Editorial for "Automated Segmentation of Brain Metastases on T1-Weighted MRI Using Convolutional Neural Network: Impact of Using Volume Aware Loss and Sampling Strategy"
- PMID: 35678418
- DOI: 10.1002/jmri.28272
Editorial for "Automated Segmentation of Brain Metastases on T1-Weighted MRI Using Convolutional Neural Network: Impact of Using Volume Aware Loss and Sampling Strategy"
Comment on
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Automated Detection of Brain Metastases on T1-Weighted MRI Using a Convolutional Neural Network: Impact of Volume Aware Loss and Sampling Strategy.J Magn Reson Imaging. 2022 Dec;56(6):1885-1898. doi: 10.1002/jmri.28274. Epub 2022 May 27. J Magn Reson Imaging. 2022. PMID: 35624544
References
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- Charron O, Lallement A, Jarnet D, Noblet V, Clavier JB, Meyer P. Automatic detection and segmentation of brain metastases on multimodal MR images with a deep convolutional neural network. Comput Biol Med 2018;95:43-54. https://doi.org/10.1016/j.compbiomed.2018.02.004.
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- Liu Y, Stojadinovic S, Hrycushko B, et al. A deep convolutional neural network-based automatic delineation strategy for multiple brain metastases stereotactic radiosurgery. PLoS One 2017;12(10):e0185844. https://doi.org/10.1371/journal.pone.0185844.
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- Grovik E, Yi D, Iv M, Tong E, Rubin D, Zaharchuk G. Deep learning enables automatic detection and segmentation of brain metastases on multisequence MRI. J Magn Reson Imaging 2020;51(1):175-182. https://doi.org/10.1002/jmri.26766.
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- Xue J, Wang B, Ming Y, et al. Deep learning-based detection and segmentation-assisted management of brain metastases. Neuro Oncol 2020;22(4):505-514. https://doi.org/10.1093/neuonc/noz234.
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