Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
365-05-009 ISMRM Abstract

Detecting the Undetected: Real-Time Anomaly Detection in the Female Pelvis

Primary: Analysis Methods - Generative Models
Secondary: Body - Uterus
365-05-009 · Novel Acquisition Methods · Monday, 11 May, 4:10 PM–5:05 PM · Digital Posters Row F
Keywords: Womens health Abdominal MRI Pelvic Disorders Unsupervised Anomaly Detection Variational Autoencoder
Accepted
Anika Knupfer 1,2, Johanna Paula Müller3, Jordina Aviles Verdera1,2,4, Martin Fenske2, Claudius S Mathy2, Smiti Tripathy1,2, Sebastian Arndt2,5, Matthias May2, Michael Uder2, Matthias W Beckmann6, Stefanie Burghaus6, Jana Hutter1,2,4
1Smart Imaging Lab, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
2Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
3Image Data Exploration and Analysis Lab, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
4School of Biomedical Engineering and Imaging Sciences, Kings College London, London, United Kingdom
5Medical Center for Information and Communication Technology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
6Institute of Women's Health, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
Presenting Author: Anika Knupfer

Synopsis

Motivation:
Goals:
Approach:
Results:
Full abstract & presentation

The full text, figures, and any recorded presentation for this abstract are not shown here. Log in if you are a member or registered attendee with access.

Full abstracts, figures, and presentations for Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition are available to registered attendees. This content becomes freely available to the public roughly two years after the meeting.

To request or purchase access, contact the ISMRM Central Office at info@ismrm.org.

Log in

References

1. Gao Y, Wang X, Wang Q, Jiang L, Wu C, Guo Y, Cui N, Tang H, Tang L. Rising global burden of common gynecological diseases in women of childbearing age from 1990 to 2021: an update from the Global Burden of Disease Study 2021. Reproductive Health. 2025 Apr 21;22(1):57. https://doi.org/10.1186/s12978-025-02013-1 [doi]
2. Sebastiano J, Rodriguez C, Samuels ZV, Pepin K, Zeglis BM. Molecular Imaging in Gynecology: Beyond Cancer. Journal of Nuclear Medicine. 2024 Jul 1;65(7):998-1003. https://doi.org/10.2967/jnumed.124.267546 [doi]
3. Sudderuddin S, Helbren E, Telesca M, Williamson R, Rockall A. MRI appearances of benign uterine disease. Clinical radiology. 2014 Nov 1;69(11):1095-104. https://doi.org/10.1016/j.crad.2014.05.108 [doi]
4. Hudelist G, Fritzer N, Thomas A, Niehues C, Oppelt P, Haas D, Tammaa A, Salzer H. Diagnostic delay for endometriosis in Austria and Germany: causes and possible consequences. Human reproduction. 2012 Dec 1;27(12):3412-6. https://doi.org/10.1093/humrep/des316 [doi]
5. Bulun, S.E., Yilmaz, B.D., Sison, C., Miyazaki, K., Bernardi, L., Liu, S., Kohlmeier, A., Yin, P., Milad, M., Wei, J., Endometriosis. Endocrine Reviews. 2019 Aug;40:1048–1079. https://doi.org/10.1210/er.2018-00242 [doi]
6. Ellis H. Anatomy of the uterus. Anaesthesia & Intensive Care Medicine. 2011 Mar 1;12(3):99-101. https://doi.org/10.1016/j.mpaic.2010.11.005 [doi]
7. Cai L, Pfob A. x. Artificial intelligence in abdominal and pelvic ultrasound imaging: current applications. Abdominal Radiology. 2025 Apr;50(4):1775-89. https://doi.org/10.1007/s00261-024-04640-x [doi]
8. Ong CL. Pitfalls of gynaecological ultrasonography. Singapore medical journal. 2004 Jun 1;45:289-94
9. Manti F, Battaglia C, Bruno I, Ammendola M, Navarra G, Currò G, Laganà D. The role of magnetic resonance imaging in the planning of surgical treatment of deep pelvic endometriosis. Frontiers in surgery. 2022 Jun 28;9:944399. https://doi.org/10.3389/fsurg.2022.944399 [doi]
10. Woodfield CA, Krishnamoorthy S, Hampton BS, Brody JM. Imaging pelvic floor disorders: trend toward comprehensive MRI. American Journal of Roentgenology. 2010 Jun;194(6):1640-9. https://doi.org/10.2214/ajr.09.3670 [doi]
11. Mahmoud NA, Khater HM. Comparative study between MRI and Ultrasound in evaluation of Uterine lesions. Benha Medical Journal. 2025 Jun 1;42(6):134-48. https://doi.org/10.21608/bmfj.2025.344381.2286 [doi]
12. Tong A, Cope AG, Waters TL, McDonald JS, VanBuren WM. Best practices: ultrasound versus MRI in the assessment of pelvic endometriosis. American Journal of Roentgenology. 2024 Dec 11;223(6):e2431085. https://doi.org/10.2214/ajr.24.31085 [doi]
13. Lorusso F, Scioscia M, Rubini D, Stabile Ianora AA, Scardigno D, Leuci C, De Ceglie M, Sardaro A, Lucarelli N, Scardapane A. Magnetic resonance imaging for deep infiltrating endometriosis: current concepts, imaging technique and key findings. Insights into imaging. 2021 Jul 22;12(1):105. https://doi.org/10.1186/s13244-021-01054-x [doi]
14. Nougaret S, Lakhman Y, Gourgou S, Kubik-Huch R, Derchi L, Sala E, Forstner R, European Society of Radiology (ESR) and the European Society of Urogenital Radiology (ESUR). MRI in female pelvis: an ESUR/ESR survey. Insights into imaging. 2022 Mar 28;13(1):60. https://doi.org/10.1186/s13244-021-01152-w [doi]
15. Pan H, Chen M, Bai W, Li B, Zhao X, Zhang M, Zhang D, Li Y, Wang H, Geng H, Kong W. Large-scale uterine myoma MRI dataset covering all FIGO types with pixel-level annotations. Scientific Data. 2024 Apr 22;11(1):410. http://dx.doi.org/https://doi.org/10.1186/s13244-021-01054-x [doi]
16. Pan, H., Chen, M., Bai, W., Li, B., Zhao, X., Zhang, M., Zhang, D., Li, Y., Wang, H. UMD.zip. Figshare. 2023 Jan 18. Accessed January 2025 https://figshare.com/articles/dataset/UMD_zip/23541312?file=44111183
17. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556. 2014 Sep 4.
18. Karimi D, Dou H, Warfield SK, Gholipour A. Deep learning with noisy labels: Exploring techniques and remedies in medical image analysis. Medical image analysis. 2020 Oct 1;65:101759. https://doi.org/10.1016/j.media.2020.101759 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/365-05-009