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Jieneng Chen, Jieru Mei, Xianhang Li, Yongyi Lu, Qihang Yu, Qingyue Wei, Xiangde Luo, Yutong Xie, Ehsan Adeli, Yan Wang and 6 more
Medical image segmentation is crucial for healthcare, yet convolution-based methods like U-Net face limitations in modeling long-range dependencies. To address this, Transformers designed for sequence-to-sequence predictions have been integrated into medical image segmentation. However, a comprehensive understanding of Transformers' self-attention in U-Net components is lacking. TransUNet, first introduced in 2021, is widely recognized as one of the first models to integrate Transformer into medical image analysis. In this study, we present the versatile framework of TransUNet that encapsulates Transformers' self-attention into two key modules: (1) a Transformer encoder tokenizing image patches from a convolution neural network (CNN) feature map, facilitating global context extraction, and (2) a Transformer decoder refining candidate regions through cross-attention between proposals and U-Net features. These modules can be flexibly inserted into the U-Net backbone, resulting in three configurations: Encoder-only, Decoder-only, and Encoder+Decoder. TransUNet provides a library encompassing both 2D and 3D implementations, enabling users to easily tailor the chosen architecture. Our findings highlight the encoder's efficacy in modeling interactions among multiple abdominal organs and the decoder's strength in handling small targets like tumors. It excels in diverse medical applications, such as multi-organ segmentation, pancreatic tumor segmentation, and hepatic vessel segmentation. Notably, our TransUNet achieves a significant average Dice improvement of 1.06% and 4.30% for multi-organ segmentation and pancreatic tumor segmentation, respectively, when compared to the highly competitive nn-UNet, and surpasses the top-1 solution in the BrasTS2021 challenge. 2D/3D Code and models are available at https://github.com/Beckschen/TransUNet and https://github.com/Beckschen/TransUNet-3D, respectively.
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Chen, J., Mei, J., Li, X., Lu, Y., Yu, Q., Wei, Q., Luo, X., Xie, Y., Adeli, E., Wang, Y., Lungren, M. P., Zhang, S., Xing, L., Lü, L., Yuille, A., & Zhou, Y. (2024). TransUNet: Rethinking the U-Net architecture design for medical image segmentation through the lens of transformers. Medical Image Analysis, 97, 103280. https://doi.org/10.1016/j.media.2024.103280
Chen, Jieneng, et al. "TransUNet: Rethinking the U-Net architecture design for medical image segmentation through the lens of transformers." Medical Image Analysis, vol. 97, 2024, pp. 103280, https://doi.org/10.1016/j.media.2024.103280.
Chen, Jieneng, Jieru Mei, Xianhang Li, Yongyi Lu, Qihang Yu, Qingyue Wei, Xiangde Luo, et al. "TransUNet: Rethinking the U-Net architecture design for medical image segmentation through the lens of transformers." Medical Image Analysis 97 (2024): 103280. https://doi.org/10.1016/j.media.2024.103280.
Chen J., Mei J., Li X. [и др.] TransUNet: Rethinking the U-Net architecture design for medical image segmentation through the lens of transformers // Medical Image Analysis. — 2024. — Vol. 97. — P. 103280. — URL: https://doi.org/10.1016/j.media.2024.103280
@article{chen2024transunet,
title = {{TransUNet: Rethinking the U-Net architecture design for medical image segmentation through the lens of transformers}},
author = {Jieneng Chen and Jieru Mei and Xianhang Li and Yongyi Lu and Qihang Yu and Qingyue Wei and Xiangde Luo and Yutong Xie and Ehsan Adeli and Yan Wang and Matthew P. Lungren and Shaoting Zhang and Lei Xing and Le Lü and Alan Yuille and Yuyin Zhou},
journal = {Medical Image Analysis},
year = {2024},
volume = {97},
pages = {103280},
doi = {10.1016/j.media.2024.103280},
url = {https://doi.org/10.1016/j.media.2024.103280}
}TY - JOUR AU - Chen, Jieneng AU - Mei, Jieru AU - Li, Xianhang AU - Lu, Yongyi AU - Yu, Qihang AU - Wei, Qingyue AU - Luo, Xiangde AU - Xie, Yutong AU - Adeli, Ehsan AU - Wang, Yan AU - Lungren, Matthew P. AU - Zhang, Shaoting AU - Xing, Lei AU - Lü, Le AU - Yuille, Alan AU - Zhou, Yuyin TI - TransUNet: Rethinking the U-Net architecture design for medical image segmentation through the lens of transformers JO - Medical Image Analysis VL - 97 SP - 103280 PY - 2024 DA - 2024/07/22 DO - 10.1016/j.media.2024.103280 UR - https://doi.org/10.1016/j.media.2024.103280 ER -
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