Abstract
Uterine disorders, such as adenomyosis and fibroids, are major contributors to pelvic pain, abnormal uterine bleeding, and infertility. Morphologic configurations and geometric alterations of the uterine cavity serve as critical imaging biomarkers in clinical diagnosis. One well established example is the question mark sign, a highly specific indicator of adenomyosis, characterized by distinctive uterine contour distortions. However, beyond this singular marker, a broader range of shape variations may hold diagnostic significance.
To systematically capture these morphologic and geometric patterns, we adapted a Variational Autoencoder (VAE) pre-trained on fastMRI datasets. Instead of encoding MRI images alone, we designed the model to jointly incorporate both the segmented uterine cavity and MRI scans. By embedding an anatomy-informed prior, the model is better equipped to characterize structural anatomy relevant to uterine pathology.
Our results indicate that both fine-tuning the VAE and using the hybrid encoding approach produce embeddings that align more closely with clinically relevant disease patterns and improve downstream clustering performance. By refining the joint representation of segmentation and MRI data, our method could enhance the potential of latent diffusion models for extracting imaging biomarkers in female pelvic disorders.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bourdon, M., et al.: Adenomyosis: an update regarding its diagnosis and clinical features. J. Gynecol. Obstet. Hum. Reprod. 50, 102228 (2021)
Freytag, D., Günther, V., Maass, N., Alkatout, I.: Uterine fibroids and infertility. Diagnostics 11, 1455 (2021)
Andres, M.P., Borrelli, G.M., Ribeiro, J., Baracat, E.C., Abrão, M.S., Kho, R.M.: Transvaginal ultrasound for the diagnosis of adenomyosis: systematic review and meta-analysis. J. Minim. Invasive Gynecol. 25, 257–264 (2018)
Zannoni, L., et al.: Question mark sign and transvaginal ultrasound uterine tenderness for the diagnosis of adenomyosis: a prospective validation. J. Ultrasound Med. 39, 1405–1412 (2020)
Atlason, H.E., Love, A., Sigurdsson, S., Gudnason, V., Ellingsen, L.M.: SegAE: unsupervised white matter lesion segmentation from brain MRIs using a CNN autoencoder. NeuroImage: Clin. 24, 102085 (2019)
Muhammad, H., et al.: Unsupervised subtyping of cholangiocarcinoma using a deep clustering convolutional autoencoder. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 604–612. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32239-7_67
Wasserthal, J., et al.: TotalSegmentator: robust segmentation of 104 anatomic structures in CT images. Radiol Artif Intell 5, e230024 (2023)
Lindholz, M., et al.: Analyzing the TotalSegmentator for facial feature removal in head CT scans. Radiography 31, 372–378 (2025)
Liu, C., Yu, X., Wang, D., Jiang, T.: ACLNet: a deep learning model for ACL rupture classification combined with bone morphology. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 57-67. Springer, Cham (2024)
Pan, H., et al.: Large-scale uterine myoma MRI dataset covering all FIGO types with pixel-level annotations. Sci. Data 11, 410 (2024)
Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18, 203–211 (2021)
Microsoft, MRI Autoencoder v0.1, Hugging Face Model Hub. https://huggingface.co/microsoft/mri-autoencoder-v0.1. Accessed 27 Feb 2025
Zbontar, J., et al.: fastMRI: an open dataset and benchmarks for accelerated MRI. arXiv preprint arXiv:1811.08839 (2018)
Podell, D., et al.: Sdxl: improving latent diffusion models for high-resolution image synthesis. arXiv preprint arXiv:2307.01952 (2023)
Kingma, D.P., Welling, M.: Auto-encoding variational bayes. Banff, Canada (2013)
Kingma, D.P., Salimans, T., Welling, M.: Variational dropout and the local reparameterization trick. In: Advances in Neural Information Processing Systems, vol. 28 (2015)
Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Adv. Neural. Inf. Process. Syst. 33, 6840–6851 (2020)
Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: a next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2623–2631 (2019)
McInnes, L., Healy, J., Melville, J.: UMAP: uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:1802.03426 (2020)
Acknowledgments
This study was funded by “NUM 2.0” (FKZ: 01KX2121).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Ethics declarations
Disclosure of Interests
M.L. receives funding from the Berlin Institute of Health (Junior Digital Clinician Scientist Grant). C.A.H. receives funding from the Berlin Institute of Health (Digital Clinician Scientist Grant) and Siemens Healthineers and previously received seed funding from the University Medicine Greifswald and speaker fees from Siemens Healthineers, Bayer AG, Koelis AG, and Bracco Imaging Deutschland outside the submitted work. T.P. also receives funding from the Berlin Institute of Health (Advanced Clinician Scientist Grant, Platform Grant), the Ministry of Education and Research (BMBF, 01KX2121 (“NUM 2.0”, RACOON), 68GX21001A, 01ZZ2315D), the German Research Foundation (DFG, SFB 1340/2), and the European Union (H2020, CHAIMELEON: 952172, DIGITAL, EUCAIM:101100633). Furthermore, T.P. declares relationships with the following companies: research agreements (no personal payments) with AGO, Aprea AB, AR-CAGY-GINECO, Astellas Pharma Global Inc. (APGD), Astra Zeneca, Clovis Oncology, Inc., Holaira, Incyte Corporation, Karyopharm, Lion Biotechnologies, Inc., MedImmune, Merck Sharp & Dohme Corp, Millennium Pharmaceuticals, Inc., Morphotec Inc., NovoCure Ltd., PharmaMar S.A. and PharmaMar USA, Inc., Roche, Siemens Healthineers, and TESARO Inc.; fees for a book translation (Elsevier B.V.); and fees for speaking engagements (Bayer Healthcare).
Rights and permissions
Copyright information
© 2026 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Ruppel, R. et al. (2026). UteroVAE: A Shape-Informed Variational Autoencoder for Uterine MRI Encoding in Adenomyosis, Fibroids, and Healthy Uteri. In: Celebi, M.E., et al. Skin Image Analysis, and Computer-Aided Pelvic Imaging for Female Health. DGM4MICCAI 2025. Lecture Notes in Computer Science, vol 16149. Springer, Cham. https://doi.org/10.1007/978-3-032-05825-6_7
Download citation
DOI: https://doi.org/10.1007/978-3-032-05825-6_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-032-05824-9
Online ISBN: 978-3-032-05825-6
eBook Packages: Computer ScienceComputer Science (R0)Springer Nature Proceedings Computer Science