Robail Yasrab, Zeyu Fu, Lior Drukker, Lok Hin Lee, He Zhao, Aris T Papageorghiou, Alison J Noble
{"title":"端到端第一孕期胎儿超声视频自动 CRL 和 NT 分段。","authors":"Robail Yasrab, Zeyu Fu, Lior Drukker, Lok Hin Lee, He Zhao, Aris T Papageorghiou, Alison J Noble","doi":"10.1109/ISBI52829.2022.9761400","DOIUrl":null,"url":null,"abstract":"<p><p>This study presents a novel approach to automatic detection and segmentation of the Crown Rump Length (CRL) and Nuchal Translucency (NT), two essential measurements in the first trimester US scan. The proposed method automatically localises a standard plane within a video clip as defined by the UK Fetal Abnormality Screening Programme. A Nested Hourglass (NHG) based network performs semantic pixel-wise segmentation to extract NT and CRL structures. Our results show that the NHG network is faster (19.52% < GFlops than FCN32) and offers high pixel agreement (mean-IoU=80.74) with expert manual annotations.</p>","PeriodicalId":74566,"journal":{"name":"Proceedings. IEEE International Symposium on Biomedical Imaging","volume":"2022 ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2022-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7614066/pdf/EMS159390.pdf","citationCount":"0","resultStr":"{\"title\":\"End-to-end First Trimester Fetal Ultrasound Video Automated CRL and NT Segmentation.\",\"authors\":\"Robail Yasrab, Zeyu Fu, Lior Drukker, Lok Hin Lee, He Zhao, Aris T Papageorghiou, Alison J Noble\",\"doi\":\"10.1109/ISBI52829.2022.9761400\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>This study presents a novel approach to automatic detection and segmentation of the Crown Rump Length (CRL) and Nuchal Translucency (NT), two essential measurements in the first trimester US scan. The proposed method automatically localises a standard plane within a video clip as defined by the UK Fetal Abnormality Screening Programme. A Nested Hourglass (NHG) based network performs semantic pixel-wise segmentation to extract NT and CRL structures. Our results show that the NHG network is faster (19.52% < GFlops than FCN32) and offers high pixel agreement (mean-IoU=80.74) with expert manual annotations.</p>\",\"PeriodicalId\":74566,\"journal\":{\"name\":\"Proceedings. IEEE International Symposium on Biomedical Imaging\",\"volume\":\"2022 \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-04-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7614066/pdf/EMS159390.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings. IEEE International Symposium on Biomedical Imaging\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISBI52829.2022.9761400\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. IEEE International Symposium on Biomedical Imaging","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISBI52829.2022.9761400","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
本研究提出了一种自动检测和分割胎儿臀长（CRL）和颈部透明层（NT）的新方法，这两项指标是孕期前三个月 US 扫描的基本测量指标。根据英国胎儿畸形筛查计划的规定，该方法可自动定位视频片段中的标准平面。基于嵌套沙漏（NHG）的网络执行语义像素分割，以提取 NT 和 CRL 结构。我们的结果表明，NHG 网络的速度更快（比 FCN32 快 19.52%），与专家手动注释的像素一致性更高（平均 IoU=80.74）。
End-to-end First Trimester Fetal Ultrasound Video Automated CRL and NT Segmentation.
This study presents a novel approach to automatic detection and segmentation of the Crown Rump Length (CRL) and Nuchal Translucency (NT), two essential measurements in the first trimester US scan. The proposed method automatically localises a standard plane within a video clip as defined by the UK Fetal Abnormality Screening Programme. A Nested Hourglass (NHG) based network performs semantic pixel-wise segmentation to extract NT and CRL structures. Our results show that the NHG network is faster (19.52% < GFlops than FCN32) and offers high pixel agreement (mean-IoU=80.74) with expert manual annotations.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
