Voxel-based morphometry in single subjects without a scanner-specific normal database using a convolutional neural network.
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| Title: | Voxel-based morphometry in single subjects without a scanner-specific normal database using a convolutional neural network. |
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| Authors: | Krüger, Julia1 (AUTHOR), Opfer, Roland1 (AUTHOR), Spies, Lothar1 (AUTHOR), Hedderich, Dennis2 (AUTHOR), Buchert, Ralph3 (AUTHOR) r.buchert@uke.de |
| Source: | European Radiology. Jun2024, Vol. 34 Issue 6, p3578-3587. 10p. |
| Subjects: | Convolutional neural networks, Voxel-based morphometry, Databases, Frontotemporal lobar degeneration, Alzheimer's disease, Neurodegeneration |
| Abstract: | Objectives: Reliable detection of disease-specific atrophy in individual T1w-MRI by voxel-based morphometry (VBM) requires scanner-specific normal databases (NDB), which often are not available. The aim of this retrospective study was to design, train, and test a deep convolutional neural network (CNN) for single-subject VBM without the need for a NDB (CNN-VBM). Materials and methods: The training dataset comprised 8945 T1w scans from 65 different scanners. The gold standard VBM maps were obtained by conventional VBM with a scanner-specific NDB for each of the 65 scanners. CNN-VBM was tested in an independent dataset comprising healthy controls (n = 37) and subjects with Alzheimer's disease (AD, n = 51) or frontotemporal lobar degeneration (FTLD, n = 30). A scanner-specific NDB for the generation of the gold standard VBM maps was available also for the test set. The technical performance of CNN-VBM was characterized by the Dice coefficient of CNN-VBM maps relative to VBM maps from scanner-specific VBM. For clinical testing, VBM maps were categorized visually according to the clinical diagnoses in the test set by two independent readers, separately for both VBM methods. Results: The VBM maps from CNN-VBM were similar to the scanner-specific VBM maps (median Dice coefficient 0.85, interquartile range [0.81, 0.90]). Overall accuracy of the visual categorization of the VBM maps for the detection of AD or FTLD was 89.8% for CNN-VBM and 89.0% for scanner-specific VBM. Conclusion: CNN-VBM without NDB provides a similar performance in the detection of AD- and FTLD-specific atrophy as conventional VBM. Clinical relevance statement: A deep convolutional neural network for voxel-based morphometry eliminates the need of scanner-specific normal databases without relevant performance loss and, therefore, could pave the way for the widespread clinical use of voxel-based morphometry to support the diagnosis of neurodegenerative diseases. Key Points: • The need of normal databases is a barrier for widespread use of voxel-based brain morphometry. • A convolutional neural network achieved a similar performance for detection of atrophy than conventional voxel-based morphometry. • Convolutional neural networks can pave the way for widespread clinical use of voxel-based morphometry. [ABSTRACT FROM AUTHOR] |
| Copyright of European Radiology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 177797472 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Voxel-based morphometry in single subjects without a scanner-specific normal database using a convolutional neural network. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Krüger%2C+Julia%22">Krüger, Julia</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Opfer%2C+Roland%22">Opfer, Roland</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Spies%2C+Lothar%22">Spies, Lothar</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hedderich%2C+Dennis%22">Hedderich, Dennis</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Buchert%2C+Ralph%22">Buchert, Ralph</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> r.buchert@uke.de</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22European+Radiology%22">European Radiology</searchLink>. Jun2024, Vol. 34 Issue 6, p3578-3587. 10p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Voxel-based+morphometry%22">Voxel-based morphometry</searchLink><br /><searchLink fieldCode="DE" term="%22Databases%22">Databases</searchLink><br /><searchLink fieldCode="DE" term="%22Frontotemporal+lobar+degeneration%22">Frontotemporal lobar degeneration</searchLink><br /><searchLink fieldCode="DE" term="%22Alzheimer's+disease%22">Alzheimer's disease</searchLink><br /><searchLink fieldCode="DE" term="%22Neurodegeneration%22">Neurodegeneration</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Objectives: Reliable detection of disease-specific atrophy in individual T1w-MRI by voxel-based morphometry (VBM) requires scanner-specific normal databases (NDB), which often are not available. The aim of this retrospective study was to design, train, and test a deep convolutional neural network (CNN) for single-subject VBM without the need for a NDB (CNN-VBM). Materials and methods: The training dataset comprised 8945 T1w scans from 65 different scanners. The gold standard VBM maps were obtained by conventional VBM with a scanner-specific NDB for each of the 65 scanners. CNN-VBM was tested in an independent dataset comprising healthy controls (n = 37) and subjects with Alzheimer's disease (AD, n = 51) or frontotemporal lobar degeneration (FTLD, n = 30). A scanner-specific NDB for the generation of the gold standard VBM maps was available also for the test set. The technical performance of CNN-VBM was characterized by the Dice coefficient of CNN-VBM maps relative to VBM maps from scanner-specific VBM. For clinical testing, VBM maps were categorized visually according to the clinical diagnoses in the test set by two independent readers, separately for both VBM methods. Results: The VBM maps from CNN-VBM were similar to the scanner-specific VBM maps (median Dice coefficient 0.85, interquartile range [0.81, 0.90]). Overall accuracy of the visual categorization of the VBM maps for the detection of AD or FTLD was 89.8% for CNN-VBM and 89.0% for scanner-specific VBM. Conclusion: CNN-VBM without NDB provides a similar performance in the detection of AD- and FTLD-specific atrophy as conventional VBM. Clinical relevance statement: A deep convolutional neural network for voxel-based morphometry eliminates the need of scanner-specific normal databases without relevant performance loss and, therefore, could pave the way for the widespread clinical use of voxel-based morphometry to support the diagnosis of neurodegenerative diseases. Key Points: • The need of normal databases is a barrier for widespread use of voxel-based brain morphometry. • A convolutional neural network achieved a similar performance for detection of atrophy than conventional voxel-based morphometry. • Convolutional neural networks can pave the way for widespread clinical use of voxel-based morphometry. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of European Radiology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s00330-023-10356-1 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 3578 Subjects: – SubjectFull: Convolutional neural networks Type: general – SubjectFull: Voxel-based morphometry Type: general – SubjectFull: Databases Type: general – SubjectFull: Frontotemporal lobar degeneration Type: general – SubjectFull: Alzheimer's disease Type: general – SubjectFull: Neurodegeneration Type: general Titles: – TitleFull: Voxel-based morphometry in single subjects without a scanner-specific normal database using a convolutional neural network. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Krüger, Julia – PersonEntity: Name: NameFull: Opfer, Roland – PersonEntity: Name: NameFull: Spies, Lothar – PersonEntity: Name: NameFull: Hedderich, Dennis – PersonEntity: Name: NameFull: Buchert, Ralph IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 09387994 Numbering: – Type: volume Value: 34 – Type: issue Value: 6 Titles: – TitleFull: European Radiology Type: main |
| ResultId | 1 |
