Automatic segmentation methods for low-field neonatal brain magnetic resonance images

Low-field magnetic resonance imaging (MRI) technology is a safe and non-invasive examination technique for neonates.However,the brain MRI images generated by this technique have the problems of insufficient contrast and large slice thickness,making segmentation and analysis challenging.In this paper,we propose a fast and accurate method for automatic segmentation of low-field neonate brain MRI images,which includes super-resolution reconstruction,brain extraction,and brain tissue segmentation.Firstly,we preprocess data using N4 bias field correction and the largest connected component method.Then,an improved SMORE-based deep learning method is implemented for super-resolution reconstruction of low-field MRI images,addressing the limitations of the original SMORE single-subject singlemodel.Next,we use the nnU-Net self-configuring method with a mis-segmentation loss function for brain extraction so that the network pays more attention to mis-segmentation regions.Finally,a HyperDense-Net method based on a voxel-level contrastive learning loss is adopted for brain tissue segmentation,which enhances the segmentation ability of the network for tissue edges.In this work,the training was carried out on low-field 0.35T magnetic resonance images of neonatal brains.A Dice coefficient of 0.9785 for the brain extraction task and an average Dice coefficient of 0.9405 for the brain tissue segmentation task are achieved.The experimental results demonstrate that the proposed method can effectively improve the segmentation accuracy of low-field magnetic resonance images of neonatal brains.