EndoUSScan: Keyframe detection in transvaginal ultrasound imaging for measuring endometrial thickness
Yiyang Liu1, Boyuan Peng2, Qin Zhou3
1Beijing Luhe Hospital, Capital Medical University, Beijing, China; Graduate Department of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu, Fukushima, Japan.
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).
