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The release of nnU-Net marked a paradigm shift in 3D medical image segmentation, demonstrating that a properly configured U-Net architecture could still achieve state-of-the-art results.
The multimodal brain tumor image segmentation benchmark (brats)
B. H. Menze, A. Jakab, S. Bauer, J. Kalpathy-Cramer, and et al · 2014
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2015 miccai multi-atlas labeling beyond the cranial vault workshop and challenge
B. Landman, Z. Xu, J. E. Igelsias, M. Styner, and et al · 2015
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U-net: Convolutional networks for biomedical image segmentation
O. Ronneberger, P. Fischer, and T. Brox · 2015
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Advancing the cancer genome atlas glioma mri collections with expert segmentation labels and radiomic features
S. Bakas, H. Akbari, A. Sotiras, M. Bilello, and et al · 2017
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Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, and et al · 2017
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Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: is the problem solved?
O. Bernard, A. Lalande, C. Zotti, Cervenansky, and et al · 2018
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nnu-net: Self-adapting framework for u-net-based medical image segmentation
F. Isensee, J. Petersen, A. Klein, D. Zimmerer, P. F. Jaeger, S. Kohl, J. Wasserthal, G. Koehler, T. Norajitra, S. Wirkert, et al · 2018
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An attempt at beating the 3d u-net
F. Isensee and K. H. Maier-Hein · 2019
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3d mri brain tumor segmentation using autoencoder regularization
A. Myronenko · 2019
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U. Baid, S. Ghodasara, S. Mohan, M. Bilello, and et al · 2021
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Transunet: Transformers make strong encoders for medical image segmentation
J. Chen, Y. Lu, Q. Yu, X. Luo, and et al · 2021
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Utnet: a hybrid transformer architecture for medical image segmentation
Y. Gao, M. Zhou, and D. N. Metaxas · 2021
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Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images
A. Hatamizadeh, V. Nath, Y. Tang, D. Yang, and et al · 2021
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Dints: Differentiable neural network topology search for 3d medical image segmentation
Y. He, D. Yang, H. Roth, C. Zhao, and D. Xu · 2021
Cited alongside, same era.
nnu-net: a self-configuring method for deep learning-based biomedical image segmentation
F. Isensee, P. F. Jaeger, S. A. Kohl, J. Petersen, and K. H. Maier-Hein · 2021
Cited alongside, same era.
Transbts: Multimodal brain tumor segmentation using transformer
W. Wang, C. Chen, M. Ding, J. Li, and et al · 2021
Cited alongside, same era.
Cotr: Efficiently bridging cnn and transformer for 3d medical image segmentation
Y. Xie, J. Zhang, C. Shen, and Y. Xia · 2021
Cited alongside, same era.
Transfuse: Fusing transformers and cnns for medical image segmentation
Y. Zhang, H. Liu, and Q. Hu · 2021
Cited alongside, same era.
nnformer: Interleaved transformer for volumetric segmentation
The liver tumor segmentation benchmark (lits)
P. Bilic, P. Christ, H. B. Li, E. Vorontsov, and et al · 2023
Later among the works it cites.
Mamba: Linear-time sequence modeling with selective state spaces
A. Gu and T. Dao · 2023
Later among the works it cites.
Swinunetr-v2: Stronger swin transformers with stagewise convolutions for 3d medical image segmentation
Y. He, V. Nath, D. Yang, Y. Tang, and et al · 2023
Later among the works it cites.
The kits21 challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase ct, 2023
N. Heller, F. Isensee, D. Trofimova, R. Tejpaul, and et al · 2023
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Z. Huang, H. Wang, Z. Deng, J. Ye, and et al · 2023
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H.-Y. Zhou, J. Guo, Y. Zhang, L. Yu, and et al · 2021
Cited alongside, same era.
The medical segmentation decathlon
M. Antonelli, A. Reinke, S. Bakas, K. Farahani, and et al · 2022
Cited alongside, same era.
Swin-unet: Unet-like pure transformer for medical image segmentation
H. Cao, Y. Wang, J. Chen, D. Jiang, and et al · 2022
Cited alongside, same era.
Monai: An open-source framework for deep learning in healthcare
M. J. Cardoso, W. Li, R. Brown, et al · 2022
Cited alongside, same era.
Unetr: Transformers for 3d medical image segmentation
A. Hatamizadeh, Y. Tang, V. Nath, D. Yang, and et al · 2022
Cited alongside, same era.
Unetr: Transformers for 3d medical image segmentation
A. Hatamizadeh, Y. Tang, V. Nath, D. Yang, and et al · 2022
Cited alongside, same era.
Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation
Y. Ji, H. Bai, C. Ge, J. Yang, and et al · 2022
Cited alongside, same era.
Later among the works it cites.
Transformer utilization in medical image segmentation networks
S. Roy, G. Koehler, M. Baumgartner, C. Ulrich, J. Petersen, F. Isensee, and K. Maier-Hein · 2023
Later among the works it cites.
Mednext: transformer-driven scaling of convnets for medical image segmentation
S. Roy, G. Koehler, C. Ulrich, M. Baumgartner, and et al · 2023
Later among the works it cites.
Totalsegmentator: Robust segmentation of 104 anatomic structures in ct images
J. Wasserthal, H.-C. Breit, M. Meyer, M. Pradella, d. Hinck, A. W. Sauter, T. Heye, D. T. Boll, J. Cyriac, S. Yang, M. Bach, and M. Segeroth · 2023
Later among the works it cites.
D-former: A u-shaped dilated transformer for 3d medical image segmentation
Y. Wu, K. Liao, J. Chen, J. Wang, and et al · 2023
Later among the works it cites.
Levit-unet: Make faster encoders with transformer for medical image segmentation
G. Xu, X. Zhang, X. He, and X. Wu · 2023
Later among the works it cites.
https://github.com/Project-MONAI/research-contributions/issues/68
Swinunetr comment on additional training data · 2024
Closest in time.
U-mamba: Enhancing long-range dependency for biomedical image segmentation
J. Ma, F. Li, and B. Wang · 2024
Closest in time.
Segmamba: Long-range sequential modeling mamba for 3d medical image segmentation
Z. Xing, T. Ye, Y. Yang, G. Liu, and L. Zhu · 2024
Closest in time.