Deep Supervision Attention U-net for segmentation of uterine zones: a multi-center study

Motivation: The microstructure in the wall of the uterus is key for normal uterine physiology throughout the menstrual cycle and altered in diseases such as adenomyosis. Visualization and quantification in-vivo is thus essential. While MRI offers superior soft tissue contrast, the analysis is limited by a lack of automatic assessing options. Goal(s): Robust automatic segmentation of uterine layers, independent of acquisition specifics. Approach: Deeply supervised Attention-based 3D U-net trained on a multicenter multi field strength dataset for automated segmentation of uterine layers. Results: The proposed network achieved a mean Dice score of 0.8178 and a mean Jaccard index of 0.7176. Impact: This study enabled the deep learning-based automatic segmentation of the uterine zones, that would in future provide deeper insights into uterine layer changes at different menstrual cycle points and facilitate the study of Adenomyosis and other uterine abnormalities.