基于改進U-Net的細胞核圖像分割網(wǎng)絡(luò)

打開文本圖片集

Anucleus image segmentation network based on improved U-Net

SONGWenbo，ZHU Kaiyan，LIU Tong，SONGWeibo （School of InformationEngineering，DalianOceanUniversity，Dalian116O23，China）

Abstract：Deep learning model basedonconvolutional neural network（CNN） haveachieved significantbreakthrough in biomedicalimagesegmentationandhavebeen widelyappliedinpracticalscenarios.Theaccuracyof nucleus imagesegmentation playsacrucialroleinpathologicaldiagnosis.However，theexistingnucleussegmentationalgorithsstillsuferfromisuessuch asfuzzyandadherentboundaries，soanimagesegmentationalgorihmbasedonimprovedU-Netisproposed.Inthemodel，a tripleattentionmoduleisutizedtoenhancefeature focus，andfeaturefusion module，AGgatemodule，andlightweightInception moduleareincorporatedtoimprovesegmentationaccuracy.Theproposedalgorithmwasvalidatedonthe2O18DataScienceBowl （DSB2018）dataset.The evaluationmetrics including IoU（intersectionover union）and DSC reach 81.85%and 90.00% respectively.Experimentalresultsdemonstratethatincomparisonwiththeothersegmentationmodels，theproposedalgorithm exhibitssignificantadvantagesintermsof theconformitybetweensegmentedresultsandgroundtruthlabels，achievingsuperior performance.

Keywords：CNN;deep learning;nucleus segmentation;U-Netnetwork;atentionmechanism;image segmentation

0 引言

細胞核圖像分割作為醫(yī)學(xué)圖像分析的關(guān)鍵一環(huán)，扮演著鑒定細胞、了解生物過程以及提高藥物檢測效率的重要角色。（剩余10565字）