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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 …