在深度神经网络中融合多种输入描述符与监督器以进行纤维束图过滤
Merging multiple input descriptors and supervisors in a deep neural network for tractogram filtering
AI总结：
针对纤维束成像假阳性率高的问题，提出融合四种过滤监督器标签并整合多种流线信息的深度神经网络，实现流线三分类，发现坐标与扩散数据最关键。
摘要：
当前纤维束成像方法的主要问题之一是其较高的假阳性率。纤维束图过滤是一种在后处理步骤中从纤维束成像数据中去除假阳性流线的可选方案。本文训练了一个用于过滤纤维束成像数据的深度神经网络，将纤维束图中的每条流线分类为“合理”“不合理”或“不确定”。为此，采用四种不同的纤维束图过滤策略作为监督器：TractQuerier、RecobundlesX、TractSeg以及一种受解剖学启发的过滤器；它们的输出被结合起来以获得流线的分类标签。研究评估了沿流线的不同类型信息对执行该分类任务的重要性，包括流线坐标、扩散数据、标志点、T1加权信息和脑分区。结果发现，在这一特定分类任务中，流线坐标最为相关，其次是扩散数据。
英文摘要：
One of the main issues of the current tractography methods is their high false-positive rate. Tractogram filtering is an option to remove false-positive streamlines from tractography data in a post-processing step. In this paper, we train a deep neural network for filtering tractography data in which every streamline of a tractogram is classified as {\em plausible, implausible}, or {\em inconclusive}. For this, we use four different tractogram filtering strategies as supervisors: TractQuerier, RecobundlesX, TractSeg, and an anatomy-inspired filter. Their outputs are combined to obtain the classification labels for the streamlines. We assessed the importance of different types of information along the streamlines for performing this classification task, including the coordinates of the streamlines, diffusion data, landmarks, T1-weighted information, and a brain parcellation. We found that the streamline coordinates are the most relevant followed by the diffusion data in this particular classification task.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
