Skull and scalp segmentation in neonatal cerebral MRI using subject-specific probability models

Abstract

AbstractThis study presents a new approach for the segmentation of cranial bones in magnetic resonance images (MRIs) acquired from neonates in the age range of 39 to 42 weeks, gestational age. This approach uses subject specific probability maps of the skull and scalp, which are created from atlas computed tomography (CT) images taken retrospectively from neonates in the same age range. Our method uses also a subject specific probability map of cerebrospinal fluid (CSF), which is constructed from retrospective atlas MRIs. To construct skull, scalp and CSF probability maps, a subject specific bimodal MR-CT neonatal head template is created (using atlas MR and CT images), and employed. In the next step, the subject specific probability maps are fed to expectation maximization algorithm in conjunction with Markov random field method implemented in FSL software to segment skull and scalp from the input MR image. The results of the proposed method were evaluated through various experiments. First, we computed the similarity between frontal and occipital sutures (reconstructed from segmented cranial bones) and the ground truth (created manually by an expert radiologist). For this purpose, modified versions of Dice similarity index (DSI) were adopted and used. Second, the size of anterior fontanel was compared to its normal size as reported for the neonates in the same age range. Third, the thickness of cranial bones was computed and compared to its normal values as reported for healthy neonates. Finally, a retrospective data including MRI and CT images was used which have been acquired from the same neonate within a short time interval. After aligning the two images, the similarity between cranial bones of the MR and CT image was compared using DSI and modified Hausdorff distance. The results of these experiments demonstrated the success of our segmentation method.