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 Plus annual

Presents the access of premium content as premium feature

Premium Content

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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Colorectal cancer (CRC) is among the top three malignant tumor types in termsof morbidity and mortality. Histopathological images are the gold standard fordiagnosing colon cancer. Cellular nuclei instance segmentation andclassification, and nuclear component regression tasks can aid in the analysisof the tumor microenvironment in colon tissue. Traditional methods are stillunable to handle both types of tasks end-to-end at the same time, and have poorprediction accuracy and high application costs. This paper proposes a new UNetmodel for handling nuclei based on the UNet framework, called MGTUNet, whichuses Mish, Group normalization and transposed convolution layer to improve thesegmentation model, and a ranger optimizer to adjust the SmoothL1Loss values.Secondly, it uses different channels to segment and classify different types ofnucleus, ultimately completing the nuclei instance segmentation andclassification task, and the nuclei component regression task simultaneously.Finally, we did extensive comparison experiments using eight segmentationmodels. By comparing the three evaluation metrics and the parameter sizes ofthe models, MGTUNet obtained 0.6254 on PQ, 0.6359 on mPQ, and 0.8695 on R2.Thus, the experiments demonstrated that MGTUNet is now a state-of-the-artmethod for quantifying histopathological images of colon cancer.

MGTUNet: An new UNet for colon nuclei instance segmentation and quantification