A framework for the automatic detection and characterization of brain malformations: Validation on the corpus callosum.
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| Title: | A framework for the automatic detection and characterization of brain malformations: Validation on the corpus callosum. |
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| Authors: | Peruzzo, Denis1,2 denis.peruzzo@gmail.com, Arrigoni, Filippo1, Triulzi, Fabio1,3, Righini, Andrea4, Parazzini, Cecilia4, Castellani, Umberto2 |
| Source: | Medical Image Analysis. Aug2016, Vol. 32, p233-242. 10p. |
| Subjects: | Brain abnormality diagnosis, Magnetic resonance imaging of the brain, Corpus callosum abnormalities, Support vector machines, Neurologic examination |
| Abstract: | In this paper, we extend the one-class Support Vector Machine (SVM) and the regularized discriminative direction analysis to the Multiple Kernel (MK) framework, providing an effective analysis pipeline for the detection and characterization of brain malformations, in particular those affecting the corpus callosum. The detection of the brain malformations is currently performed by visual inspection of MRI images, making the diagnostic process sensible to the operator experience and subjectiveness. The method we propose addresses these problems by automatically reproducing the neuroradiologist’s approach. One-class SVMs are appropriate to cope with heterogeneous brain abnormalities that are considered outliers. The MK framework allows to efficiently combine the different geometric features that can be used to describe brain structures. Moreover, the regularized discriminative direction analysis is exploited to highlight the specific malformative patterns for each patient. We performed two different experiments. Firstly, we tested the proposed method to detect the malformations of the corpus callosum on a 104 subject dataset. Results showed that the proposed pipeline can classify the subjects with an accuracy larger than 90% and that the discriminative direction analysis can highlight a wide range of malformative patterns (e.g., local, diffuse, and complex abnormalities). Secondly, we compared the diagnosis of four neuroradiologists on a dataset of 128 subjects. The diagnosis was performed both in blind condition and using the classifier and the discriminative direction outputs. Results showed that the use of the proposed pipeline as an assisted diagnosis tool improves the inter-subject variability of the diagnosis. Finally, a graphical representation of the discriminative direction analysis was proposed to enhance the interpretability of the results and provide the neuroradiologist with a tool to fully and clearly characterize the patient malformations at single-subject level. [ABSTRACT FROM AUTHOR] |
| Copyright of Medical Image Analysis is the property of Elsevier B.V. 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 |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 115979475 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A framework for the automatic detection and characterization of brain malformations: Validation on the corpus callosum. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Peruzzo%2C+Denis%22">Peruzzo, Denis</searchLink><relatesTo>1,2</relatesTo><i> denis.peruzzo@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Arrigoni%2C+Filippo%22">Arrigoni, Filippo</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Triulzi%2C+Fabio%22">Triulzi, Fabio</searchLink><relatesTo>1,3</relatesTo><br /><searchLink fieldCode="AR" term="%22Righini%2C+Andrea%22">Righini, Andrea</searchLink><relatesTo>4</relatesTo><br /><searchLink fieldCode="AR" term="%22Parazzini%2C+Cecilia%22">Parazzini, Cecilia</searchLink><relatesTo>4</relatesTo><br /><searchLink fieldCode="AR" term="%22Castellani%2C+Umberto%22">Castellani, Umberto</searchLink><relatesTo>2</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Medical+Image+Analysis%22">Medical Image Analysis</searchLink>. Aug2016, Vol. 32, p233-242. 10p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Brain+abnormality+diagnosis%22">Brain abnormality diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Magnetic+resonance+imaging+of+the+brain%22">Magnetic resonance imaging of the brain</searchLink><br /><searchLink fieldCode="DE" term="%22Corpus+callosum+abnormalities%22">Corpus callosum abnormalities</searchLink><br /><searchLink fieldCode="DE" term="%22Support+vector+machines%22">Support vector machines</searchLink><br /><searchLink fieldCode="DE" term="%22Neurologic+examination%22">Neurologic examination</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In this paper, we extend the one-class Support Vector Machine (SVM) and the regularized discriminative direction analysis to the Multiple Kernel (MK) framework, providing an effective analysis pipeline for the detection and characterization of brain malformations, in particular those affecting the corpus callosum. The detection of the brain malformations is currently performed by visual inspection of MRI images, making the diagnostic process sensible to the operator experience and subjectiveness. The method we propose addresses these problems by automatically reproducing the neuroradiologist’s approach. One-class SVMs are appropriate to cope with heterogeneous brain abnormalities that are considered outliers. The MK framework allows to efficiently combine the different geometric features that can be used to describe brain structures. Moreover, the regularized discriminative direction analysis is exploited to highlight the specific malformative patterns for each patient. We performed two different experiments. Firstly, we tested the proposed method to detect the malformations of the corpus callosum on a 104 subject dataset. Results showed that the proposed pipeline can classify the subjects with an accuracy larger than 90% and that the discriminative direction analysis can highlight a wide range of malformative patterns (e.g., local, diffuse, and complex abnormalities). Secondly, we compared the diagnosis of four neuroradiologists on a dataset of 128 subjects. The diagnosis was performed both in blind condition and using the classifier and the discriminative direction outputs. Results showed that the use of the proposed pipeline as an assisted diagnosis tool improves the inter-subject variability of the diagnosis. Finally, a graphical representation of the discriminative direction analysis was proposed to enhance the interpretability of the results and provide the neuroradiologist with a tool to fully and clearly characterize the patient malformations at single-subject level. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Medical Image Analysis is the property of Elsevier B.V. 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: 10.1016/j.media.2016.05.001 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 233 Subjects: – SubjectFull: Brain abnormality diagnosis Type: general – SubjectFull: Magnetic resonance imaging of the brain Type: general – SubjectFull: Corpus callosum abnormalities Type: general – SubjectFull: Support vector machines Type: general – SubjectFull: Neurologic examination Type: general Titles: – TitleFull: A framework for the automatic detection and characterization of brain malformations: Validation on the corpus callosum. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Peruzzo, Denis – PersonEntity: Name: NameFull: Arrigoni, Filippo – PersonEntity: Name: NameFull: Triulzi, Fabio – PersonEntity: Name: NameFull: Righini, Andrea – PersonEntity: Name: NameFull: Parazzini, Cecilia – PersonEntity: Name: NameFull: Castellani, Umberto IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug2016 Type: published Y: 2016 Identifiers: – Type: issn-print Value: 13618415 Numbering: – Type: volume Value: 32 Titles: – TitleFull: Medical Image Analysis Type: main |
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