Automated delineation of stroke lesions using brain CT images
Computed tomographic (CT) images are widely used for the identification of abnormal brain tissue following infarct and hemorrhage in stroke. Manual lesion delineation is currently the standard approach, but is both time-consuming and operator-dependent. To address these issues, we present a method t...
| Main Authors: | , , |
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| Format: | Journal article |
| Language: | English |
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Elsevier
2014
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| Subjects: |
| _version_ | 1797101611437260800 |
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| author | Gillebert, C Humphreys, G Mantini, D |
| author_facet | Gillebert, C Humphreys, G Mantini, D |
| author_sort | Gillebert, C |
| collection | OXFORD |
| description | Computed tomographic (CT) images are widely used for the identification of abnormal brain tissue following infarct and hemorrhage in stroke. Manual lesion delineation is currently the standard approach, but is both time-consuming and operator-dependent. To address these issues, we present a method that can automatically delineate infarct and hemorrhage in stroke CT images. The key elements of this method are the accurate normalization of CT images from stroke patients into template space and the subsequent voxelwise comparison with a group of control CT images for defining areas with hypo- or hyper-intense signals. Our validation, using simulated and actual lesions, shows that our approach is effective in reconstructing lesions resulting from both infarct and hemorrhage and yields lesion maps spatially consistent with those produced manually by expert operators. A limitation is that, relative to manual delineation, there is reduced sensitivity of the automated method in regions close to the ventricles and the brain contours. However, the automated method presents a number of benefits in terms of offering significant time savings and the elimination of the inter-operator differences inherent to manual tracing approaches. These factors are relevant for the creation of large-scale lesion databases for neuropsychological research. The automated delineation of stroke lesions from CT scans may also enable longitudinal studies to quantify changes in damaged tissue in an objective and reproducible manner. |
| first_indexed | 2024-03-07T05:54:20Z |
| format | Journal article |
| id | oxford-uuid:e9fce84a-64bf-4b11-a94a-dff91ab5b04b |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T05:54:20Z |
| publishDate | 2014 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | oxford-uuid:e9fce84a-64bf-4b11-a94a-dff91ab5b04b2022-03-27T10:58:24ZAutomated delineation of stroke lesions using brain CT imagesJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:e9fce84a-64bf-4b11-a94a-dff91ab5b04bImage understandingStrokeComputational NeuroscienceEnglishSymplectic Elements at OxfordElsevier2014Gillebert, CHumphreys, GMantini, DComputed tomographic (CT) images are widely used for the identification of abnormal brain tissue following infarct and hemorrhage in stroke. Manual lesion delineation is currently the standard approach, but is both time-consuming and operator-dependent. To address these issues, we present a method that can automatically delineate infarct and hemorrhage in stroke CT images. The key elements of this method are the accurate normalization of CT images from stroke patients into template space and the subsequent voxelwise comparison with a group of control CT images for defining areas with hypo- or hyper-intense signals. Our validation, using simulated and actual lesions, shows that our approach is effective in reconstructing lesions resulting from both infarct and hemorrhage and yields lesion maps spatially consistent with those produced manually by expert operators. A limitation is that, relative to manual delineation, there is reduced sensitivity of the automated method in regions close to the ventricles and the brain contours. However, the automated method presents a number of benefits in terms of offering significant time savings and the elimination of the inter-operator differences inherent to manual tracing approaches. These factors are relevant for the creation of large-scale lesion databases for neuropsychological research. The automated delineation of stroke lesions from CT scans may also enable longitudinal studies to quantify changes in damaged tissue in an objective and reproducible manner. |
| spellingShingle | Image understanding Stroke Computational Neuroscience Gillebert, C Humphreys, G Mantini, D Automated delineation of stroke lesions using brain CT images |
| title | Automated delineation of stroke lesions using brain CT images |
| title_full | Automated delineation of stroke lesions using brain CT images |
| title_fullStr | Automated delineation of stroke lesions using brain CT images |
| title_full_unstemmed | Automated delineation of stroke lesions using brain CT images |
| title_short | Automated delineation of stroke lesions using brain CT images |
| title_sort | automated delineation of stroke lesions using brain ct images |
| topic | Image understanding Stroke Computational Neuroscience |
| work_keys_str_mv | AT gillebertc automateddelineationofstrokelesionsusingbrainctimages AT humphreysg automateddelineationofstrokelesionsusingbrainctimages AT mantinid automateddelineationofstrokelesionsusingbrainctimages |