Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA.
Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA; Department of Computer Science, The Johns Hopkins University, Baltimore, MD 21218, USA.
Neuroimage. 2017 Sep;158:430-440. doi: 10.1016/j.neuroimage.2017.06.047. Epub 2017 Jun 29.
Automatic segmentation of the thalamus can be used to measure differences and track changes in thalamic volume that may occur due to disease, injury or normal aging. An automatic thalamus segmentation algorithm incorporating features from diffusion tensor imaging (DTI) and thalamus priors constructed from multiple atlases is proposed. Multiple atlases with corresponding manual thalamus segmentations are registered to the target image and averaged to generate the thalamus prior. At each voxel in a region of interest around the thalamus, a multidimensional feature vector that includes the thalamus prior as well as a set of DTI features, including fractional anisotropy, mean diffusivity, and fiber orientation is formed. A random forest is trained to classify each voxel as belonging to the thalamus or background within the region of interest. Using a leave-one-out cross-validation on nine subjects, the proposed algorithm achieves a mean Dice score of 0.878 and 0.890 for the left and right thalami, respectively, which are higher Dice scores than the three state-of-art methods we compared to. We demonstrate the utility of the method with a pilot study exploring the difference in the thalamus fraction between 21 multiple sclerosis (MS) patients and 21 age-matched healthy controls. The left and right thalamic volumes (normalized by intracranial volumes) are larger in healthy controls by 7.6% and 7.3% respectively, compared to MS patients (though neither result is statistically significant).
自动分割丘脑可用于测量因疾病、损伤或正常衰老而导致的丘脑体积差异和变化。本文提出了一种融合弥散张量成像（DTI）特征和由多个图谱构建的丘脑先验的自动丘脑分割算法。将具有相应手动丘脑分割的多个图谱配准到目标图像并平均化以生成丘脑先验。在丘脑周围感兴趣区域的每个体素中，形成一个多维特征向量，该向量包括丘脑先验以及一组 DTI 特征，包括各向异性分数、平均扩散率和纤维方向。随机森林用于对感兴趣区域内的每个体素进行分类，将其归类为丘脑或背景。在九个受试者的留一交叉验证中，所提出的算法分别获得了左右丘脑的平均 Dice 评分 0.878 和 0.890，高于我们比较的三种最先进方法的 Dice 评分。我们通过一项探索 21 名多发性硬化症（MS）患者和 21 名年龄匹配的健康对照者之间丘脑分数差异的试点研究来展示该方法的效用。与 MS 患者相比，健康对照组的左右丘脑体积（通过颅内体积标准化）分别增加了 7.6%和 7.3%，尽管这两个结果均无统计学意义。
