid plexus Deep learning Glymphatic Segmentation Cerebrospinal fluid Neurofluids |
| url | https://doi.org/10.1186/s12987-024-00525-9 |
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