{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["10"],"submitter":["Liu Y"],"pubmed_abstract":["<b>Purpose:</b> 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. <b>Methods:</b> 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. <b>Results:</b> 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. <b>Conclusion:</b> Endometrial thickness can be automatically and accurately estimated from transvaginal ultrasound images for clinical screening and diagnosis."],"journal":["Frontiers in bioengineering and biotechnology"],"pagination":["853845"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9001908"],"repository":["biostudies-literature"],"pubmed_title":["Automatic Measurement of Endometrial Thickness From Transvaginal Ultrasound Images."],"pmcid":["PMC9001908"],"pubmed_authors":["Liu Y","Jiang J","Zhou Q","Peng B","Weng W","Wang S","Wang W","Zhu X","Fang L"],"additional_accession":[]},"is_claimable":false,"name":"Automatic Measurement of Endometrial Thickness From Transvaginal Ultrasound Images.","description":"<b>Purpose:</b> 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. <b>Methods:</b> 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. <b>Results:</b> 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. <b>Conclusion:</b> Endometrial thickness can be automatically and accurately estimated from transvaginal ultrasound images for clinical screening and diagnosis.","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022","modification":"2025-04-21T17:32:12.535Z","creation":"2025-04-05T16:40:42.678Z"},"accession":"S-EPMC9001908","cross_references":{"pubmed":["35425763"],"doi":["10.3389/fbioe.2022.853845"]}}