mResU-Net: multi-scale residual U-Net-based brain tumor segmentation from multimodal MRI

[1] Zeng T(2017)DeepEM3D, approaching human-level performance on 3D anisotropic EM image segmentation Bioinformatics 33 2555-2562

[2] Wu B(2015)The multimodal brain tumor image segmentation benchmark (BRATS) IEEE Trans Med Imaging 34 1993-2024

[3] Ji S(2021)nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation Nature Methods 18 203-211

[4] Menze BH(2019)Efficient brain tumor segmentation with multiscale two-pathway-group conventional neural networks IEEE J Biomed Heal Informatics 23 1911-1919

[5] Jakab A(2017)Efficient multiscale 3D CNN with fully connected CRF for accurate brain lesion segmentation Med Image Anal 36 61-78

[6] Bauer S(2018)Road extraction by deep residual u-net IEEE Geosci Remote Sens Lett 15 749-753

[7] Kalpathy-Cramer J(2020)A novel approach for fully automatic intra-tumor segmentation with 3D U-Net architecture for gliomas Front Comput Neurosci 14 1-11

[8] Farahani K(2020)Inter-slice context residual learning for 3D medical image segmentation IEEE Trans Med Imaging 40 661-672

[9] Kirby J(2020)Bu-Net: brain tumor segmentation using modified U-Net architecture Electronics (Switzerland) 9 1-12

[10] Isensee F(2021)BrainSegNet: brain tumor MR image segmentation via enhanced encoder–decoder network Diagnostics 11 169-1848

[1] Zeng T(2017)DeepEM3D, approaching human-level performance on 3D anisotropic EM image segmentation Bioinformatics 33 2555-2562

[2] Wu B(2015)The multimodal brain tumor image segmentation benchmark (BRATS) IEEE Trans Med Imaging 34 1993-2024

[3] Ji S(2021)nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation Nature Methods 18 203-211

[4] Menze BH(2019)Efficient brain tumor segmentation with multiscale two-pathway-group conventional neural networks IEEE J Biomed Heal Informatics 23 1911-1919

[5] Jakab A(2017)Efficient multiscale 3D CNN with fully connected CRF for accurate brain lesion segmentation Med Image Anal 36 61-78

[6] Bauer S(2018)Road extraction by deep residual u-net IEEE Geosci Remote Sens Lett 15 749-753

[7] Kalpathy-Cramer J(2020)A novel approach for fully automatic intra-tumor segmentation with 3D U-Net architecture for gliomas Front Comput Neurosci 14 1-11

[8] Farahani K(2020)Inter-slice context residual learning for 3D medical image segmentation IEEE Trans Med Imaging 40 661-672

[9] Kirby J(2020)Bu-Net: brain tumor segmentation using modified U-Net architecture Electronics (Switzerland) 9 1-12

[10] Isensee F(2021)BrainSegNet: brain tumor MR image segmentation via enhanced encoder–decoder network Diagnostics 11 169-1848