集成技术2016，Vol.5Issue(5)：11-29,19.
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
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
摘要
Abstract
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.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/CTKey words
intracranial hemorrhage/edema segmentation/multimodal segmentation/support vector machine/sampling strategy/computed tomography分类
信息技术与安全科学引用本文复制引用
陈明扬,朱时才,贾富仓,李晓东,AhmedElazab,胡庆茂..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[J].集成技术,2016,5(5):11-29,19.基金项目
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) （2013CB733800、2013CB733803）
