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

Nucleus image segmentation is a crucial step in the analysis, pathologicaldiagnosis, and classification, which heavily relies on the quality of nucleussegmentation. However, the complexity of issues such as variations in nucleussize, blurred nucleus contours, uneven staining, cell clustering, andoverlapping cells poses significant challenges. Current methods for nucleussegmentation primarily rely on nuclear morphology or contour-based approaches.Nuclear morphology-based methods exhibit limited generalization ability andstruggle to effectively predict irregular-shaped nuclei, while contour-basedextraction methods face challenges in accurately segmenting overlapping nuclei.To address the aforementioned issues, we propose a dual-branch network usinghybrid attention based residual U-blocks for nucleus instance segmentation. Thenetwork simultaneously predicts target information and target contours.Additionally, we introduce a post-processing method that combines the targetinformation and target contours to distinguish overlapping nuclei and generatean instance segmentation image. Within the network, we propose a context fusionblock (CF-block) that effectively extracts and merges contextual informationfrom the network. Extensive quantitative evaluations are conducted to assessthe performance of our method. Experimental results demonstrate the superiorperformance of the proposed method compared to state-of-the-art approaches onthe BNS, MoNuSeg, CoNSeg, and CPM-17 datasets.

Enhancing Nucleus Segmentation with HARU-Net: A Hybrid Attention Based Residual U-Blocks Network