Automated characterisation of cerebral microbleeds using their size and spatial distribution on brain MRI
Abstract Cerebral microbleeds (CMBs) are small, hypointense hemosiderin deposits in the brain measuring 2–10 mm in diameter. As one of the important biomarkers of small vessel disease, they have been associated with various neurodegenerative and cerebrovascular diseases. Hence, automated detection,...
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SpringerOpen
2025-01-01
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| Series: | European Radiology Experimental |
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| Online Access: | https://doi.org/10.1186/s41747-024-00544-z |
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| author | Vaanathi Sundaresan Giovanna Zamboni Robert A. Dineen Dorothee P. Auer Stamatios N. Sotiropoulos Nikola Sprigg Mark Jenkinson Ludovica Griffanti |
| author_facet | Vaanathi Sundaresan Giovanna Zamboni Robert A. Dineen Dorothee P. Auer Stamatios N. Sotiropoulos Nikola Sprigg Mark Jenkinson Ludovica Griffanti |
| author_sort | Vaanathi Sundaresan |
| collection | DOAJ |
| description | Abstract Cerebral microbleeds (CMBs) are small, hypointense hemosiderin deposits in the brain measuring 2–10 mm in diameter. As one of the important biomarkers of small vessel disease, they have been associated with various neurodegenerative and cerebrovascular diseases. Hence, automated detection, and subsequent extraction of clinically useful metrics (e.g., size and spatial distribution) from CMBs are essential for investigating their clinical impact, especially in large-scale studies. While some work has been done for CMB segmentation, extraction of clinically relevant information is not yet explored. Herein, we propose the first automated method to characterise CMBs using their size and spatial distribution, i.e., CMB count in three regions (and their substructures) used in Microbleed Anatomical Rating Scale (MARS): infratentorial, deep, and lobar. Our method uses structural atlases of the brain for determining individual regions. On an intracerebral haemorrhage study dataset, we achieved a mean absolute error of 2.5 mm for size estimation and an overall accuracy > 90% for automated rating. The code and the atlas of MARS regions in Montreal Neurological Institute—MNI space are publicly available. Relevance statement Our method to automatically characterise cerebral microbleeds (size and location) showed a mean absolute error of 2.5 mm for size estimation and an over 90% accuracy for rating of infratentorial, deep and lobar regions. This is a promising approach to automatically provide clinically relevant cerebral microbleeds metrics. Key Points We present a method to automatically characterise cerebral microbleeds according to size and location. The method achieved a mean absolute error of 2.5 mm for size estimation. Automated rating for infratentorial, deep, and lobar regions achieved an over 90% overall accuracy. We made the code and atlas of Microbleed Anatomical Rating Scale regions publicly available. Graphical Abstract |
| format | Article |
| id | doaj-art-2656b6d249c54f619001f4900cf23798 |
| institution | Kabale University |
| issn | 2509-9280 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | European Radiology Experimental |
| spelling | doaj-art-2656b6d249c54f619001f4900cf237982025-01-19T12:09:32ZengSpringerOpenEuropean Radiology Experimental2509-92802025-01-01911810.1186/s41747-024-00544-zAutomated characterisation of cerebral microbleeds using their size and spatial distribution on brain MRIVaanathi Sundaresan0Giovanna Zamboni1Robert A. Dineen2Dorothee P. Auer3Stamatios N. Sotiropoulos4Nikola Sprigg5Mark Jenkinson6Ludovica Griffanti7Department of Computational and Data Sciences, Indian Institute of ScienceNuffield Department of Clinical Neurosciences, University of OxfordNational Institute for Health and Care Research (NIHR) Nottingham Biomedical Research Centre, Queens Medical Centre, Sir Peter Mansfield Imaging Centre, University of NottinghamNational Institute for Health and Care Research (NIHR) Nottingham Biomedical Research Centre, Queens Medical Centre, Sir Peter Mansfield Imaging Centre, University of NottinghamNuffield Department of Clinical Neurosciences, University of OxfordRadiological Sciences, Mental Health and Clinical Neurosciences, School of Medicine, University of NottinghamNuffield Department of Clinical Neurosciences, University of OxfordNuffield Department of Clinical Neurosciences, University of OxfordAbstract Cerebral microbleeds (CMBs) are small, hypointense hemosiderin deposits in the brain measuring 2–10 mm in diameter. As one of the important biomarkers of small vessel disease, they have been associated with various neurodegenerative and cerebrovascular diseases. Hence, automated detection, and subsequent extraction of clinically useful metrics (e.g., size and spatial distribution) from CMBs are essential for investigating their clinical impact, especially in large-scale studies. While some work has been done for CMB segmentation, extraction of clinically relevant information is not yet explored. Herein, we propose the first automated method to characterise CMBs using their size and spatial distribution, i.e., CMB count in three regions (and their substructures) used in Microbleed Anatomical Rating Scale (MARS): infratentorial, deep, and lobar. Our method uses structural atlases of the brain for determining individual regions. On an intracerebral haemorrhage study dataset, we achieved a mean absolute error of 2.5 mm for size estimation and an overall accuracy > 90% for automated rating. The code and the atlas of MARS regions in Montreal Neurological Institute—MNI space are publicly available. Relevance statement Our method to automatically characterise cerebral microbleeds (size and location) showed a mean absolute error of 2.5 mm for size estimation and an over 90% accuracy for rating of infratentorial, deep and lobar regions. This is a promising approach to automatically provide clinically relevant cerebral microbleeds metrics. Key Points We present a method to automatically characterise cerebral microbleeds according to size and location. The method achieved a mean absolute error of 2.5 mm for size estimation. Automated rating for infratentorial, deep, and lobar regions achieved an over 90% overall accuracy. We made the code and atlas of Microbleed Anatomical Rating Scale regions publicly available. Graphical Abstracthttps://doi.org/10.1186/s41747-024-00544-zBrainCerebral haemorrhageCerebrovascular disordersHemosiderinMagnetic resonance imaging |
| spellingShingle | Vaanathi Sundaresan Giovanna Zamboni Robert A. Dineen Dorothee P. Auer Stamatios N. Sotiropoulos Nikola Sprigg Mark Jenkinson Ludovica Griffanti Automated characterisation of cerebral microbleeds using their size and spatial distribution on brain MRI European Radiology Experimental Brain Cerebral haemorrhage Cerebrovascular disorders Hemosiderin Magnetic resonance imaging |
| title | Automated characterisation of cerebral microbleeds using their size and spatial distribution on brain MRI |
| title_full | Automated characterisation of cerebral microbleeds using their size and spatial distribution on brain MRI |
| title_fullStr | Automated characterisation of cerebral microbleeds using their size and spatial distribution on brain MRI |
| title_full_unstemmed | Automated characterisation of cerebral microbleeds using their size and spatial distribution on brain MRI |
| title_short | Automated characterisation of cerebral microbleeds using their size and spatial distribution on brain MRI |
| title_sort | automated characterisation of cerebral microbleeds using their size and spatial distribution on brain mri |
| topic | Brain Cerebral haemorrhage Cerebrovascular disorders Hemosiderin Magnetic resonance imaging |
| url | https://doi.org/10.1186/s41747-024-00544-z |
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