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.

Purpose: Computer-aided diagnosis systems for polyp characterization are commercially available but cannot recognize subtypes of sessile lesions. This study aimed to develop a computer-aided diagnosis system to characterize polyps using non-magni ed white-light endoscopic images.Methods: A total of 2249 non-magni ed white-light images from 1030 lesions including 534 tubular adenomas, 225 sessile serrated adenoma/polyps and 271 hyperplastic polyps in the proximal colon were consecutively extracted from an image library and divided into training and testing datasets (4:1), based on the date of colonoscopy. Using ResNet-50 networks, we developed a classi er (1) to differentiate adenomas from serrated lesions, and another classi er (2) to differentiate sessile serrated adenoma/polyps from hyperplastic polyps. Diagnostic performance was assessed using the testing dataset. The computer-aided diagnosis system generated a probability score for each image, and a probability score for each lesion was calculated as the weighted mean with a log 10 -transformation. Two experts (E1, E2) read the identical testing dataset with a probability score.Results: The area under the curve of classi er (1) for adenomas was equivalent to E1 and superior to E2 (classi er 86%, E1 86%, E2 69%; classi er vs. E2, p<0.001). In contrast, the area under the curve of classi er (2) for sessile serrated adenoma/polyps was inferior to both experts (classi er 55%, E1 68%, E2 79%; classi er vs. E2, p<0.001). Conclusion:The classi er (1) developed using white-light images alone compares favorably with experts in differentiating adenomas from serrated lesions. However, the classi er (2) to identify sessile serrated adenoma/polyps is inferior to experts.

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