Automatic localization of landmark sets in head CT images with regression forests for image registration initialization.
Automatic localization of landmark sets in head CT images with regression forests for image registration initialization.
复制标题
使用回归森林自动定位头部 CT 图像中的地标集，以进行图像配准初始化。
DOI：
10.1117/12.2216925
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Dawant,BenoitM
中科院分区：
文献类型：
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作者：
Zhang,Dongqing;Liu,Yuan;Noble,JackH;Dawant,BenoitM
耳蜗植入体（CI）是通过手术插入耳蜗的电极阵列。个别接触刺激频率映射的神经末梢，从而取代自然的机电转换机制。CI由听力学家在术后编程，但目前这是使用行为测试完成的，没有成像信息，允许将电极位置与内耳解剖结构相关联。我们最近开发了一系列的图像处理步骤，允许内耳解剖结构的分割和个人接触的本地化。我们提出了一个新的编程策略，使用这些信息，我们已经表明，在一项研究中，68名参与者的长期收件人的78%首选的编程参数确定与此新的战略。我们的技术的大规模评估和部署的一个限制因素是在我们的图像处理算法序列中使用的一些步骤中仍然需要用户交互的量。一个这样的步骤是当目标体积具有非常不同的视场和取向时，在使用自动化的基于强度的算法之前将图谱粗略配准到目标体积。在本文中，我们提出了一个解决这个问题。它依赖于一种基于随机森林的方法来自动定位一系列地标。我们从83张图像与132个配准任务中获得的结果表明，基于强度的算法的自动初始化被证明是一种可靠的技术，以取代手动步骤。
Cochlear Implants (CIs) are electrode arrays that are surgically inserted into the cochlea. Individual contacts stimulate frequency-mapped nerve endings thus replacing the natural electro-mechanical transduction mechanism. CIs are programmed post-operatively by audiologists but this is currently done using behavioral tests without imaging information that permits relating electrode position to inner ear anatomy. We have recently developed a series of image processing steps that permit the segmentation of the inner ear anatomy and the localization of individual contacts. We have proposed a new programming strategy that uses this information and we have shown in a study with 68 participants that 78% of long term recipients preferred the programming parameters determined with this new strategy. A limiting factor to the large scale evaluation and deployment of our technique is the amount of user interaction still required in some of the steps used in our sequence of image processing algorithms. One such step is the rough registration of an atlas to target volumes prior to the use of automated intensity-based algorithms when the target volumes have very different fields of view and orientations. In this paper we propose a solution to this problem. It relies on a random forest-based approach to automatically localize a series of landmarks. Our results obtained from 83 images with 132 registration tasks show that automatic initialization of an intensity-based algorithm proves to be a reliable technique to replace the manual step.