基于CT和磁共振T2加权图像双模态分类模型的自发性脑出血后脑水肿在CT图像上的分割(英文)  

Segmentation of Cerebral Edema Around Intracranial Hemorrhage on CT Scans Through Classification Model Learned from Patients with both CT and T2-Weighted Magnetic Resonance Images

作　　者：陈明扬[1,2] 朱时才 贾富仓[1,2] 李晓东[3] Ahmed Elazab 胡庆茂[1,2] 

机构地区：[1]中国科学院深圳先进技术研究院医学图像与数字手术研究室,深圳518055 [2]中国科学院大学,北京100049 [3]山东省临沂市人民医院,临沂276000

出　　处：《集成技术》2016年第5期11-29,共19页Journal of Integration Technology

基　　金：国家973项目(2013CB733800,2013CB733803);国家自然科学基金-广东省联合基金重点项目(U1201257);深圳市技术开发项目(CXZZ20140610151856719);广东省创新团队项目(201001D0104648280)

摘　　要：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<0.000 1)、半自动水平集方法(0.712±0.118,P<0.000 1)和基于阈值的方法(0.649±0.147,P<0.000 1)。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<0.000 1)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 intracranial 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.

关 键 词：自发性脑出血 脑水肿分割 多模态分割 支持向量机 采样策略 CT 

分 类 号：R743.34[医药卫生—神经病学与精神病学] TP391.41[医药卫生—临床医学]