Automatic Measurement of Endometrial Thickness From Transvaginal Ultrasound Images.

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.