{"paper_id":"dabcbec3-0c56-4049-9bf3-a2a5ea0bb716","body_text":"Abstract\nEndometriosis 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.\nAccess this chapter\nTax calculation will be finalised at checkout\nPurchases are for personal use only\nSimilar content being viewed by others\nReferences\nBaumgartner, M., Jäger, P.F., Isensee, F., Maier-Hein, K.H.: nnDetection: a self-configuring method for medical object detection. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12905, pp. 530–539. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87240-3_51\nBloch, I., Bonnot, E., Gori, P., La Barbera, G., Sarnacki, S.: First order logic with fuzzy semantics for describing and recognizing nerves in medical images. In: IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (2025)\nBloch, I., Ralescu, A.: Fuzzy Sets Methods in Image Processing and Understanding: Medical Imaging Applications. Springer, Cham (2023)\nCage, T., et al.: Visualization of nerve fibers and their relationship to peripheral nerve tumors by diffusion tensor imaging. Neurosurg. Focus 39(3), E16 (2015)\nChapron, C., Marcellin, L., Borghese, B., Santulli, P.: Rethinking mechanisms, diagnosis and management of endometriosis. Nat. Rev. Endocrinol. 15(11), 666–682 (2019)\nDelmonte, A., Mercier, C., Pallud, J., Bloch, I., Gori, P.: White matter multi-resolution segmentation using fuzzy set theory. In: IEEE International Symposium on Biomedical Imaging (2019)\nFauconnier, A., Chapron, C.: Endometriosis and pelvic pain: epidemiological evidence of the relationship and implications. Hum. Reprod. Update 11(6), 595–606 (2005)\nFedorov, A., et al.: 3D Slicer as an image computing platform for the quantitative imaging network. Magn. Reson. Imaging 30(9), 1323–1341 (2012)\nGaryfallidis, E., et al.: Recognition of white matter bundles using local and global streamline-based registration and clustering. Neuroimage 170, 283–295 (2018)\nGori, P., Durrleman, S., Colliot, O., Mangin, J.F., Ayache, N.: A prototype representation to approximate white matter bundles with weighted currents. In: Medical Image Computing and Computer-Assisted Intervention, pp. 289–296 (2014)\nGray, H.: Anatomy of the Human Body. Lea and Febiger (1918)\nHaakma, W., et al.: Diffusion tensor magnetic resonance imaging and fiber tractography of the sacral plexus in children with spina bifida. J. Urol. 192(3), 927–933 (2014)\nHudelot, C., Atif, J., Bloch, I.: Fuzzy spatial relation ontology for image interpretation. Fuzzy Sets Syst. 159(15), 1929–1951 (2008)\nIsensee, F., Jaeger, P., Kohl, S., Petersen, J., Maier-Hein, K.: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203–211 (2021)\nIsensee, F., et al.: nnInteractive: redefining 3D promptable segmentation. arXiv preprint arXiv:2503.08373 (2025)\nKnoedler, M., Feibus, A., Lange, J., Venkatesh, R., Landman, J.: Individualized physical 3-dimensional kidney tumor models constructed from 3-dimensional printers result in improved trainee anatomic understanding. Urology 85(6), 1257–1261 (2015)\nLemos, N., Melo, H., Sermer, C., et al.: Lumbosacral plexus MR tractography: a novel diagnostic tool for extraspinal sciatica and pudendal neuralgia? Magn. Reson. Imaging 83, 107–113 (2021)\nManganaro, L., et al.: Diffusion tensor imaging and tractography to evaluate sacral nerve root abnormalities in endometriosis-related pain: a pilot study. Eur. Radiol. 24(1), 95–101 (2014)\nMuller, C., et al.: Integrating tractography in pelvic surgery: a proof of concept. J. Pediatr. Surg. Case Rep. 48, 101268 (2019)\nSmith, R., Tournier, J.D., Calamante, F., Connelly, A.: Anatomically-constrained tractography: improved diffusion MRI streamlines tractography through effective use of anatomical information. Neuroimage 62(3), 1924–1938 (2012)\nTournier, J.D., Mori, S., Leemans, A.: Diffusion tensor imaging and beyond. Magn. Reson. Med. 65(6), 1532–1556 (2011)\nTournier, J., Smith, R., Raffelt, D., et al.: MRtrix3: a fast, flexible and open software framework for medical image processing and visualisation. Neuroimage 202, 116–137 (2019)\nVinit, N., Blanc, T., Bloch, I., Sarnacki, S., Agon, C., Romero, G.: Robotics and 3D modeling for precision surgery in pediatric oncology. EJC Paediatr. Oncol. 4, 100181 (2024)\nWassermann, D., et al.: The white matter query language: a novel approach for describing human white matter anatomy. Brain Struct. Funct. 221(9), 4705–4721 (2016). https://doi.org/10.1007/s00429-015-1179-4\nWasserthal, J., Neher, P., Maier-Hein, K.: TractSeg - fast and accurate white matter tract segmentation. Neuroimage 183, 239–253 (2018)\nZijta, F., et al.: Evaluation of the female pelvic floor in pelvic organ prolapse using 3.0-Tesla diffusion tensor imaging and fibre tractography. Eur. Radiol. 22(12), 2806–2813 (2012)\nAcknowledgments\nThis 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.\nAuthor information\nAuthors and Affiliations\nCorresponding author\nEditor information\nEditors and Affiliations\nEthics declarations\nData Use Declaration\nThe 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.\nDisclosure of Interests\nThe 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.\nRights and permissions\nCopyright information\n© 2026 The Author(s), under exclusive license to Springer Nature Switzerland AG\nAbout this paper\nCite this paper\nLa 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\nDownload citation\nDOI: https://doi.org/10.1007/978-3-032-05825-6_11\nPublished:\nPublisher Name: Springer, Cham\nPrint ISBN: 978-3-032-05824-9\nOnline ISBN: 978-3-032-05825-6\neBook Packages: Computer ScienceComputer Science (R0)Springer Nature Proceedings Computer Science","source_license":"CC0","license_restricted":false}