| Autor: |
Liu, Yiyang, Zhou, Qin, Peng, Boyuan, Jiang, Jingjing, Fang, Li, Weng, Weihao, Wang, Wenwen, Wang, Shixuan, Zhu, Xin |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Frontiers in Bioengineering and Biotechnology. 10 |
| ISSN: |
2296-4185 |
| DOI: |
10.3389/fbioe.2022.853845 |
| Popis: |
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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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