Anchor-Based Segmentation of Periventricular White Matter for Neonatal HIE

The segmentation and quantitative features of periventricular white matter injury in magnetic resonance imaging are helpful for determining an accurate diagnosis of neonatal hypoxia ischemia encephalopathy (HIE) in clinical practice, but they are tedious tasks. In this paper, we present an interactive segmentation method for clinical magnetic resonance imaging (MRI) using sample estimation at the subvoxel level to establish a quantitative diagnosis. The coarse-grained voxels in clinical MRI are divided into subvoxels, and the intensities of subvoxels are adjusted using gradient adaptive filtering. Because the cerebrospinal fluid is more obvious in grayscale than the white matter and gray matter on T1-weighted images (T1WI), T2-weighted images (T2WI) and fluid-attenuated inversion recovery (FLAIR) images, and its population variance follows a Chi-square distribution, we segment the periventricular region as an anchor by estimating the parameters of the selected data and expand the anchor to obtain periventricular white matter of various widths. After aligning the segmented periventricular white matter in various modalities, we compute the radiomics features of regions of interest for the quantitative diagnosis of HIE. Current clinical practice confirms that our approach is effective and satisfies the clinical diagnostic requirements for neonatal HIE.