{"title":"[基于深度学习的 X 射线成像肾上腺血管关键帧识别算法]。","authors":"Huimin Tao, Miao Huang, Cong Liu, Yongtian Liu, Zhihua Hu, Lili Tao, Shuping Zhang","doi":"10.12455/j.issn.1671-7104.240040","DOIUrl":null,"url":null,"abstract":"<p><p>Adrenal vein sampling is required for the staging diagnosis of primary aldosteronism, and the frames in which the adrenal veins are presented are called key frames. Currently, the selection of key frames relies on the doctor's visual judgement which is time-consuming and laborious. This study proposes a key frame recognition algorithm based on deep learning. Firstly, wavelet denoising and multi-scale vessel-enhanced filtering are used to preserve the morphological features of the adrenal veins. Furthermore, by incorporating the self-attention mechanism, an improved recognition model called ResNet50-SA is obtained. Compared with commonly used transfer learning, the new model achieves 97.11% in accuracy, precision, recall, <i>F</i><sub>1</sub>, and AUC, which is superior to other models and can help clinicians quickly identify key frames in adrenal veins.</p>","PeriodicalId":52535,"journal":{"name":"中国医疗器械杂志","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"[Deep Learning-Based Key Frame Recognition Algorithm for Adrenal Vascular in X-Ray Imaging].\",\"authors\":\"Huimin Tao, Miao Huang, Cong Liu, Yongtian Liu, Zhihua Hu, Lili Tao, Shuping Zhang\",\"doi\":\"10.12455/j.issn.1671-7104.240040\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Adrenal vein sampling is required for the staging diagnosis of primary aldosteronism, and the frames in which the adrenal veins are presented are called key frames. Currently, the selection of key frames relies on the doctor's visual judgement which is time-consuming and laborious. This study proposes a key frame recognition algorithm based on deep learning. Firstly, wavelet denoising and multi-scale vessel-enhanced filtering are used to preserve the morphological features of the adrenal veins. Furthermore, by incorporating the self-attention mechanism, an improved recognition model called ResNet50-SA is obtained. Compared with commonly used transfer learning, the new model achieves 97.11% in accuracy, precision, recall, <i>F</i><sub>1</sub>, and AUC, which is superior to other models and can help clinicians quickly identify key frames in adrenal veins.</p>\",\"PeriodicalId\":52535,\"journal\":{\"name\":\"中国医疗器械杂志\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-03-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"中国医疗器械杂志\",\"FirstCategoryId\":\"1087\",\"ListUrlMain\":\"https://doi.org/10.12455/j.issn.1671-7104.240040\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"Medicine\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"中国医疗器械杂志","FirstCategoryId":"1087","ListUrlMain":"https://doi.org/10.12455/j.issn.1671-7104.240040","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Medicine","Score":null,"Total":0}
[Deep Learning-Based Key Frame Recognition Algorithm for Adrenal Vascular in X-Ray Imaging].
Adrenal vein sampling is required for the staging diagnosis of primary aldosteronism, and the frames in which the adrenal veins are presented are called key frames. Currently, the selection of key frames relies on the doctor's visual judgement which is time-consuming and laborious. This study proposes a key frame recognition algorithm based on deep learning. Firstly, wavelet denoising and multi-scale vessel-enhanced filtering are used to preserve the morphological features of the adrenal veins. Furthermore, by incorporating the self-attention mechanism, an improved recognition model called ResNet50-SA is obtained. Compared with commonly used transfer learning, the new model achieves 97.11% in accuracy, precision, recall, F1, and AUC, which is superior to other models and can help clinicians quickly identify key frames in adrenal veins.