IEEE Trans Med Imaging. 2018 Jul;37(7):1678-1689. doi: 10.1109/TMI.2018.2794918.
When analyzing brain tumors, two tasks are intrinsically linked, spatial localization, and physiological characterization of the lesioned tissues. Automated data-driven solutions exist, based on image segmentation techniques or physiological parameters analysis, but for each task separately, the other being performedmanually or with user tuning operations. In this paper, the availability of quantitative magnetic resonance (MR) parameters is combined with advancedmultivariate statistical tools to design a fully automated method that jointly performs both localization and characterization. Non trivial interactions between relevant physiologicalparameters are capturedthanks to recent generalized Student distributions that provide a larger variety of distributional shapes compared to the more standard Gaussian distributions. Probabilisticmixtures of the former distributions are then consideredto account for the different tissue types and potential heterogeneity of lesions. Discriminative multivariate features are extracted from this mixture modeling and turned into individual lesion signatures. The signatures are subsequently pooled together to build a statistical fingerprintmodel of the different lesion types that captures lesion characteristics while accounting for inter-subject variability. The potential of this generic procedure is demonstrated on a data set of 53 rats, with 36 rats bearing 4 different brain tumors, for which 5 quantitative MR parameters were acquired.
在分析脑肿瘤时，两个任务是内在相关的，即病变组织的空间定位和生理特征化。存在基于图像分割技术或生理参数分析的自动化数据驱动解决方案，但对于每个任务，另一个任务都是手动执行或通过用户调整操作完成。在本文中，定量磁共振（MR）参数的可用性与先进的多元统计工具相结合，设计了一种全自动方法，该方法可以联合执行定位和特征化。由于最近的广义学生分布提供了比更标准的高斯分布更多样化的分布形状，因此可以捕捉到相关生理参数之间的非平凡相互作用。然后考虑这些前分布的概率混合，以解释不同的组织类型和病变的潜在异质性。从这种混合模型中提取出判别性多元特征，并将其转化为单个病变特征。随后，这些特征被汇集在一起，以建立不同病变类型的统计指纹模型，该模型在考虑个体间变异性的同时捕获病变特征。在一组 53 只大鼠的数据上，对这种通用程序的潜力进行了验证，其中 36 只大鼠患有 4 种不同的脑肿瘤，共采集了 5 个定量 MR 参数。
