Zacharaki Evangelia I, Kanterakis Stathis, Bryan R Nick, Davatzikos Christos
Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA.
Med Image Comput Comput Assist Interv. 2008;11(Pt 1):620-7. doi: 10.1007/978-3-540-85988-8_74.
Brain lesions, especially White Matter Lesions (WMLs), are associated with cardiac and vascular disease, but also with normal aging. Quantitative analysis of WML in large clinical trials is becoming more and more important. In this paper, we present a computer-assisted WML segmentation method, based on local features extracted from conventional multi-parametric Magnetic Resonance Imaging (MRI) sequences. A framework for preprocessing the temporal data by jointly equalizing histograms reduces the spatial and temporal variance of data, thereby improving the longitudinal stability of such measurements and hence the estimate of lesion progression. A Support Vector Machine (SVM) classifier trained on expert-defined WML's is applied for lesion segmentation on each scan using the AdaBoost algorithm. Validation on a population of 23 patients from 3 different imaging sites with follow-up studies and WMLs of varying sizes, shapes and locations tests the robustness and accuracy of the proposed segmentation method, compared to the manual segmentation results from an experienced neuroradiologist. The results show that our CAD-system achieves consistent lesion segmentation in the 4D data facilitating the disease monitoring.
脑损伤，尤其是白质损伤（WMLs），与心脏和血管疾病相关，但也与正常衰老有关。在大型临床试验中对WML进行定量分析变得越来越重要。在本文中，我们提出了一种基于从传统多参数磁共振成像（MRI）序列中提取的局部特征的计算机辅助WML分割方法。通过联合均衡直方图对时间数据进行预处理的框架减少了数据的空间和时间方差，从而提高了此类测量的纵向稳定性，进而提高了病变进展的估计。使用AdaBoost算法，在专家定义的WML上训练的支持向量机（SVM）分类器应用于每次扫描的病变分割。在来自3个不同成像部位的23名患者群体上进行验证，并进行随访研究以及对大小、形状和位置各异的WML进行测试，与经验丰富的神经放射科医生的手动分割结果相比，检验了所提出分割方法的稳健性和准确性。结果表明，我们的CAD系统在4D数据中实现了一致的病变分割，便于疾病监测。
