Dewey Blake E, Carass Aaron, Blitz Ari M, Prince Jerry L
Dept. of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA.
Dept. of Computer Science, The Johns Hopkins University, Baltimore, MD 21218, USA.
Proc SPIE Int Soc Opt Eng. 2017 Feb;10137. doi: 10.1117/12.2256147. Epub 2017 Mar 13.
Multi-atlas label fusion is an accurate but time-consuming method of labeling the human brain. Using an intermediate image as a registration target can allow researchers to reduce time constraints by storing the deformations required of the atlas images. In this paper, we investigate the effect of registration through an intermediate template image on multi-atlas label fusion and propose a novel registration technique to counteract the negative effects of through-template registration. We show that overall computation time can be decreased dramatically with minimal impact on final label accuracy and time can be exchanged for improved results in a predictable manner. We see almost complete recovery of Dice similarity over a simple through-template registration using the corrected method and still maintain a 3-4 times speed increase. Further, we evaluate the effectiveness of this method on brains of patients with normal-pressure hydrocephalus, where abnormal brain shape presents labeling difficulties, specifically the ventricular labels. Our correction method creates substantially better ventricular labeling than traditional methods and maintains the speed increase seen in healthy subjects.
多图谱标签融合是一种用于标记人类大脑的准确但耗时的方法。使用中间图像作为配准目标可以让研究人员通过存储图谱图像所需的变形来减少时间限制。在本文中，我们研究了通过中间模板图像进行配准对多图谱标签融合的影响，并提出了一种新颖的配准技术来抵消通过模板配准的负面影响。我们表明，总体计算时间可以大幅减少，同时对最终标签准确性的影响最小，并且可以以可预测的方式用时间换取更好的结果。通过使用校正后的方法，我们看到与简单的通过模板配准相比，骰子相似度几乎完全恢复，并且仍然保持3到4倍的速度提升。此外，我们评估了该方法在正常压力脑积水患者大脑上的有效性，在这些患者中，异常的大脑形状给标记带来了困难，特别是脑室标记。我们的校正方法比传统方法创建的脑室标记要好得多，并且保持了在健康受试者中看到的速度提升。
