Jing Zhang, Jian Zhou, Ming Huang, Raja Soosaimarian Peter Raj
{"title":"残差双注意U-Net模型分割运动员踝关节损伤图像","authors":"Jing Zhang, Jian Zhou, Ming Huang, Raja Soosaimarian Peter Raj","doi":"10.1590/1678-4324-2023230335","DOIUrl":null,"url":null,"abstract":": The image of an athlete's ankle joint injury can help to check whether the athlete's ankle joint is damaged, and plays a very important role in clinical diagnosis. To address the problem of poor segmentation effect of traditional athletes' ankle injury image segmentation algorithm, an ankle injury image segmentation algorithm based on residual double attention U-Net model is proposed. First, the region of interest is extracted from the original ankle injury image. After translation, rotation and turnover, the image data is expanded. Second, the residual structure is used to adjust the gradient propagation and residual feedback of the segmentation framework, extract the attribute information in the region of interest","PeriodicalId":9169,"journal":{"name":"Brazilian Archives of Biology and Technology","volume":"1 1","pages":""},"PeriodicalIF":1.0000,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Segmentation for Athlete's Ankle Injury Image Using Residual Double Attention U-Net Model\",\"authors\":\"Jing Zhang, Jian Zhou, Ming Huang, Raja Soosaimarian Peter Raj\",\"doi\":\"10.1590/1678-4324-2023230335\",\"DOIUrl\":null,\"url\":null,\"abstract\":\": The image of an athlete's ankle joint injury can help to check whether the athlete's ankle joint is damaged, and plays a very important role in clinical diagnosis. To address the problem of poor segmentation effect of traditional athletes' ankle injury image segmentation algorithm, an ankle injury image segmentation algorithm based on residual double attention U-Net model is proposed. First, the region of interest is extracted from the original ankle injury image. After translation, rotation and turnover, the image data is expanded. Second, the residual structure is used to adjust the gradient propagation and residual feedback of the segmentation framework, extract the attribute information in the region of interest\",\"PeriodicalId\":9169,\"journal\":{\"name\":\"Brazilian Archives of Biology and Technology\",\"volume\":\"1 1\",\"pages\":\"\"},\"PeriodicalIF\":1.0000,\"publicationDate\":\"2023-09-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Brazilian Archives of Biology and Technology\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1590/1678-4324-2023230335\",\"RegionNum\":4,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Brazilian Archives of Biology and Technology","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1590/1678-4324-2023230335","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"BIOLOGY","Score":null,"Total":0}
Segmentation for Athlete's Ankle Injury Image Using Residual Double Attention U-Net Model
: The image of an athlete's ankle joint injury can help to check whether the athlete's ankle joint is damaged, and plays a very important role in clinical diagnosis. To address the problem of poor segmentation effect of traditional athletes' ankle injury image segmentation algorithm, an ankle injury image segmentation algorithm based on residual double attention U-Net model is proposed. First, the region of interest is extracted from the original ankle injury image. After translation, rotation and turnover, the image data is expanded. Second, the residual structure is used to adjust the gradient propagation and residual feedback of the segmentation framework, extract the attribute information in the region of interest