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纸质出版日期： 2020-03-16 ，
录用日期： 2019-11-25
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李锵, 白柯鑫, 赵柳, 关欣. MRI脑肿瘤图像分割研究进展及挑战[J]. 中国图象图形学报, 2020,25(3):419-431.
Qiang Li, Kexin Bai, Liu Zhao, Xin Guan. Progresss and challenges of MRI brain tumor image segmentation[J]. Journal of Image and Graphics, 2020,25(3):419-431.
脑肿瘤分割是医学图像处理中的一项重要内容，其目的是辅助医生做出准确的诊断和治疗，在临床脑部医学领域具有重要的实用价值。核磁共振成像（MRI）是临床医生研究脑部组织结构的主要影像学工具，为了使更多研究者对MRI脑肿瘤图像分割理论及其发展进行探索，本文对该领域研究现状进行综述。首先总结了用于MRI脑肿瘤图像分割的方法，并对现有方法进行了分类，即分为监督分割和非监督分割；然后重点综述了基于深度学习的脑肿瘤分割方法，在研究其关键技术基础上归纳了优化策略；最后介绍了脑肿瘤分割（BraTS）挑战，并结合挑战中所用方法展望了脑肿瘤分割领域未来的发展趋势。MRI脑肿瘤图像分割领域的研究已经取得了一些显著进展，尤其是深度学习的发展为该领域的研究提供了新的思路。但由于脑肿瘤在大小、形状和位置方面的高度变化，以及脑肿瘤图像数据有限且类别不平衡等问题，使得脑肿瘤图像分割仍是一个极具挑战的课题。由于分割过程缺乏可解释性和透明性，如何将全自动分割方法应用于临床试验，还需要进行深入研究。
Brain tumor segmentation is an important part of medical image processing. It assists doctors in making accurate diagnoses and treatment plans. It clearly carries important practical value in clinical brain medicine. With the development of medical imaging
imaging technology plays an important role in the evaluation of the treatment of brain tumor patients and can provide doctors with a clear internal structure of the human body. Magnetic resonance imaging (MRI) is the main imaging tool for clinicians to study the structure of brain tissue. Brain tumor MRI modalities include T1-weighted
contrast-enhanced T1-weighted
T2-weighted
and liquid attenuation inversion recovery pulses. Different imaging modalities can provide complementary information to analyze brain tumors. These four types of modalities are usually combined to diagnose the location and size of brain tumors. At present
due to the extensive application of MRI equipment in brain examination
a large number of brain MRI images are generated in the clinical setting. This rise in the quantity of brain MRI images hinders doctors in manually annotating and segmenting all images promptly. Moreover
the manual segmentation of brain tumor tissues highly depends on doctors' professional experience. Therefore
research has focused on the ways to segment brain tumors efficiently
accurately
and automatically. In recent years
significant advancement has been made in the study of brain tumor segmentation methods. To enable other researchers to explore the theory and development of segmentation methods for brain tumor MRI images
this work reviews the current research status in this field. In this study
the current semiautomatic and fully automatic segmentation methods for brain tumor MRI images are divided into two categories:unsupervised segmentation and supervised segmentation. The difference between the two methods lies in the use of hand-labeled image data. Unsupervised segmentation is a nonpriori image segmentation method based on computer clustering statistical analysis. The unsupervised methods are divided into threshold-based
region-based
pixel-based
and model-based segmentation technologies according to the different segmentation principles. This work briefly describes the unsupervised methods according to the above classification and summarizes their advantages and disadvantages. The main feature of supervised segmentation is its use of labeled image data. The segmentation process involves model training and testing. In the former
labeled data are used to learn the mapping from image features to labels. In the latter
the model assigns labels to unlabeled data. Supervised segmentationis mainly based on the segmentation technology of pixel classification. It generally includes traditional machine learning methods and methods based on neural networks. The common traditional machine learning methods and the methods based on neural networks used in brain tumor segmentation are briefly described herein
and their advantages and disadvantages are summarized. The segmentation methods for brain tumor images based on deep learning are mainly described. With the advancement of artificial intelligence
deep learning
especially the new technology represented by convolutional neural networks (CNNs)
has been well received because of its superior brain tumor segmentation results. Compared with traditional segmentation methods
CNNs can automatically learn representative complex features directly from the data. Hence
the research of brain tumor segmentation based on CNNs mainly focuses on network structure design rather than image processing before feature extraction. This study focuses on the structure of neural networks used in the field of brain tumor image segmentation and summarizes the optimization strategies of deep learning. Lastly
the challenge of brain tumor segmentation (BraTS) is introduced
and the future development trend of brain tumor segmentation is established in combination with the methods used in the challenge. The BraTS challenge is a competition to evaluate the segmentation methods for brain tumor MRI images.The BraTS challenge uses preoperative MRI image data from multiple institutional brains to focus on the segmentation of gliomas. In addition
the BraTS challenge involves predicting overall patient survival by combining radiological features with machine learning algorithms to determine the clinical relevance of this segmentation task. The segmentation methods for brain tumor MRI images have inherent advantages
disadvantages
and application scope. Researchers have been working on how to improve the accuracy of segmentation results
the robustness of models
and the overall operational efficiency. Hence
this study analyzes the advantages and disadvantages of various methods
the optimization strategies of deep learning
and future development trends. The optimization strategy of deep learning is as follows.In the aspect of imaging data
data enhancement techniques
such as flipping
scaling
and cropping
are used to increase the amount of training data and improve the generalization ability of models. Acascade framework is introduced to realize the segmentation of whole tumors
core tumors
and enhanced tumors by combining the acascade framework with the inclusion relationship of tumors in the brain anatomical structure. An improved loss function is used to deal with image category imbalances. In terms of network structure
multiscale and multichannel strategies are adopted to make full use of image feature information.In the process of downsampling
aconvolution operation is used instead of pooling so that image information can be further learned while reducing image information loss.Between the convolutional layers
the jump connection method is applied to effectively solve the degradation problem of the deep network.In different cases
the appropriate standardization method
activation function
and loss rate are selected to achieve satisfactory segmentation effects. This work summarizes the development trend of brain tumor segmentation methods by learning and arranging the methods used in the BraTS challenge. As a result of the diversification of MRI imaging modalities
making full use of the each modal image information can effectively improve the accuracy of brain tumor segmentation. Therefore
the reasonable utilization of multimodality images can be expected to become a research hotspot.Methods based on deep learning are outstanding in the field of brain tumor segmentation and have become a hot research direction.The defects of machine learning algorithms lead to an inaccurate segmentation of brain tumors. A popular trend is to improve the original method or combine various methods effectively. Remarkable progress has been made in the segmentation of brain tumor MRI images. The development of deep learning
in particular
provides new ideas for the research in this field. However
brain tumor image segmentation is still a challenging subject because brain tumors vary in size
shape
and position. Moreover
brain tumor image data are limited
and the categories are not balanced. As a result of the lack of interpretability and transparency in the segmentation process
the application of a fully automated segmentation method to clinical trials still requires further research.
脑肿瘤图像分割核磁共振成像(MRI)监督分割非监督分割深度学习
brain tumor image segmentationmagnetic resonance imaging (MRI)unsupervised segmentationsupervised segmentationdeep learning
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