Keywords: Uterus, Reproductive, Female Reproductive System, Machine Learning, Artificial Intelligence
Motivation: To quantify female reproductive anatomy in MR imaging.
Goal(s): To develop an AI-based solution to segment the regions of interest (RoIs) for the uterine zone, ovaries, pelvic fluid, and detect benign uterine conditions.
Approach: A deep learning based method is applied on a large representative population of 9334 sagittal T2-weighted female pelvis scans to extract normative menstrual cycle- and aging-curves for various RoIs.
Results: Our proposed normative curves define the standard menstrual cycle and aging trends. RoI segmentation, fibroid, and cyst detection models achieve average foreground dice, specificity and accuracy scores of 83.9%, 95.2% and 94.37%, respectively.
Impact: Proposing a robust, precise AI solution for analyzing female reproductive organs on MR imaging, including uterine zones, ovaries, pelvic fluid, and fibroids/cysts. Using this, we define standard aging and menstrual cycle curves for women.
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