Asclepios Research Project, INRIA Sophia-Antipolis, France.
Neuroimage. 2011 Jul 15;57(2):378-90. doi: 10.1016/j.neuroimage.2011.03.080. Epub 2011 Apr 8.
A new algorithm is presented for the automatic segmentation of Multiple Sclerosis (MS) lesions in 3D Magnetic Resonance (MR) images. It builds on a discriminative random decision forest framework to provide a voxel-wise probabilistic classification of the volume. The method uses multi-channel MR intensities (T1, T2, and FLAIR), knowledge on tissue classes and long-range spatial context to discriminate lesions from background. A symmetry feature is introduced accounting for the fact that some MS lesions tend to develop in an asymmetric way. Quantitative evaluation of the proposed methods is carried out on publicly available labeled cases from the MICCAI MS Lesion Segmentation Challenge 2008 dataset. When tested on the same data, the presented method compares favorably to all earlier methods. In an a posteriori analysis, we show how selected features during classification can be ranked according to their discriminative power and reveal the most important ones.
提出了一种新的算法，用于自动分割 3D 磁共振（MR）图像中的多发性硬化症（MS）病变。它基于判别随机决策森林框架，为体积提供了基于体素的概率分类。该方法使用多通道 MR 强度（T1、T2 和 FLAIR）、组织分类知识和远程空间上下文来区分病变与背景。引入了一种对称特征，以说明某些 MS 病变倾向于以不对称的方式发展。使用来自 MICCAI MS 病变分割挑战 2008 数据集的公开标记案例对所提出的方法进行了定量评估。在对同一数据进行测试时，所提出的方法与所有早期方法相比具有优势。在后验分析中，我们展示了如何根据分类过程中的判别能力对选定的特征进行排序，并揭示最重要的特征。
