Research progress on fetal brain magnetic resonance image segmentation
Medical imaging is an important tool for prenatal screening,diagnosis,treatment guidance,and evaluation,which can effectively avoid abnormal development of the fetal central nervous system,especially for the fetal brain.Medi-cal imaging is mainly operated through X-ray,ultrasound,computed tomography(CT),magnetic resonance imaging(MRI),and other technologies.MRI is a typical non-invasive imaging technology and has become increasingly important for prenatal diagnosis in recent years.MR images are able to produce high tissue contrast,spatial resolution and compre-hensive information to facilitate the diagnosis and treatment of the diseases.The automatic measurement of fetal brain MR images can realize the quantitative evaluation of fetal brain development,resulting in improved efficiency and accuracy of diagnoses.The realization of automatic,quantitative,and accurate analysis of fetal brain MR images depends on reliable image segmentation.Therefore,fetal brain MR image segmentation is of vital clinical significance and research value.Owing to the multiple tissues and organs around the fetal brain,poor image quality,and rapid brain structure changes,fetal brain MR image segmentation encounters numerous challenges.This paper summarizes fetal brain MR image segmen-tation methods.First,the main public atlases and data sets of fetal brain MR images are introduced in detail,including seven publicly available fetal brain MR atlases/data sets.The segmentation labels,acquisition parameters,and some other information of atlases/data sets are described.The links for the atlases/data sets are provided.Second,image segmentation methods,such as brain extraction and tissue/lesion segmentation,are classified and analyzed.Brain extraction methods are categorized into thresholding,region growing,atlas fusion,and classification techniques.Classification techniques are subdivided into traditional machine learning-and deep learning-based methods.Deep learning can automatically learn deep and discriminative features from data,and the performance is significantly improved compared with other methods.Most deep learning-based methods are based on U-Net.And the multi-stage extraction framework is commonly used.There-fore,we further subdivide the deep learning-based methods into single-and multi-stage strategies with U-Net and other con-volutional neural networks.Tissue/lesion segmentation methods are categorized into atlas fusion and classification tech-niques.For the tissue segmentation methods,classification techniques are subdivided into traditional machine learning-and deep learning-based methods.Similarly,we further subdivide the deep learning-based methods with U-Net,other con-volutional neural networks,and Transformer-based neural networks.In each subsection,we compare the performance of different methods.Lastly,by analyzing the methods,the challenges and future research directions of fetal brain MR image segmentation are summarized and prospected.The conclusions are as follows.1)One main issue of fetal brain image analy-sis is that there are only a few publicly available data sets,and the sample size of the available data sets is small.Accord-ingly,evaluating performance uniformly with private data sets is common.At present,there are only three publicly avail-able data sets,and images for fetal brain lesions are limited.Moreover,some data sets were not annotated.Therefore,lack of annotated data is an issue that seriously restricts the extensive and in-depth application of deep learning-based meth-ods.2)Data in the existing data sets are insufficient to support clinical application.At present,most existing fetal brain atlases or datasets are collected from 1.5 T devices.These data have undergone numerous preprocessing operations to derive high-resolution MR images.However,in clinical applications,most images are still the original ones from 1.5 T devices.In addition,image quality and resolution are significantly different among public atlases/data sets.Consequently,the segmentation models trained via available atlases/data sets cannot be directly applied to clinical images obtained from MRI devices.3)Although existing deep learning-based methods have been leveraged to fetal brain image segmentation,most references only apply existing deep learning-based methods without sufficient innovation and consideration of the anatomy and image characteristics of fetal brains.4)The current performance of deep learning-based methods is still unsat-isfactory,while the low accuracy of fetal brain extraction and tissue/lesion segmentation will affect the final diagnosis.5)Most methods do not consider image degradation issues,such as motion artifacts and blurred boundary.6)Variability in clinical imaging equipment,parameters,and other factors leads to significant differences in fetal brain magnetic resonance images,resulting in limited generalization ability of current segmentation methods.Several possible research directions are presented.1)Extensive research on automatic annotation is highly required,especially for deep learning-based methods.2)Additional information on fetal brain,such as anatomical structure and image characteristics,can be further studied.3)Deep learning-based methods,such as weak supervised,transfer,and self-supervised learning,can be used to compen-sate for data sets with minimal ground truth or without ground truth.4)Researchers could combine segmentation methods with super-resolution,motion correction,or design deep networks based on low-quality images to address practical prob-lems of fetal brain MR images.5)Researchers could enhance the generalization ability of models by combining transfer learning,data augmentation,and training with multimodal data.
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