564-06-004 ISMRM Abstract

nnU-Net for automatic segmentation of the parasagittal dura in autistic children using 3D T2-FLAIR

Primary: Neuro - Neurofluids

Secondary: Analysis Methods - Segmentation and Detection

564-06-004 · Segmentation for Neuro Applications · Wednesday, 13 May, 4:55 PM–5:50 PM · Digital Posters Row E

Keywords: Pediatric Neurofluids Deep Learning Segmentation Brain MRI Neural Network

Accepted

Francesca Castellotti ¹, Nivedita Agarwal¹, Ruth O'Gorman Tuura^2,3, Raimund Kottke⁴, Chiara Girardi¹, Tommaso Ciceri⁵

¹Neuroradiology Unit, Scientific Insitute IRCCS Eugenio Medea, Bosisio Parini (LC), Italy

²Center for MR Research, University Children's Hospital Zurich, Zurich, Switzerland

³Children's Research Center, University Children's Hospital Zurich, Zurich, Switzerland

⁴Department of Radiology, University Children's Hospital Zurich, Zurich, Switzerland

⁵Neuroimaging Unit, Scientific Insitute IRCCS Eugenio Medea, Bosisio Parini (LC), Italy

Presenting Author: Francesca Castellotti

Synopsis

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References

1. Ringstad, G., & Eide, P. K. (2020). Cerebrospinal fluid tracer efflux to parasagittal dura in humans. Nature Communications, 11(1), 354. https://doi.org/10.1038/s41467-019-14195-x [doi]

2. Rustenhoven, J., et al. (2021). Functional characterization of the dural sinuses as a neuroimmune interface. Cell, 184(4), 1000–1016.e27. https://doi.org/10.1016/j.cell.2020.12.040 [doi]

3. Hett, K., et al. (2022). Parasagittal dural space and cerebrospinal fluid (CSF) flow across the lifespan in healthy adults. Fluids and Barriers of the CNS, 19(1), 1–13. https://doi.org/10.1186/s12987-022-00320-4 [doi]

4. Song, A. K., et al. (2023). Parasagittal dural space hypertrophy and amyloid-β deposition in Alzheimer's disease. Brain Communications, 5(3), fcad128. https://doi.org/10.1093/braincomms/fcad128 [doi]

5. Melin, E., Ringstad, G., Valnes, L. M., & Eide, P. K. (2023). Human parasagittal dura is a potential neuroimmune interface. Communications Biology, 6(1), 260. https://doi.org/10.1038/s42003-023-04634-3 [doi]

6. Hett, K., McKnight, C. D., Leguizamon, M., et al. (2024). Deep learning segmentation of peri-sinus structures from structural magnetic resonance imaging: validation and normative ranges across the adult lifespan. Fluids and Barriers of the CNS, 21, 15. https://doi.org/10.1186/s12987-024-00516-w [doi]

7. Agarwal, N., Frigerio, G., Rizzato, G., et al. (2024). Parasagittal dural volume correlates with cerebrospinal fluid volume and developmental delay in children with autism spectrum disorder. Commun Med, 4, 1–9. https://doi.org/10.1002/cmd2.413 [doi]

8. Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III (pp. 234–241). Springer International Publishing. https://doi.org/10.1007/978-3-319-24574-4_28 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/564-06-004