超像素/体素分割算法把具有相同结构信息的点划分至同一子区域，获得可准确描述图像局部特征且符合功能子结构的平滑边缘信息，在医学磁共振成像（magnetic resonance imaging，MRI）分割领域广泛应用。本文比较了不同超像素算法分割脑肿瘤医学图像的性能。归纳并总结了多种最新超像素/体素算法的研究成果及应用，为进一步比较算法性能，选取了多模态脑肿瘤分割挑战赛（Multimodal Brain Tumor Segmentation Challenge，BraTS）2018数据集中的部分脑肿瘤图像进行超像素分割。同时，通过边缘召回率、欠分割错误率、紧密度评测和可达分割准确率4项指标分析算法性能，并阐述算法的未来发展趋势和可行性空间。通过上述算法分析可得：基于图论的（graph-based）、标准化分割（normalized cut）、随机游走算法（lazy random walk）可获得精准的核心肿瘤信息，但对增强肿瘤的准确率稍显不足，不利于后续特征区域提取。基于密度的聚类算法（density-based spatial clustering of applications with noise，DBSCAN）和线性谱聚类（linear spectral clustering，LSC）算法可较好保留肿瘤边界信息，具有较好的局部局灶信息特征，但不能实现邻域信息表达，且没有解决质量跨度较大的问题。拓扑保持正则、Turbopixels和简单线性迭代聚类分割算法（simple linear iterative clustering algorithm，SLIC）的超像素形状结构上更加完整紧凑，对病灶边界的特征描述较为平滑柔和，以此弥补算法对边界描述的不足之处。通过评价指标、国内外最新发展动态和实验对比分析，可看出超像素/体素分割算法具有较高的分割性能，研究领域具有良好的发展前景。

To obtain smooth edge information that can accurately describe the local features and conform to the functional sub structure, the superpixel/voxel segmentation algorithm divides the points with the similar structure information into the same sub region. It is widely used in the field of magnetic resonance imaging (MRI) segmentation. We carry out the comparative performance analysis of different algorithms in brain tumor medical image segmentation. Our algorithms are used to set the number of superpixel seed points directly in the contexts of graph-based, normalized cut, entropy rate, topology preserving regularization, lazy random walk, Turbopixels, density-based spatial clustering of applications with noise(DBSCAN), linear spectral clustering(LSC), and simple linear iterative clustering algorithm (SLIC), respectively. Due to the watershed and the superpixel lattice algorithms cannot achieve accurate manipulations of the number of superpixels, it is required to achieve the superpixel segmentation of the brain tumor images in BraTS 2018 dataset. The graph-based algorithm can segment the core tumor region accurately and identify the brain tumor region with vascular filling effectively. However, it is insufficient for the segmentation accuracy of the completed and enhanced tumor regions of slightly. The performance of the normalized cut algorithm can obtain the brain tumor boundary derived of strong dependence information and retain the characteristic information of the tumor boundary. However, the algorithm divides the lesion region, the gray matter, and the white matter into the same superpixel. The whole tumor region can be divided into the multiple regions, which cannot represent the functional substructure of human brains effectively. The superpixel lattice algorithm can obtain the core tumor location better, but the segmented superpixel boundary does not have the strong attachment. The boundary information of the enhanced tumor can be obtained based on the entropy rate algorithm accurately, which has the obvious density difference between the tumor region and the surrounding tumor. Yet, the generated shape of superpixel boundary is irregular, which cannot express the clear neighborhood information. The topology preserving regularization algorithm can describe the focus accurately, but it cannot clarify the large mass span issue. The lazy random walk algorithm can generate more regular core tumor superpixel boundary, but it can not obtain enhanced tumor boundary information and cannot retain the characteristics of tumor boundary information. The watershed algorithm can obtain the weak boundary information of peripheral edema and intratumoral hemorrhage caused by brain tumor with obvious space occupying effect or lateral ventricular extrusion. However, the obtained superpixel does not conform to the structure of brain functional, which tends to different superpixel from the division of the same functional blocks. The Turbopixels algorithm overcomes the problem that the number of superpixels is different in the initial setting, which leads to the difference of the accuracy of the segmentation results and enhances the robustness of the algorithm. However, the algorithm has little contrast to the whole gray level and the accuracy of segmentation is greatly reduced with the presence of adhesion between the brain tumor location and the surrounding tissues. The DBSCAN algorithm can obtain the core tumor information and identify the necrotic region and the liquefied region in accordance with the image density, which can provide tumor information for complications. However, the algorithm is more sensitive to the noise points and is not robust to the boundary information. The LSC algorithm can release boundary blur and fuzziness of medical imaging equipment. But, the superpixel boundary divides the brain regions with the same features and functional substructures into the multiple blocks, which cannot reflect the shape, size, appearance, other forms of brain tumors, and the pull with the surrounding meninges or blood vessels. The SLIC algorithm has a strong compact and complete retention of feature continuity, which can extract brain tumor features. However, there is a lot of redundancy in the algorithm calculation process, which is challenged to large-scale object segmentation operation, the SLICO algorithm is improved through the SLIC algorithm, which has the high efficient segmentation with low computational complexity. In conclusion, such algorithms can preserve tumor boundary information and have local focal information better in related to graph-based, the normalized cut, the lazy random walk, the DBSCAN, and the LSC. The four algorithms keep the shape structure of the superpixel more complete and compact in regular like topology preserving regularization, Turbopixels, SLIC, and SLICO. Furthermore, the feature description of the lesion boundary is smooth and soft to make up the boundary deficiency. We summarize the current results and applications of various superpixel/voxel algorithms. The performance of the algorithm is analyzed by four indexes like boundary recall, under-segmentation error, compactness measure, and achievable segmentation accuracy. The superpixel/voxel algorithm can improve the efficiency of medical image processing with large object efficiency, which is beneficial to the expression of local information of the brain structure. Some future challenging issues are predicted as mentioned below:1) to divide the brain function and regions without brain structure into the same sub region; 2) to resolve over-fitting or insufficient segmentation caused by abnormal points and noise points near the boundary; 3) to integrate multi-modal lesion information via machine learning.