{"title":"基于神经网络的MR离散三维场景半月板曲面估计","authors":"O. M. Rucci, M. Spuri, C. Perego","doi":"10.1109/IEMBS.1992.5761340","DOIUrl":null,"url":null,"abstract":"Menisci pathologies are more easily assessed if images represented on planes parallel to the menisci major section are examined. In the case of magnetic resonance imaging (MR), this procedure requires the physician to locate the plane where the two menisci approximately lie. Unfortunately, this operation is time-consuming and it is often accomplished by successive refinements. In this paper we propose a system for the automated location of such plane. The system works on sets of MR sagittal tomograms and includes three modules, two of which, those performing the image understanding tasks, are based on neural networks. Satisfactory results have been obtained with short computation time.","PeriodicalId":6457,"journal":{"name":"1992 14th Annual International Conference of the IEEE Engineering in Medicine and Biology Society","volume":"28 1","pages":"990-991"},"PeriodicalIF":0.0000,"publicationDate":"1992-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A neural network system for menisci surface estimation in MR discrete 3D scenes\",\"authors\":\"O. M. Rucci, M. Spuri, C. Perego\",\"doi\":\"10.1109/IEMBS.1992.5761340\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Menisci pathologies are more easily assessed if images represented on planes parallel to the menisci major section are examined. In the case of magnetic resonance imaging (MR), this procedure requires the physician to locate the plane where the two menisci approximately lie. Unfortunately, this operation is time-consuming and it is often accomplished by successive refinements. In this paper we propose a system for the automated location of such plane. The system works on sets of MR sagittal tomograms and includes three modules, two of which, those performing the image understanding tasks, are based on neural networks. Satisfactory results have been obtained with short computation time.\",\"PeriodicalId\":6457,\"journal\":{\"name\":\"1992 14th Annual International Conference of the IEEE Engineering in Medicine and Biology Society\",\"volume\":\"28 1\",\"pages\":\"990-991\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1992-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"1992 14th Annual International Conference of the IEEE Engineering in Medicine and Biology Society\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IEMBS.1992.5761340\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"1992 14th Annual International Conference of the IEEE Engineering in Medicine and Biology Society","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IEMBS.1992.5761340","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A neural network system for menisci surface estimation in MR discrete 3D scenes
Menisci pathologies are more easily assessed if images represented on planes parallel to the menisci major section are examined. In the case of magnetic resonance imaging (MR), this procedure requires the physician to locate the plane where the two menisci approximately lie. Unfortunately, this operation is time-consuming and it is often accomplished by successive refinements. In this paper we propose a system for the automated location of such plane. The system works on sets of MR sagittal tomograms and includes three modules, two of which, those performing the image understanding tasks, are based on neural networks. Satisfactory results have been obtained with short computation time.