Segmenting the Brain Surface from CT Images with Artifacts Using Dictionary Learning for Non-rigid MR-CT Registration.
Segmenting the Brain Surface from CT Images with Artifacts Using Dictionary Learning for Non-rigid MR-CT Registration.
复制标题
使用字典学习从具有伪影的 CT 图像中分割大脑表面以进行非刚性 MR-CT 配准。
DOI：
10.1007/978-3-319-19992-4_52
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Papademetris,Xenophon
中科院分区：
文献类型：
--
作者：
Onofrey,JohnA;Staib,LawrenceH;Papademetris,Xenophon
本文提出了一种基于字典学习的方法来分割癫痫患者手术植入电极后的手术后CT图像中的脑表面。使用植入后CT中识别的电极，外科医生需要与植入前功能和结构MR成像准确配准，以指导癫痫组织的手术切除。在这项工作中，我们使用基于表面的配准方法来对齐MR和CT大脑表面。这里的关键挑战不是配准，而是从CT图像中提取皮质表面，其中包括颅骨的缺失部分和电极引入的伪影。为了从这些图像中分割大脑，我们提出学习一个外观模型，该模型捕获正常组织和沿沿着该大脑表面边界发现的伪影。使用临床数据，我们证明了我们的方法既准确地提取大脑表面，更好地定位电极比基于强度的刚性和非刚性配准方法。
This paper presents a dictionary learning-based method to segment the brain surface in post-surgical CT images of epilepsy patients following surgical implantation of electrodes. Using the electrodes identified in the post-implantation CT, surgeons require accurate registration with pre-implantation functional and structural MR imaging to guide surgical resection of epileptic tissue. In this work, we use a surface-based registration method to align the MR and CT brain surfaces. The key challenge here is not the registration, but rather the extraction of the cortical surface from the CT image, which includes missing parts of the skull and artifacts introduced by the electrodes. To segment the brain from these images, we propose learning a model of appearance that captures both the normal tissue and the artifacts found along this brain surface boundary. Using clinical data, we demonstrate that our method both accurately extracts the brain surface and better localizes electrodes than intensity-based rigid and non-rigid registration methods.