English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Computer vision technology is widely used in biological and medical dataanalysis and understanding. However, there are still two major bottlenecks inthe field of cell membrane segmentation, which seriously hinder furtherresearch: lack of sufficient high-quality data and lack of suitable evaluationcriteria. In order to solve these two problems, this paper first proposes anUltra-high Resolution Image Segmentation dataset for the Cell membrane, calledU-RISC, the largest annotated Electron Microscopy (EM) dataset for the Cellmembrane with multiple iterative annotations and uncompressed high-resolutionraw data. During the analysis process of the U-RISC, we found that the currentpopular segmentation evaluation criteria are inconsistent with humanperception. This interesting phenomenon is confirmed by a subjective experimentinvolving twenty people. Furthermore, to resolve this inconsistency, we proposea new evaluation criterion called Perceptual Hausdorff Distance (PHD) tomeasure the quality of cell membrane segmentation results. Detailed performancecomparison and discussion of classic segmentation methods along with twoiterative manual annotation results under existing evaluation criteria and PHDis given.

Human Perception-based Evaluation Criterion for Ultra-high Resolution Cell Membrane Segmentation