An AI-Based Solution for MR Image Analysis of the Female Reproductive System

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.