Segmenting the Brain Surface from CT Images with Artifacts Using Dictionary Learning for Non-rigid MR-CT Registration.

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.