Automatic Measurement of Endometrial Thickness From Transvaginal Ultrasound Images - Liu, Yiyang; Zhou, Qin; Peng, Boyuan; Jiang, Jingjing; Fang, Li; Weng, Weihao; Wang, Wenwen; Wang, Shixuan; Zhu, Xin (Frontiers in Bioengineering and Biotechnology, 2022)
Purpose: Endometrial thickness is one of the most important indicators in endometrial disease screening. Herein, we propose a method for automated measurement of endometrial thickness from transvaginal ultrasound images. Methods: Accurate automated measurement of endometrial thickness relies on endometrium segmentation from transvaginal ultrasound images that usually have ambiguous boundaries and heterogeneous textures. Therefore, a two-step method was proposed for automated measurement of endometrial thickness. First, a semantic segmentation method was developed based on deep learning, to segment the endometrium from 2D transvaginal ultrasound images. Then, we estimated endometrium thickness from the segmented results, using a largest inscribed circle searching method. Overall 8,119 images (size: 852x1136 pixels) from 467 subjects were used to train and validate the proposed method. Result: We achieved an average Dice coefficient of 0.82 for endometrial segmentation using a validation dataset of 1,059 images from 71 subjects. With validation using 3,210 images from 214 subjects, 89.3% of endometrial thickness errors were within the clinically accepted range of ± 2 mm. Conclusion: Endometrial thickness can be automatically and accurately estimated from transvaginal ultrasound images for clinical diagnosis.