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Reliability of using single specialist annotation for designing and evaluating automatic segmentation methods: A skull stripping case study.

, , , , , , , and . ISBI, page 1344-1347. IEEE, (2018)

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Reliability of using single specialist annotation for designing and evaluating automatic segmentation methods: A skull stripping case study., , , , , , , and . ISBI, page 1344-1347. IEEE, (2018)Improving Face Detection Performance by Skin Detection Post-Processing., , , and . SIBGRAPI, page 300-307. IEEE Computer Society, (2017)An open, multi-vendor, multi-field-strength brain MR dataset and analysis of publicly available skull stripping methods agreement., , , , , , , , and . NeuroImage, (2018)Generative AI for Medical Imaging: extending the MONAI Framework., , , , , , , , , and 14 other author(s). CoRR, (2023)Using convolution neural networks to learn enhanced fiber orientation distribution models from commercially available diffusion magnetic resonance imaging., , , , , , and . CoRR, (2020)Patient-specific prediction of SEEG electrode bending for stereotactic neurosurgical planning., , , , , , , , , and 1 other author(s). Int. J. Comput. Assist. Radiol. Surg., 16 (5): 789-798 (2021)Enhancing the estimation of fiber orientation distributions using convolutional neural networks., , , , , , and . Comput. Biol. Medicine, (2021)Convolutional neural networks for skull-stripping in brain MR imaging using silver standard masks., , , , and . Artif. Intell. Medicine, (2019)Convolutional Neural Networks for Skull-stripping in Brain MR Imaging using Consensus-based Silver standard Masks., , , , and . CoRR, (2018)Towards Uncertainty Quantification for Electrode Bending Prediction in Stereotactic Neurosurgery., , , , , , , , , and . ISBI, page 674-677. IEEE, (2020)