Visionerves: Automatic and Reproducible Hybrid AI for Peripheral Nervous System Recognition Applied to Endometriosis Cases

Endometriosis often leads to chronic pelvic pain and possible nerve involvement, yet imaging the peripheral nerves remains a challenge. We introduce Visionerves, a novel hybrid AI framework for peripheral nervous system recognition from multi-gradient DWI and morphological MRI data. Unlike conventional tractography, Visionerves encodes anatomical knowledge through fuzzy spatial relationships, removing the need for selection of manual ROIs. The pipeline comprises two phases: (A) automatic segmentation of anatomical structures using a deep learning model, and (B) tractography and nerve recognition by symbolic spatial reasoning. Applied to the lumbosacral plexus in 10 women with (confirmed or suspected) endometriosis, Visionerves demonstrated substantial improvements over standard tractography, with Dice score improvements of up to 25% and spatial errors reduced to less than 5 mm. This automatic and reproducible approach enables detailed nerve analysis and paves the way for non-invasive diagnosis of endometriosis-related neuropathy, as well as other conditions with nerve involvement. Access this chapter Tax calculation will be finalised at checkout Purchases are for personal use only Similar content being viewed by others

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Radiol. 22(12), 2806–2813 (2012) Acknowledgments This work has been funded and supported by Ligue contre le cancer, Fondation Béatrice Denys and Prématuration IP Paris. This work was performed using HPC Jean-Zay resources from GENCI–IDRIS (Grant 2025-AD011015418). We would like to thank Fatiha Tacine for her assistance with the organization and retrieval of the acquisitions at the Hôpital européen Georges Pompidou. We would like to thank also Alice Sorrentino for her assistance with the organization and segmentation of the acquisitions at the Hôpital Necker-Enfants malades. Author information Authors and Affiliations Corresponding author Editor information Editors and Affiliations Ethics declarations Data Use Declaration The 131 patients used in phase A were included under a license granted by the Hôpital Necker-Enfants malades for acquisitions during protocol n\(^{\circ }\)2015-101705-44. The 10 endometriosis patients used for testing the Visionerves framework were included under preliminary acquisitions of a future research clinical protocol n\(^{\circ }\)2024-100538-39 approved by the Hôpital européen Georges-Pompidou. Disclosure of Interests The authors declare that a patent application has been filed, related to the method presented in this paper. This disclosure is made in the interest of transparency and does not affect the integrity or objectivity of the research. Rights and permissions Copyright information © 2026 The Author(s), under exclusive license to Springer Nature Switzerland AG About this paper Cite this paper La Barbera, G. et al. (2026). Visionerves: Automatic and Reproducible Hybrid AI for Peripheral Nervous System Recognition Applied to Endometriosis Cases. 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_11 Download citation DOI: https://doi.org/10.1007/978-3-032-05825-6_11 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