EndoUSScan: Keyframe detection in transvaginal ultrasound imaging for measuring endometrial thickness

IF 4.8 2区医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer methods and programs in biomedicine Pub Date : 2025-07-18 DOI:10.1016/j.cmpb.2025.108957

Yiyang Liu , Boyuan Peng , Qin Zhou , Suzhen Yuan , Wei Yan , Li Fang , Jingjing Jiang , Shixuan Wang , Xin Zhu , Wenwen Wang

{"title":"EndoUSScan: Keyframe detection in transvaginal ultrasound imaging for measuring endometrial thickness","authors":"Yiyang Liu , Boyuan Peng , Qin Zhou , Suzhen Yuan , Wei Yan , Li Fang , Jingjing Jiang , Shixuan Wang , Xin Zhu , Wenwen Wang","doi":"10.1016/j.cmpb.2025.108957","DOIUrl":null,"url":null,"abstract":"<div><h3>Background:</h3><div>Accurate measurement of endometrial thickness (ET) using transvaginal ultrasound (TVUS) imaging is essential for diagnosing various gynecological conditions. However, manual ET measurement remains challenging, especially for junior physicians, due to variability in image quality and patient characteristics.</div></div><div><h3>Methods:</h3><div>A prospective observational study was performed using a dataset of 976 uterine ultrasound videos (82,063 images) measured in 2014-2019 in Tongji Hospital, Huazhong University of Science and Technology. We developed EndoUSScan, a comprehensive system for automated image selection and keyframe identification. The system incorporates MSNet, an improved DenseNet169-based system, to select candidate images with accurate endometrial representation. We also designed a keyframe detection system to assist junior medical staff in identifying frames with the largest ET from the candidate images. Comparative evaluations involved six junior sonographers, who assessed both speed and accuracy.</div></div><div><h3>Findings:</h3><div>MSNet achieved an accuracy of 94.7% and a specificity of 96.7% in selecting candidate images, outperforming conventional models including ResNet50, ResNet101, DenseNet121, and DenseNet169. The automatically selected keyframes were consistent with the expert-defined gold standard. Compared with manual procedures by junior sonographers, EndoUSScan significantly improved both the speed and accuracy of keyframe selection.</div></div><div><h3>Interpretation:</h3><div>This study presents the first fully automated and clinically validated system for keyframe detection in TVUS videos to support ET measurement. By standardizing the image selection process and assisting junior sonographers, EndoUSScan enhances diagnostic efficiency and accuracy, ultimately contributing to improved patient care.</div></div><div><h3>Funding:</h3><div>This study was funded by the <span>National Key Research and Development Program of China</span> (grant number <span><span>2022YFC2704100)</span></span> and <span>Knowledge Innovation Program of Wuhan -Basic Research</span> (No. <span><span>2023020201010041</span></span>).</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"271 ","pages":"Article 108957"},"PeriodicalIF":4.8000,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer methods and programs in biomedicine","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0169260725003748","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}

引用次数: 0

Abstract

Background:

Accurate measurement of endometrial thickness (ET) using transvaginal ultrasound (TVUS) imaging is essential for diagnosing various gynecological conditions. However, manual ET measurement remains challenging, especially for junior physicians, due to variability in image quality and patient characteristics.

Methods:

A prospective observational study was performed using a dataset of 976 uterine ultrasound videos (82,063 images) measured in 2014-2019 in Tongji Hospital, Huazhong University of Science and Technology. We developed EndoUSScan, a comprehensive system for automated image selection and keyframe identification. The system incorporates MSNet, an improved DenseNet169-based system, to select candidate images with accurate endometrial representation. We also designed a keyframe detection system to assist junior medical staff in identifying frames with the largest ET from the candidate images. Comparative evaluations involved six junior sonographers, who assessed both speed and accuracy.

Findings:

MSNet achieved an accuracy of 94.7% and a specificity of 96.7% in selecting candidate images, outperforming conventional models including ResNet50, ResNet101, DenseNet121, and DenseNet169. The automatically selected keyframes were consistent with the expert-defined gold standard. Compared with manual procedures by junior sonographers, EndoUSScan significantly improved both the speed and accuracy of keyframe selection.

Interpretation:

This study presents the first fully automated and clinically validated system for keyframe detection in TVUS videos to support ET measurement. By standardizing the image selection process and assisting junior sonographers, EndoUSScan enhances diagnostic efficiency and accuracy, ultimately contributing to improved patient care.

Funding:

This study was funded by the National Key Research and Development Program of China (grant number 2022YFC2704100) and Knowledge Innovation Program of Wuhan -Basic Research (No. 2023020201010041).

查看原文本刊更多论文

EndoUSScan：关键帧检测在经阴道超声成像测量子宫内膜厚度

背景：使用经阴道超声（TVUS）成像准确测量子宫内膜厚度（ET）对于诊断各种妇科疾病至关重要。然而，手动ET测量仍然具有挑战性，特别是对于初级医生，由于图像质量和患者特征的可变性。方法：选取华中科技大学同济医院2014-2019年子宫超声影像976张（82063张），进行前瞻性观察研究。我们开发了EndoUSScan，一个用于自动图像选择和关键帧识别的综合系统。该系统结合了MSNet，一个改进的基于densenet169的系统，以选择具有准确子宫内膜表征的候选图像。我们还设计了一个关键帧检测系统，以帮助初级医务人员从候选图像中识别具有最大ET的帧。比较评估涉及六位初级超声医师，他们评估了速度和准确性。结果：在选择候选图像方面，MSNet的准确率为94.7%，特异性为96.7%，优于传统模型，包括ResNet50、ResNet101、DenseNet121和DenseNet169。自动选择的关键帧与专家定义的黄金标准一致。与初级超声医师的手动程序相比，EndoUSScan显著提高了关键帧选择的速度和准确性。解释：这项研究提出了第一个全自动和临床验证的系统，用于在TVUS视频中检测关键帧，以支持ET测量。通过标准化图像选择过程和协助初级超声医师，EndoUSScan提高了诊断效率和准确性，最终有助于改善患者护理。基金资助：本研究由国家重点研发计划（批准号：2022YFC2704100）和武汉市知识创新计划（基础研究）（批准号：2023020201010041）资助。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Computer methods and programs in biomedicine 工程技术-工程：生物医学

CiteScore

12.30

自引率

6.60%

发文量

601

审稿时长

135 days

期刊介绍： To encourage the development of formal computing methods, and their application in biomedical research and medical practice, by illustration of fundamental principles in biomedical informatics research; to stimulate basic research into application software design; to report the state of research of biomedical information processing projects; to report new computer methodologies applied in biomedical areas; the eventual distribution of demonstrable software to avoid duplication of effort; to provide a forum for discussion and improvement of existing software; to optimize contact between national organizations and regional user groups by promoting an international exchange of information on formal methods, standards and software in biomedicine. Computer Methods and Programs in Biomedicine covers computing methodology and software systems derived from computing science for implementation in all aspects of biomedical research and medical practice. It is designed to serve: biochemists; biologists; geneticists; immunologists; neuroscientists; pharmacologists; toxicologists; clinicians; epidemiologists; psychiatrists; psychologists; cardiologists; chemists; (radio)physicists; computer scientists; programmers and systems analysts; biomedical, clinical, electrical and other engineers; teachers of medical informatics and users of educational software.