School of Computer Science and Engineering, Nanjing University of Science and Technology, Xiaolingwei 200, Nanjing 210094, China.
Jiangsu Key Lab of Spectral Imaging and Intelligent Sensing, Nanjing 210094, China.
CNS Neurol Disord Drug Targets. 2017;16(2):150-159. doi: 10.2174/1871527315666161220145247.
Brain lesions, especially White Matter Lesions (WMLs) that mostly found on magnetic resonance images of elderly people, are not only associated with normal aging, but also with various geriatric disorders including cardiovascular diseases, vascular disease, psychiatric disorders and dementia. Quantitative analysis of WMLs in large clinical trials is crucial in scientific investigations of such neurological diseases as well as in studying aging processes. Exploiting the different appearances of WMLs in multiple modalities, we propose a novel coarse classification to region-scalable refining method to segment WMLs in Magnetic Resonance Imaging (MRI) sequences without user intervention. Specifically, a nonlinear voxel-wise classifier is trained based on intensity features extracted from multimodality MRI sequences, and tissues' probabilistic prior provided by partial volume estimate images in native space. By considering the prior that the WMLs almost exist in white matter, a rejection algorithm is then used to eliminate the false-positive labels from the initial coarse classification. To further segment precise lesions boundary and detect missing lesions, a region-scalable refining is finally employed to effectively segment the WMLs based on the previous initial contour. Compared with the manual segmentation results from an experienced neuroradiologist, the segmentations for real images of our proposal show desirable performances and high accuracy and provide competitive solution with stateof- the-art methods.
脑损伤，特别是在老年人磁共振图像上发现的主要为脑白质病变（White Matter Lesions，WMLs），不仅与正常衰老有关，而且还与各种老年疾病有关，包括心血管疾病、血管疾病、精神疾病和痴呆症。在大型临床试验中对 WML 进行定量分析对于研究此类神经疾病以及研究衰老过程至关重要。我们利用 WML 在多种模态下的不同表现，提出了一种新的粗分类到区域可扩展细化方法，无需用户干预即可对磁共振成像（Magnetic Resonance Imaging，MRI）序列中的 WML 进行分割。具体来说，基于从多模态 MRI 序列中提取的强度特征和以体素为单位的概率先验图（由局部体积估计图像在原始空间中提供），训练非线性体素分类器。通过考虑 WML 几乎存在于白质中的先验知识，然后使用拒绝算法从初始粗分类中消除假阳性标签。为了进一步分割精确的病变边界和检测缺失的病变，最后采用区域可扩展细化方法，根据先前的初始轮廓有效地分割 WML。与经验丰富的神经放射科医生的手动分割结果相比，我们提出的方法对真实图像的分割表现出令人满意的性能和高精度，并提供了具有最新方法的有竞争力的解决方案。
