{"title":"利用卷积神经网络识别内镜图像中的输尿管和子宫动脉","authors":"B. Harangi, A. Hajdu, R. Lampé, P. Torok","doi":"10.1109/CBMS.2017.137","DOIUrl":null,"url":null,"abstract":"Endoscope-based surgery has several beneficial effects regarding the rehabilitation of the patients, but has some drawbacks causing difficulties to medical experts, on the contrary. The main disadvantage is that the tactile information is loosed to expert who takes the surgical intervention. There are some organs (e.g. ureters and arteries) in the human body which have similar visual appearances, so the differentiation of them based on only visual expression via endoscopy is a challenging task to the medical experts. To support keyhole-surgery using state-of-the-art image processing solutions, we have developed a semi-automatic software which can distinguish ureters from arteries by a dedicated convolutional neural network (CNN). We have trained the CNN on 2000 images acquired during endoscopic surgery and tested on 500 test ones. 94.2% accuracy has been achieved in this two-classes classification task regarding a binary error function.","PeriodicalId":141105,"journal":{"name":"2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"Recognizing Ureter and Uterine Artery in Endoscopic Images Using a Convolutional Neural Network\",\"authors\":\"B. Harangi, A. Hajdu, R. Lampé, P. Torok\",\"doi\":\"10.1109/CBMS.2017.137\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Endoscope-based surgery has several beneficial effects regarding the rehabilitation of the patients, but has some drawbacks causing difficulties to medical experts, on the contrary. The main disadvantage is that the tactile information is loosed to expert who takes the surgical intervention. There are some organs (e.g. ureters and arteries) in the human body which have similar visual appearances, so the differentiation of them based on only visual expression via endoscopy is a challenging task to the medical experts. To support keyhole-surgery using state-of-the-art image processing solutions, we have developed a semi-automatic software which can distinguish ureters from arteries by a dedicated convolutional neural network (CNN). We have trained the CNN on 2000 images acquired during endoscopic surgery and tested on 500 test ones. 94.2% accuracy has been achieved in this two-classes classification task regarding a binary error function.\",\"PeriodicalId\":141105,\"journal\":{\"name\":\"2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-06-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CBMS.2017.137\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CBMS.2017.137","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Recognizing Ureter and Uterine Artery in Endoscopic Images Using a Convolutional Neural Network
Endoscope-based surgery has several beneficial effects regarding the rehabilitation of the patients, but has some drawbacks causing difficulties to medical experts, on the contrary. The main disadvantage is that the tactile information is loosed to expert who takes the surgical intervention. There are some organs (e.g. ureters and arteries) in the human body which have similar visual appearances, so the differentiation of them based on only visual expression via endoscopy is a challenging task to the medical experts. To support keyhole-surgery using state-of-the-art image processing solutions, we have developed a semi-automatic software which can distinguish ureters from arteries by a dedicated convolutional neural network (CNN). We have trained the CNN on 2000 images acquired during endoscopic surgery and tested on 500 test ones. 94.2% accuracy has been achieved in this two-classes classification task regarding a binary error function.