Computer vision technology is widely used in biological and medical data analysis and understanding. However, there are still two major bottlenecks in the field of cell membrane segmentation, which seriously hinder further research: lack of sufficient high-quality data and lack of suitable evaluation criteria. In order to solve these two problems, this paper first proposes an Ultra-high Resolution Image Segmentation dataset for the Cell membrane, called U-RISC, the largest annotated Electron Microscopy (EM) dataset for the Cell membrane with multiple iterative annotations and uncompressed high-resolution raw data. During the analysis process of the U-RISC, we found that the current popular segmentation evaluation criteria are inconsistent with human perception. This interesting phenomenon is confirmed by a subjective experiment involving twenty people. Furthermore, to resolve this inconsistency, we propose a new evaluation criterion called Perceptual Hausdorff Distance (PHD) to measure the quality of cell membrane segmentation results. Detailed performance comparison and discussion of classic segmentation methods along with two iterative manual annotation results under existing evaluation criteria and PHD is given.
翻译:计算机视觉技术广泛用于生物和医学数据分析和理解,然而,细胞膜分离领域仍存在两个主要瓶颈,严重妨碍进一步的研究:缺乏足够的高质量数据和缺乏适当的评价标准。为了解决这两个问题,本文件首先提议为细胞膜膜(称为U-RISC)建立一个超高分辨率图像分解数据集,这是细胞膜(含多个迭代说明和未压缩高分辨率原始数据)的最大附加说明的电微镜(EM)数据集。在对美国辐射研究中心的分析过程中,我们发现目前的流行分解评价标准与人类的看法不一致。这一令人感兴趣的现象得到了涉及20人的主观实验的证实。此外,为了解决这一不一致问题,我们提出了一个新的评价标准,即Permitual Hausdorf距离(PHD),以测量细胞膜分解结果的质量。对典型分解方法进行了详细的绩效比较和讨论,同时根据现有的评价标准和PHD提供了两份迭代手册的分解结果。