MRI脑肿瘤图像分割的深度学习方法综述

磁共振成像（MRI）作为一种典型的非侵入式成像技术,可产生高质量的无损伤和无颅骨伪影的脑影像,为脑肿瘤的诊断和治疗提供更为全面的信息,是脑肿瘤诊疗的主要技术手段。MRI脑肿瘤自动分割利用计算机技术从多模态脑影像中自动将肿瘤区（坏死区、水肿区、非增强肿瘤区和增强肿瘤区）和正常组织区进行分割和标注,对于辅助脑肿瘤的诊疗具有重要作用。本文对MRI脑肿瘤图像分割的深度学习方法进行了总结与分析,给出了各类方法的基本思想、网络架构形式、代表性改进方案以及优缺点总结等,并给出了部分典型方法在BraTS（multimodal brain tumor segmentation）数据集上的性能表现与分析结果。通过对该领域研究方法进行综述,对现有基于深度学习的MRI脑肿瘤分割研究方法进行了梳理,作为新的发展方向,MRI脑肿瘤图像分割的深度学习方法较传统方法已取得明显的性能提升,已成为领域主流方法并持续展现出良好的发展前景,有助于进一步推动MRI脑肿瘤分割在临床诊疗上的应用。

Review of deep learning methods for MRI brain tumor image segmentation

Brain tumors, abnormal cells growing in the human brain, are common neurological diseases that are extremely harmful to human health. Malignant brain tumors can lead to high mortality. Magnetic resonance imaging (MRI), a typical noninvasive imaging technology, can produce high-quality brain images without damage and skull artifacts, as well as provide comprehensive information to facilitate the diagnosis and treatment of brain tumors. Additionally, the segmentation of MRI brain tumors utilizes computer technology to segment and label tumors (necrosis, edema, and nonenhanced and enhanced tumors) and normal tissues automatically on multimodal brain images, which assists in their diagnosis and treatment. However, given the complexity of brain tissue structure, the diversity of spatial location, the shape and size of brain tumors, and various influence factors, such as field offset effect, volume effect, and equipment noise, during the processing of MRI brain images, automatically achieving accurate tumor segmentation results from MRI brain images has been challenging. With the continuous breakthroughs of deep learning technology in computer vision and medical image analysis, MRI brain tumor segmentation methods based on deep learning have also attracted wide attention in recent years. A series of important research results have been reported, illuminating the promising potential of deep learning methods for MRI brain tumor segmentation task. Therefore, this work aims to review deep learning-based MRI brain tumor segmentation methods, i.e., the current mainstream of MRI brain tumor segmentation. Through an extensive study of the literature on MRI brain tumor segmentation problem, we comprehensively summarize and analyze the existing deep learning methods for MRI brain tumor segmentation. To provide a further understanding of this task, we first introduce a family of authoritative brain tumor segmentation databases, i.e., BraTS (2012-2018) Databases, which run in conjunction with the Medical Image Computing and Computer Assisted Intervention (MICCAI) 2012-2018 Conferences. Several important evaluation metrics, including dice similarity coefficient, predictive positivity value, and sensitivity, are also briefly described. On the basis of the basic network architecture for brain tumor segmentation, we classify the existing deep learning-based MRI brain tumor segmentation methods into three categories, namely, convolutional neural network-, fully convolutional network-, and generative adversarial network-based MRI brain tumor segmentation methods. Convolutional neural network-based methods can be further divided into three sub-categories:single network-based, multinetwork-based, and traditional-method-combination-based approaches. On the basis of the three categories, we comprehensively describe and analyze the basic ideas, network architecture, and typical improvement schemes for each type of method. In addition, we compare the performance results of the representative methods achieved on the BraTS series datasets and summarize the comparative analysis results as well as the advantages and disadvantages of the representative methods. Finally, we discuss three possible future research directions.By reviewing the main work in this field, the existing deep learning methods for MRI brain tumor segmentation are examined well, and our threefold conclusion follows:1) Embedding advanced network architecture or introducing prior information of brain tumors into the deep segmentation network will achieve superior accuracy performance for each type of method. 2) Fully convolutional network-based MRI brain tumor segmentation methods can obtain improved balance between accuracy and efficiency. 3) Generative adversarial network-based MRI brain tumor segmentation methods, a novel and powerful semi-supervised method, has shown good potential for the extremely challenging construction of a large-scale MRI brain tumor segmentation dataset with fine labels. Three possible future research directions are recommended, namely, embedding numerous powerful feature representation modules (e.g., squeeze-and-excitation block, matrix power normalization unit), constructing semi-supervised networks with prior medical knowledge (e.g., constraint information, location, and size and shape information of brain tumors), and transferring networks from other image tasks (e.g., promising detection networks of faster and masker region-based convolutional neural networks). MRI brain tumor segmentation is an important step in the diagnosis and treatment of brain tumors. This process can quickly obtain further accurate MRI brain tumor segmentation results through computer technology, which can effectively assist doctors in computing the location and size of tumors and formulating numerous reasonable treatment and rehabilitation strategies for patients with brain tumors. As a new development direction in recent years, deep learning-based MRI brain tumor segmentation has achieved significant performance improvement over traditional methods. As the mainstream in this field, this method will further promote the clinical diagnosis and treatment level of computer-aided MRI brain tumor segmentation technology.