Geremia Ezequiel, Menze Bjoern H, Clatz Olivier, Konukoglu Ender, Criminisi Antonio, Ayache Nicholas
Asclepios Research Project, INRIA Sophia-Antipolis, France.
Med Image Comput Comput Assist Interv. 2010;13(Pt 1):111-8. doi: 10.1007/978-3-642-15705-9_14.
A new algorithm is presented for the automatic segmentation of Multiple Sclerosis (MS) lesions in 3D MR images. It builds on the discriminative random decision forest framework to provide a voxel-wise probabilistic classification of the volume. Our method uses multi-channel MIR intensities (T1, T2, Flair), spatial prior and long-range comparisons with 3D regions to discriminate lesions. A symmetry feature is introduced accounting for the fact that some MS lesions tend to develop in an asymmetric way. Quantitative evaluation of the data is carried out on publicly available labeled cases from the MS Lesion Segmentation Challenge 2008 dataset and demonstrates improved results over the state of the art.
提出了一种用于自动分割三维磁共振图像中多发性硬化（MS）病变的新算法。该算法基于判别式随机决策森林框架，对体素进行逐点概率分类。我们的方法使用多通道磁共振成像强度（T1、T2、液体衰减反转恢复序列（FLAIR））、空间先验以及与三维区域的远距离比较来区分病变。引入了一种对称特征，以考虑到一些MS病变倾向于不对称发展这一事实。利用2008年MS病变分割挑战赛数据集中公开的标记病例对数据进行了定量评估，结果表明该算法比现有技术有了改进。
