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Radiomic ultrasound-based artificial intelligence (AI) tools may improve adnexal mass evaluations by introducing more quantitative assessments. Detailed segmentation of the lesions is the first step in a radiomics AI classification pipeline. However, accurate outlining is a difficult task, prone to error, and time-consuming. We aimed to develop an automatic method to reduce variability and improve clinical workflow. To evaluate the robustness of using retrospective data, we investigated whether images with sonographic measurement markups interfere with automatic segmentations. A retrospective dataset of 296 images from 106 adnexal lesions (53 benign/53 malignant) was separated by patient into training (19 benign/17 malignant; 89 images) and independent test (34 benign/36 malignant; 207 images) sets. The U-Net was trained twice using images with and without markups. Images were cropped to 20 pixels surrounding the outline and resized to 256x256 pixels. The training set was augmented using flips and rotations. U-Net segmentation performance was compared to expert outlines using the Dice Similarity Coefficient (DSC) and the average Hausdorff distance (HD) and compared using the median and 95% confidence interval (CI) of the difference, with statistical significance indicated if the 95% CI did not cross zero. The median DSC and HD on the independent test set when markups were included in training were 0.92 and 14.4, respectively, and 0.91 and 17.1, respectively, without markups in training. The differences were 0.008 [95% CI: -0.033, 0.056] for DSC and -1.23 [95% CI: -13.5, 7.33] for HD, indicating no evidence for statistically significant difference in performance. Using a U-net algorithm to automatically outline adnexal lesions had excellent agreement with expert outlines, independent of measurement markups presence,supporting AI pipeline development to differentiate between benign and malignant adnexal masses.
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