Gillebert Céline R, Humphreys Glyn W, Mantini Dante
Department of Experimental Psychology, University of Oxford, Oxford, UK.
Department of Experimental Psychology, University of Oxford, Oxford, UK ; Department of Health Sciences and Technology, ETH Zürich, Switzerland.
Neuroimage Clin. 2014 Mar 21;4:540-8. doi: 10.1016/j.nicl.2014.03.009. eCollection 2014.
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.
计算机断层扫描（CT）图像被广泛用于识别中风后梗死和出血导致的异常脑组织。目前手动勾勒病变是标准方法，但既耗时又依赖操作人员。为了解决这些问题，我们提出了一种能够自动勾勒中风CT图像中梗死和出血区域的方法。该方法的关键要素是将中风患者的CT图像准确归一化到模板空间，并随后与一组对照CT图像进行逐体素比较，以确定信号强度减低或增高的区域。我们使用模拟病变和实际病变进行的验证表明，我们的方法在重建梗死和出血导致的病变方面是有效的，并且生成的病变图在空间上与专家操作人员手动生成的病变图一致。一个局限性是，相对于手动勾勒，自动方法在靠近脑室和脑轮廓的区域灵敏度降低。然而，自动方法在节省大量时间以及消除手动追踪方法固有的操作人员间差异方面具有诸多优势。这些因素对于创建用于神经心理学研究的大规模病变数据库具有重要意义。从CT扫描中自动勾勒中风病变还可能使纵向研究能够以客观且可重复的方式量化受损组织的变化。
