Segmenting the Brain Surface From CT Images With Artifacts Using Locally Oriented Appearance and Dictionary Learning.
Segmenting the Brain Surface From CT Images With Artifacts Using Locally Oriented Appearance and Dictionary Learning.
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使用局部面向外观和词典学习将大脑表面从CT图像分割。
DOI：
10.1109/tmi.2018.2868045
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发表时间：
2019-03
影响因子：
10.6
通讯作者：
Papademetris X
中科院分区：
文献类型：
--
作者：
Onofrey JA;Staib LH;Papademetris X
术后CT图像中脑表面的准确分割对于癫痫患者的图像引导神经外科手术至关重要。颅内电极植入手术后，外科医生需要将植入后的CT图像与植入前的功能和结构MR成像准确匹配，以指导手术切除癫痫组织。执行注册的一种方法是通过表面匹配。这种设置的关键挑战是CT分割，由于颅骨的缺失部分和从电极引入的伪影，很难提取皮质表面。在本文中，我们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术后CT图像的脑表面。我们建议学习一个局部导向的外观模型，该模型既可以捕获正常组织，也可以捕获沿大脑表面边界发现的人工制品。利用临床癫痫成像数据数据库来训练和测试我们的方法，我们证明了与标准的、非定向的图像外观模型相比，我们使用局部定向图像外观的方法既能更准确地提取脑表面，也能更好地定位术后脑表面的电极。此外，我们将我们的方法与标准的基于地图集的分割方法和基于u - net的深度卷积神经网络分割方法进行了比较。
Accurate segmentation of the brain surface in post-surgical CT images is critical for image-guided neurosurgical procedures in epilepsy patients. Following surgical implantation of intra-cranial electrodes, surgeons require accurate registration of the post-implantation CT images to pre-implantation functional and structural MR imaging to guide surgical resection of epileptic tissue. One way to perform the registration is via surface matching. The key challenge in this setup is the CT segmentation, where extraction of the cortical surface is difficult due to missing parts of the skull and artifacts introduced from the electrodes. In this paper, we present a dictionary learning-based method to segment the brain surface in post-surgical CT images of epilepsy patients following surgical implantation of electrodes. We propose learning a model of locally-oriented appearance that captures both the normal tissue and the artifacts found along this brain surface boundary. Utilizing a database of clinical epilepsy imaging data to train and test our approach, we demonstrate that our method using locally-oriented image appearance both more accurately extracts the brain surface and better localizes electrodes on the post-operative brain surface compared to standard, non-oriented appearance modeling. Additionally, we compare our method to a standard atlas-based segmentation approach and to a U-Net-based deep convolutional neural network segmentation method.