Abstract
Extracting brain from other non-brain tissues is an essential step in neuroimage analyses such as brain volume estimation. The transformers and 3D UNet based methods achieve strong performance using attention and 3D convolutions. They normally have complex architecture and are thus computationally slow. Consequently, they can hardly be deployed in computational resource-constrained environments. To achieve rapid segmentation, the most recent work UNeXt reduces convolution filters and also presents the Multilayer Perception (MLP) blocks that exploit simpler and linear MLP operations. To further boost performance, it shifts the feature channels in MLP block so as to focus on learning local dependencies. However, it performs segmentation on 2D medical images rather than 3D volumes. In this paper, we propose a recursive MLP based 3D UNet to efficiently extract brain from 3D head volume. Our network involves 3D convolution blocks and MLP blocks to capture both long range information and local dependencies. Meanwhile, we also leverage the simplicity of MLPs to enhance computational efficiency. Unlike UNeXt extracting one locality, we apply several shifts to capture multiple localities representing different local dependencies and then introduce a recursive design to aggregate them. To save computational cost, the shifts do not introduce any parameters and the parameters are also shared across recursions. Extensive experiments on two public datasets demonstrate the superiority of our approach against other state-of-the-art methods with respect to both accuracy and CPU inference time.
G. Shangguan and H. Xiong—Equal contribution.
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References
The Internet Brain Segmentation Repository (IBSR). https://www.nitrc.org/projects/ibsr
The Neurofeedback Skull-stripped (NFBS) repository. https://preprocessed-connectomes-project.org/NFB_skullstripped/
Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016)
Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_49
Cooley, C.Z., et al.: A portable scanner for magnetic resonance imaging of the brain. Nat. Biomed. Eng. 5(3), 229–239 (2021)
Fatima, A., Madni, T.M., Anwar, F., Janjua, U.I., Sultana, N.: Automated 2D slice-based skull stripping multi-view ensemble model on NFBS and IBSR datasets. J. Digit. Imaging 35(2), 374–384 (2022)
Hatamizadeh, A., et al.: UNETR: transformers for 3D medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022)
Hendrycks, D., Gimpel, K.: Gaussian error linear units (GELUs). arXiv preprint arXiv:1606.08415 (2016)
Hwang, H., Rehman, H.Z.U., Lee, S.: 3D U-Net for skull stripping in brain MRI. Appl. Sci. 9(3), 569 (2019)
Ibtehaz, N., Rahman, M.S.: MultiResUNet: rethinking the U-Net architecture for multimodal biomedical image segmentation. Neural Netw. 121, 74–87 (2020)
Isensee, F., Kickingereder, P., Wick, W., Bendszus, M., Maier-Hein, K.H.: Brain tumor segmentation and radiomics survival prediction: contribution to the BRATS 2017 challenge. In: Crimi, A., Bakas, S., Kuijf, H., Menze, B., Reyes, M. (eds.) BrainLes 2017. LNCS, vol. 10670, pp. 287–297. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75238-9_25
Jenkinson, M., Beckmann, C.F., Behrens, T.E., Woolrich, M.W., Smith, S.M.: FSL. Neuroimage 62(2), 782–790 (2012)
Lian, D., Yu, Z., Sun, X., Gao, S.: AS-MLP: an axial shifted MLP architecture for vision. In: International Conference on Learning Representations (ICLR), pp. 1–19 (2022)
Mazurek, M.H., et al.: Portable, bedside, low-field magnetic resonance imaging for evaluation of intracerebral hemorrhage. Nat. Commun. 12(1), 1–11 (2021)
Milletari, F., Navab, N., Ahmadi, S.A.: V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565–571 (2016)
Nie, D., Wang, L., Adeli, E., Lao, C., Lin, W., Shen, D.: 3-D fully convolutional networks for multimodal isointense infant brain image segmentation. IEEE Trans. Cybern. 49(3), 1123–1136 (2018)
Pan, S., et al.: Abdomen CT multi-organ segmentation using token-based MLP-mixer. Med. Phys. 50, 3027–3038 (2022)
Qiu, Z., Yao, T., Ngo, C.W., Mei, T.: MLP-3D: a MLP-Like 3D architecture with grouped time mixing. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3062–3072 (2022)
Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Sheth, K.N., et al.: Assessment of brain injury using portable, low-field magnetic resonance imaging at the bedside of critically ill patients. JAMA Neurol. 78(1), 41–47 (2021)
Smith, S.M.: Fast robust automated brain extraction. Hum. Brain Mapp. 17(3), 143–155 (2002)
Sun, L., Shao, W., Zhu, Q., Wang, M., Li, G., Zhang, D.: Multi-scale multi-hierarchy attention convolutional neural network for fetal brain extraction. Pattern Recogn. 133, 109029 (2023)
Tolstikhin, I.O., et al.: MLP-Mixer: an all-MLP architecture for vision. Adv. Neural. Inf. Process. Syst. 34, 24261–24272 (2021)
Valanarasu, J.M.J., Patel, V.M.: UNeXt: MLP-based rapid medical image segmentation network. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022. LNCS, vol. 13435, pp. 23–33. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-16443-9_3
Wang, Z., Zou, N., Shen, D., Ji, S.: Non-local U-nets for biomedical image segmentation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 6315–6322 (2020)
Yu, T., Li, X., Cai, Y., Sun, M., Li, P.: S\(^2\)-MLP spatial-shift MLP architecture for vision. In: the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 297–306 (2022)
Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: UNet++: redesigning skip connections to exploit multiscale features in image segmentation. IEEE Trans. Med. Imaging 39(6), 1856–1867 (2020)
Acknowledgements
This work is supported by National Natural Science Foundation of China (No. 62072160) and Science and Technology Research Project of Henan Province (No. 232102211024).
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Shangguan, G., Xiong, H., Liu, D., Shen, H. (2024). Fast and Efficient Brain Extraction with Recursive MLP Based 3D UNet. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Lecture Notes in Computer Science, vol 14449. Springer, Singapore. https://doi.org/10.1007/978-981-99-8067-3_43
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