Multi-regional Segmentation Of The Neonatal Brain Based On The Self-attention Mechanism Of Shifted Windows

Magnetic Resonance Imaging(MRI)is increasingly used by clinicians to evaluate and care for newborns with suspected brain injury.It provides detailed anatomical information about the neonatal brain,compensates for the function of ultrasound in screening and diagnosing brain disorders,and can accurately segment brain regions such as white matter,gray matter and cerebrospinal fluid,which is important for quantitative studies of early neonatal brain development.Especially in preterm infants,the brain is often different from that of full-term infants due to their abnormal intrauterine development time.At this time,the segmentation results of functional brain regions can assist physicians in more accurately assessing and diagnosing the brain development of preterm infants,as well as giving accurate treatment methods and reducing the probability of sequelae.Traditionally,radiologists typically sketch brain structures by manually labeling them,which is time-consuming and difficult to meet practical requirements.Over the past decade or so,deep learning has evolved iteratively and has attracted a lot of attention in the medical field.To improve measurement efficiency and accuracy,automatic segmentation of medical images using deep learning techniques has become a growing pursuit for researchers.Many methods have been proposed for automatic image segmentation,but there is still room for further improvement in the accuracy of traditional convolutional neural network segmentation.Meanwhile,for neonatal brain region segmentation,MRI images of neonatal brain show low signal-to-noise ratio,low tissue contrast,partial volume effect and brightness inversion between white matter compared with adult brain,which poses a serious challenge to the accurate study of neonatal brain structure.The great success of Transformer in NLP has inspired research to use it for other tasks.Vision Transformer(VIT)was first used as a pure Transformer architecture for vision tasks,however,it suffers from compatibility issues with high resolution images,which is not friendly for image processing tasks and raises the image processing hardware threshold for image processing tasks.To solve this problem,Swin Transformer was proposed,which is arguably one of the most exciting researches since the original VIT.Unlike VIT,it uses a shift window mechanism that is more efficient and capable of feature extraction,and is now used as the backbone of many vision model architectures due to its superior performance,and is also often involved in the field of medical image segmentation.In order to study and evaluate neonatal brain development more accurately,this paper carries out two aspects of work as follows.1.In this paper,a novel end-to-end medical image segmentation framework is designed,mainly for neonatal brain MRI images,including measures of neonatal brain MRI image pre-processing,training,and post-processing.The preprocessing part applies techniques such as N4 bias field correction and balanced contrast enhancement to improve the quality of the input images.In the training part,the shift window selfattentive module in Swin Transformers is extracted and improved to build an encoderdecoder structure network with full-scale jump link and deep supervision mechanism to obtain multifunctional region segmentation images of neonatal brain.The postprocessing part uses the maximum connected domain algorithm to improve the segmentation accuracy.Finally,the network is trained and tested on the d HCP neonatal brain public dataset,and compared qualitatively and quantitatively with state-of-the-art methods.The experiments demonstrate that the proposed method in this paper effectively improves the segmentation accuracy,and the effectiveness of the proposed improvement is demonstrated by ablation experiments.2.In this paper,we further designed an age prediction framework for the segmented region to predict the neonatal brain age as expected based on neonatal brain MRI,and to study the neonatal brain development more intuitively.In addition to the preprocessing method proposed in the previous study,this study also added brain cranial bone removal to reduce the influence of irrelevant regions.The training part is slightly improved from the previous study to make it more suitable for age prediction.The training phase was finally validated by training on the d HCP dataset as well,and a quantitative comparison was made with several methods,and the proposed method outperformed the other methods in both MAE and R2 metrics.