Liu Luoluo, Glaister Jeffrey, Sun Xiaoxia, Carass Aaron, Tran Trac D, Prince Jerry L
Dept. of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.
Dept. of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA; Dept. of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA.
Proc SPIE Int Soc Opt Eng. 2016 Feb 27;9784. doi: 10.1117/12.2214206. Epub 2016 Mar 21.
Automatic thalamus segmentation is useful to track changes in thalamic volume over time. In this work, we introduce a task-driven dictionary learning framework to find the optimal dictionary given a set of eleven features obtained from T1-weighted MRI and diffusion tensor imaging. In this dictionary learning framework, a linear classifier is designed concurrently to classify voxels as belonging to the thalamus or non-thalamus class. Morphological post-processing is applied to produce the final thalamus segmentation. Due to the uneven size of the training data samples for the non-thalamus and thalamus classes, a non-uniform sampling scheme is proposed to train the classifier to better discriminate between the two classes around the boundary of the thalamus. Experiments are conducted on data collected from 22 subjects with manually delineated ground truth. The experimental results are promising in terms of improvements in the Dice coefficient of the thalamus segmentation over state-of-the-art atlas-based thalamus segmentation algorithms.
自动丘脑分割有助于追踪丘脑体积随时间的变化。在这项工作中，我们引入了一个任务驱动的字典学习框架，以根据从T1加权磁共振成像（MRI）和扩散张量成像获得的一组十一个特征找到最优字典。在这个字典学习框架中，同时设计了一个线性分类器，将体素分类为属于丘脑或非丘脑类别。应用形态学后处理来生成最终的丘脑分割结果。由于非丘脑和丘脑类别的训练数据样本大小不均匀，提出了一种非均匀采样方案来训练分类器，以便在丘脑边界周围更好地区分这两类。对从22名受试者收集的数据进行了实验，这些数据具有手动描绘的真实情况。就丘脑分割的骰子系数相对于基于图谱的最新丘脑分割算法的改进而言，实验结果很有前景。
