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Bioeng. Biotechnol."],"abstract":"<jats:p><jats:bold>Purpose:<\/jats:bold> 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.<\/jats:p><jats:p><jats:bold>Methods:<\/jats:bold> 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 \u00d7 1136 pixels) from 467 cases were used to train and validate the proposed method.<\/jats:p><jats:p><jats:bold>Results:<\/jats:bold> 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 \u00b12\u00a0mm.<\/jats:p><jats:p><jats:bold>Conclusion:<\/jats:bold> Endometrial thickness can be automatically and accurately estimated from transvaginal ultrasound images for clinical screening and diagnosis.<\/jats:p>","DOI":"10.3389\/fbioe.2022.853845","type":"journal-article","created":{"date-parts":[[2022,3,30]],"date-time":"2022-03-30T03:53:56Z","timestamp":1648612436000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":20,"title":["Automatic Measurement of Endometrial Thickness From Transvaginal Ultrasound Images"],"prefix":"10.3389","volume":"10","author":[{"given":"Yiyang","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qin","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Boyuan","family":"Peng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingjing","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Fang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weihao","family":"Weng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenwen","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shixuan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2022,3,29]]},"reference":[{"key":"B1","article-title":"Deep Learning Using Rectified Linear Units (Relu)","author":"Agarap","year":"2018","journal-title":"arXiv preprint arXiv:1803.08375"},{"key":"B2","doi-asserted-by":"publisher","first-page":"e7702","DOI":"10.7717\/peerj.7702","article-title":"The Impact of Artificial Intelligence in Medicine on the Future Role of the Physician","volume":"7","author":"Ahuja","year":"2019","journal-title":"PeerJ"},{"key":"B3","article-title":"Hold-out vs. Cross-Validation in Machine Learning","author":"Allibhai","year":"2018","journal-title":"Acc\u00e8s"},{"key":"B4","doi-asserted-by":"publisher","first-page":"1789","DOI":"10.1016\/j.jogc.2019.03.025","article-title":"Guideline No. 390-classification and Management of Endometrial Hyperplasia","volume":"41","author":"Auclair","year":"2019","journal-title":"J. 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