{"title":"基于人工神经网络的腹部器官分割研究进展","authors":"Evgin Göçeri, Esther Martinez","doi":"10.1109/ICMLA.2015.231","DOIUrl":null,"url":null,"abstract":"There are many neural network based abdominal organ segmentation approaches from medical images. Computed tomography images were mostly used in these approaches. Applied techniques are usually based on prior information regarding position, shape, and size of organs in these methods. In the literature, there are only a few neural network based techniques that were implemented to segment abdominal organs from magnetic resonance based images. In this paper, we present these methods and their results.","PeriodicalId":288427,"journal":{"name":"2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Artificial Neural Network Based Abdominal Organ Segmentations: A Review\",\"authors\":\"Evgin Göçeri, Esther Martinez\",\"doi\":\"10.1109/ICMLA.2015.231\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"There are many neural network based abdominal organ segmentation approaches from medical images. Computed tomography images were mostly used in these approaches. Applied techniques are usually based on prior information regarding position, shape, and size of organs in these methods. In the literature, there are only a few neural network based techniques that were implemented to segment abdominal organs from magnetic resonance based images. In this paper, we present these methods and their results.\",\"PeriodicalId\":288427,\"journal\":{\"name\":\"2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICMLA.2015.231\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMLA.2015.231","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Artificial Neural Network Based Abdominal Organ Segmentations: A Review
There are many neural network based abdominal organ segmentation approaches from medical images. Computed tomography images were mostly used in these approaches. Applied techniques are usually based on prior information regarding position, shape, and size of organs in these methods. In the literature, there are only a few neural network based techniques that were implemented to segment abdominal organs from magnetic resonance based images. In this paper, we present these methods and their results.