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自发性脑出血后脑水肿在 CT 图像呈现的模糊边缘是 CT 图像上实现脑水肿自动分割的一个严峻挑战。在磁共振 T2加权图像上，脑水肿的边界相对清晰。因此，文章提出利用14套同时拥有磁共振和 CT 图像的病例，将其磁共振 T2加权图像的手动分割金标准通过配准映射到 CT 空间，结合 CT 图像信息通过对配准后的结果进行机器学习得到脑水肿体素分类器，并利用此分类器进行 CT 图像上的脑水肿分割。采用近邻采样策略，选择公共测度子空间进行特征选择，基于支持向量机方法利用穷举法得到分割精度最高的水肿分类器；通过36套临床脑出血的 CT 数据的验证，结果显示该方法的 Dice 系数达到0.859±0.037，明显高于基于区域增长的方法(0.789±0.036，P＜0.0001)、半自动水平集方法(0.712±0.118，P＜0.0001)和基于阈值的方法(0.649±0.147，P＜0.0001)。与之对比，使用 CT 手动分割金标准得到的分类器分割精度 Dice 系数(0.686±0.136，P＜0.0001)明显小于基于 T2金标准的分类器。试验结果显示磁共振 T2加权图像上脑水肿的清晰边界在精确区分水肿与周围正常脑组织的时候可能提供更强的约束。文章提出的方法为脑出血患者的脑水肿量化、病理改变严重性的评估、以及治疗提供潜在的工具。“,”Segmentation of cerebral edema from computed tomography (CT) scans for patients with intracr-anial hemorrhage (ICH) is challenging as edema does not show clear boundary on CT. By exploiting the clear boundary on T2-weighted magnetic resonance images, a method was proposed to segment edema on CT images through the model learned from 14 patients with both CT and T2-weighted images using ground truth edema from T2-weighted images to train and classify the features extracted on CT images. By constructing negative samples around the positive samples, employing the feature selection based on common subspace measures, and using support vector machine, the classification model was attained corresponding to the optimum segmentation accuracy. The method has been validated against 36 clinical head CT scans presenting ICH to yield a mean Dice coefficient of 0.859±0.037, which is significantly higher than that of region growing method (0.789±0.036, P<0.000 1), semi-automated level set method (0.712±0.118, P<0.000 1), and threshold based method (0.649±0.147, P<0.000 1). Comparative experiments have been carried out to find that the classifier purely from CT will yield a significantly lower Dice coefficient (0.686±0.136, P<0.000 1). The higher segmentation accuracy may suggest that clear boundaries of edema from T2-weighted images provide implicit constraints on CT images that could differentiate edema from its neighboring brain tissues more accurately. The proposed method could provide a potential tool to quantify edema, evaluate the severity of pathological changes, and guide therapy of patients with ICH.