Automatic lesion incidence estimation and detection in multiple sclerosis using multisequence longitudinal MRI.
Automatic lesion incidence estimation and detection in multiple sclerosis using multisequence longitudinal MRI.
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DOI：
10.3174/ajnr.a3172
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发表时间：
2013-01
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通讯作者：
Crainiceanu CM
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作者：
Sweeney EM;Shinohara RT;Shea CD;Reich DS;Crainiceanu CM
检测病变的发生率和扩大是监测MS进展的关键。在临床试验中，通过手动分割和比较序列MR图像来观察病变负荷，这既耗时又昂贵，而且容易出现观察者间和观察者内的变异性。从连续的时间点减去图像会使稳定的病变为空，只留下新的病变活动。我们提出了一种自动分割入射病变体素的方法SUPRIME。我们使用Logistic回归模型结合多个磁共振成像序列和来自连续纵向研究的减影图像来估计病变发生率的体素水平概率。我们使用了来自10个受试者的110个磁共振成像研究的T1加权、T2加权、FLAIR和PD体积。为了评估模型的性能，我们将5名受试者分配到训练集，将其余5名受试者分配到验证集。利用SUBLIME，在体素水平上，病变发生率在验证集中被检测和描绘，AUC为99%(95%可信区间[97%，100%])。这种全自动化和计算快速的方法允许敏感和特定的病变发生率检测，可以应用于大型图像集合。利用统计模型的显式形式，SUBLIME可以很容易地适应可用成像序列或多或少的情况。
Detecting incidence and enlargement of lesions is essential in monitoring the progression of MS. In clinical trials, lesion load is observed by manually segmenting and comparing serial MR images, which is time consuming, costly, and prone to inter- and intraobserver variability. Subtracting images from consecutive time points nulls stable lesions, leaving only new lesion activity. We propose SuBLIME, an automated method for segmenting incident lesion voxels. We used logistic regression models incorporating multiple MR imaging sequences and subtraction images from consecutive longitudinal studies to estimate voxel-level probabilities of lesion incidence. We used T1-weighted, T2-weighted, FLAIR, and PD volumes from a total of 110 MR imaging studies from 10 subjects. To assess the performance of the model, we assigned 5 subjects to a training set and the remaining 5 to a validation set. With SuBLIME, lesion incidence is detected and delineated in the validation set with an AUC of 99% (95% CI [97%, 100%]) at the voxel level. This fully automated and computationally fast method allows sensitive and specific detection of lesion incidence that can be applied to large collections of images. Using the explicit form of the statistical model, SuBLIME can easily be adapted to cases when more or fewer imaging sequences are available.