Accuracy of an nnUNet Neural Network for the Automatic Segmentation of Intracranial Aneurysms, Their Parent Vessels, and Major Cerebral Arteries from MRI-TOF
The nnUNet MRA-TOF-based algorithm provided a fast and adequate automatic extraction of unruptured IAs, their parent vessels, and the most relevant cerebral arteries. Future steps involve the expansion of the training set with the inclusion of more MRA-TOF studies with and without IAs and its incorporation in 3D imaging viewers and treatment prediction.
Authors
- Lambertus W. Bartels (ORCID: https://orcid.org/0000-0002-3578-3602)
- Elisa Colombo (ORCID: https://orcid.org/0000-0003-0424-1250)
- Mathijs de Boer (ORCID: https://orcid.org/0009-0004-6415-7194)
- Luca Regli (ORCID: https://orcid.org/0000-0003-4639-4474)
- Tristan P. C. van Doormaal (ORCID: https://orcid.org/0000-0002-6299-3859)
Institutions
- University Medical Center Utrecht (NL)
- University Hospital of Zurich (CH)
- Laboratoire d’Imagerie Biomédicale (FR)
Publication Details
- Journal
- American Journal of Neuroradiology
- Published
- 2024-11-22
- DOI
- https://doi.org/10.3174/ajnr.a8607
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.40
Funders
- Universität Zürich