Segmenting the Brain Surface from CT Images with Artifacts Using Locally-Oriented Appearance and Dictionary Learning.

Accurate segmentation of the brain surface in postsurgical 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.