Advancing adenomyosis detection through deep learning-assisted uterus segmentation in MRI

Artificial Intelligence, MR, Neural networks, Segmentation, Outcomes Authors: C. Tappermann, M. S. May, L. Siegler, M. Fenske, M. B. Bauer, T. Rüttinger, S. Arndt, B. Lassen-Schmidt DOI: 10.26044/ecr2025/C-28020 Purpose Adenomyosis is an under-researched gynecological condition where endometrial glands and stroma infiltrate the myometrium. It affects more than 20 % of women during their reproductive years and can cause chronic pelvic pain, social restrictions, and infertility [1, 2].The RACOON FADEN project aims to advance research on the early detection of adenomyosis from MRI in a multicenter study. Thirteen German university hospitals are participating in this study. Thickening of the junctional zone, the hypointense layer between endometrium and myometrium, is widely accepted as a surrogate for...

and materials The RACOON FADEN study was approved by the ethics committee of all participating university hospitals. The study protocol complies with the declaration of Helsinki. Funding was provided by the Bundesministerium für Bildung und Forschung via Netzwerk Universitätsmedizin (NUM 2.0, FKZ: 01KX2121).We included a cohort of thirteen female patients and volunteers (27 ± 5.44 years, 21.8 ± 2.96 BMI) from three German university hospitals in our first MRI datasets from the FADEN study preparation to develop the initial models. This dataset will expand throughout the project...

The 2D u-net achieves an average DSC of 0.71 (MM), 0.62 (JZ) and 0.66 (EM) on the test data, improving slightly to 0.78 (MM), 0.64 (JZ) and 0.66 (EM) when considering only the slices up to the cervical junction. The 3D u-net differs slightly from the performance of the 2D u-net. This model achieves an average DSC of 0.52 (MM), 0.68 (JZ), and 0.73 (EM). Again, considering the slices up to the cervical junction, the DSC increases to 0.66 (MM), 0.70 (JZ), and 0.77 (EM)....

These results serve as an initial proof-of-concept for the methods employed in supporting clinicians with this project's segmentation task. While the current approach demonstrates feasibility, there is significant potential for improvement. By acquiring more data, we will iteratively improve the model quality through a continuous training loop, allowing for better generalization and robustness across diverse cases. Furthermore, we aim to extend these methods to additional sequences and explore the possibility of combining them into a single model. In parallel, we plan to incorporate more complex... Personal information and conflict of interest C. Tappermann: Nothing to disclose M. S. May: Nothing to disclose L. Siegler: Nothing to disclose M. Fenske: Nothing to disclose M. B. Bauer: Nothing to disclose T. Rüttinger: Nothing to disclose S. Arndt: Nothing to disclose B. Lassen-Schmidt: Nothing to disclose

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