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Editorial
. 2023 Mar;57(3):882-883.
doi: 10.1002/jmri.28329. Epub 2022 Jul 1.

Editorial for "Fully Automated MRI Segmentation and Volumetric Measurement of Intracranial Meningioma Using Deep Learning"

Thai Akasaka  1 , Tomohisa Okada  1
Affiliations
Editorial

Editorial for "Fully Automated MRI Segmentation and Volumetric Measurement of Intracranial Meningioma Using Deep Learning"

Thai Akasaka et al. J Magn Reson Imaging. 2023 Mar.
No abstract available

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References

    1. Kang H, Witanto JN, Pratama K, et al. Fully automated segmentation and volumetric measurement of intracranial meningioma using deep learning. J Magn Reson Imaging 2023;57:871-881.
    1. Isensee F, Jaeger PF, Kohl SAA, Petersen J, Maier-Hein KH. nnU-Net: A self-configuring method for deep learning-based biomedical image segmentation. Nat Methods 2021;18:203-211.
    1. Neromyliotis E, Kalamatianos T, Paschalis A, et al. Machine learning in meningioma MRI: Past to present. A narrative review. J Magn Reson Imaging 2022;55:48-60.
    1. Maier H, Öfner D, Hittmair A, Kitz K, Budka H. Classic, atypical, and anaplastic meningioma: Three histopathological subtypes of clinical relevance. J Neurosurg 1992;77:616-623.
    1. Okuchi S, Okada T, Yamamoto A, et al. Grading meningioma: A comparative study of thallium-SPECT and FDG-PET. Medicine 2015;94:e549.

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