Trimmed-Likelihood Estimation for Focal Lesions and Tissue Segmentation in Multisequence MRI for Multiple Sclerosis
Trimmed-Likelihood Estimation for Focal Lesions and Tissue Segmentation in Multisequence MRI for Multiple Sclerosis
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DOI：
10.1109/tmi.2011.2114671
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发表时间：
2011-08-01
影响因子：
10.6
通讯作者：
Barillot, Christian
中科院分区：
文献类型：
--
作者：
Garcia-Lorenzo, Daniel;Prima, Sylvain;Barillot, Christian
我们提出了一种新的自动分割方法的多发性硬化症（MS）病变的磁共振图像。该方法使用正常出现的脑组织的强度的模型来执行组织分类。为了估计模型，使用分层随机方法初始化修剪似然估计器，以便对真实的图像中存在的MS病变和其他离群值具有鲁棒性。该算法首先与模拟图像进行评估，以评估存在离群值的鲁棒估计的重要性。然后，使用临床数据验证该方法，其中MS病变由几位专家手动划定。我们的方法获得的平均骰子相似系数（DSC）为0.65，这是接近的平均DSC评分（0.66）。
We present a new automatic method for segmentation of multiple sclerosis (MS) lesions in magnetic resonance images. The method performs tissue classification using a model of intensities of the normal appearing brain tissues. In order to estimate the model, a trimmed likelihood estimator is initialized with a hierarchical random approach in order to be robust to MS lesions and other outliers present in real images. The algorithm is first evaluated with simulated images to assess the importance of the robust estimator in presence of outliers. The method is then validated using clinical data in which MS lesions were delineated manually by several experts. Our method obtains an average Dice similarity coefficient (DSC) of 0.65, which is close to the average DSC obtained by raters (0.66).