  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.
