title: Automated delineation of stroke lesions using brain CT images.
abstract: 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.
authors.affiliation: Department of Experimental Psychology, University of Oxford, Oxford, UK.
authors.forename: Céline R
authors.initials: CR
authors.lastname: Gillebert
authors.affiliation: Department of Experimental Psychology, University of Oxford, Oxford, UK.
authors.forename: Glyn W
authors.initials: GW
authors.lastname: Humphreys
authors.affiliation: Department of Experimental Psychology, University of Oxford, Oxford, UK ; Department of Health Sciences and Technology, ETH Zürich, Switzerland.
authors.forename: Dante
authors.initials: D
authors.lastname: Mantini
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001921:Brain / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012984:Software; D020521:Stroke / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed / Q000379:methods*
keywords: Computerized tomography; Lesion segmentation; Medical imaging; Software tool; Stroke