Author:
Liu Yiyang,Zhou Qin,Peng Boyuan,Jiang Jingjing,Fang Li,Weng Weihao,Wang Wenwen,Wang Shixuan,Zhu Xin
Abstract
Purpose: Endometrial thickness is one of the most important indicators in endometrial disease screening and diagnosis. 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 developed 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. Second, we estimated endometrial thickness from the segmented results, using a largest inscribed circle searching method. Overall, 8,119 images (size: 852 × 1136 pixels) from 467 cases were used to train and validate the proposed method.Results: We achieved an average Dice coefficient of 0.82 for endometrium segmentation using a validation dataset of 1,059 images from 71 cases. With validation using 3,210 images from 214 cases, 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 screening and diagnosis.
Funder
National Natural Science Foundation of China
University of Aizu
Subject
Biomedical Engineering,Histology,Bioengineering,Biotechnology
Reference35 articles.
1. Deep Learning Using Rectified Linear Units (Relu);Agarap;arXiv preprint arXiv:1803.08375,2018
2. The Impact of Artificial Intelligence in Medicine on the Future Role of the Physician;Ahuja;PeerJ,2019
3. Hold-out vs. Cross-Validation in Machine Learning;Allibhai;Accès,2018
4. Guideline No. 390-classification and Management of Endometrial Hyperplasia;Auclair;J. Obstet. Gynaecol. Can.,2019
5. Segnet: A Deep Convolutional Encoder-Decoder Architecture for Robust Semantic Pixel-wise Labelling;Badrinarayanan;arXiv preprint arXiv:1505.07293,2015
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献