{"title":"一种新的HDU框架用于脑肿瘤的分割","authors":"Zhixing Wu, Fujuan Chen, Dongyuan Wu","doi":"10.1109/ICCWAMTIP.2018.8632590","DOIUrl":null,"url":null,"abstract":"It is well known that U-net is the most advanced medical image processing framework, but it performs poorly in processing complex images. DenseNet is a framework for improvement based on U-net, which has been proposed in recent years, with well performance but large parameters compared with U-net. This paper proposes a Half Dense U-net network, which combines the advantages of DenseNet and U-Net, reduces the number of DenseNet parameters and improves the segmentation accuracy. Compared with U-Net, DenseNet and ResNet proposed in recent years, our proposed model can precisely locate the tumor boundary of brain tumors, thus obtaining higher recognition quality.","PeriodicalId":117919,"journal":{"name":"2018 15th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"122 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"A Novel Framework Called HDU for Segmentation of Brain Tumor\",\"authors\":\"Zhixing Wu, Fujuan Chen, Dongyuan Wu\",\"doi\":\"10.1109/ICCWAMTIP.2018.8632590\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"It is well known that U-net is the most advanced medical image processing framework, but it performs poorly in processing complex images. DenseNet is a framework for improvement based on U-net, which has been proposed in recent years, with well performance but large parameters compared with U-net. This paper proposes a Half Dense U-net network, which combines the advantages of DenseNet and U-Net, reduces the number of DenseNet parameters and improves the segmentation accuracy. Compared with U-Net, DenseNet and ResNet proposed in recent years, our proposed model can precisely locate the tumor boundary of brain tumors, thus obtaining higher recognition quality.\",\"PeriodicalId\":117919,\"journal\":{\"name\":\"2018 15th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)\",\"volume\":\"122 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 15th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCWAMTIP.2018.8632590\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 15th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCWAMTIP.2018.8632590","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Novel Framework Called HDU for Segmentation of Brain Tumor
It is well known that U-net is the most advanced medical image processing framework, but it performs poorly in processing complex images. DenseNet is a framework for improvement based on U-net, which has been proposed in recent years, with well performance but large parameters compared with U-net. This paper proposes a Half Dense U-net network, which combines the advantages of DenseNet and U-Net, reduces the number of DenseNet parameters and improves the segmentation accuracy. Compared with U-Net, DenseNet and ResNet proposed in recent years, our proposed model can precisely locate the tumor boundary of brain tumors, thus obtaining higher recognition quality.