Abstract
An increase in signal-to-noise ratio (SNR) and susceptibility-induced contrast at higher field strengths, e.g., 7T, is crucial for medical image analysis by providing better insights for the pathophysiology, diagnosis, and treatment of several disease entities. However, it is difficult to obtain 7T images in real clinical practices due to the high cost and low accessibility. In this paper, we propose a novel knowledge keeper network (KKN) to guide brain tissue segmentation by taking advantage of 7T representations without explicitly using 7T images. By extracting features of a 3T input image substantially and then transforming them to 7T features via knowledge distillation (KD), our method achieves deriving 7T-like representations from a given 3T image and exploits them for tissue segmentation. On two independent datasets, we evaluated our method’s validity in qualitative and quantitative manners on 7T-like image synthesis and 7T-guided tissue segmentation by comparing with the comparative methods in the literature.
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References
Avants, B.B., Tustison, N.J., Wu, J., Cook, P.A., Gee, J.C.: An open source multivariate framework for N-tissue segmentation with evaluation on public data. Neuroinformatics 9(4), 381–400 (2011)
Bahrami, K., Shi, F., Rekik, I., Gao, Y., Shen, D.: 7T-guided super-resolution of 3T MRI. Med. Phys. 44(5), 1661–1677 (2017)
Balchandani, P., Naidich, T.: Ultra-high-field MR neuroimaging. Am. J. Neuroradiol. 36(7), 1204–1215 (2015)
Buciluǎ, C., Caruana, R., Niculescu-Mizil, A.: Model compression. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 535–541 (2006)
Chen, H., Dou, Q., Yu, L., Qin, J., Heng, P.A.: VoxResNet: deep voxelwise residual networks for brain segmentation from 3D MR images. NeuroImage 170, 446–455 (2018)
Chen, P., Liu, S., Zhao, H., Jia, J.: Distilling knowledge via knowledge review. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5008–5017 (2021)
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
Deng, M., et al.: Learning-based 3T brain MRI segmentation with guidance from 7T MRI labeling. Med. Phys. 43(12), 6588–6597 (2016)
He, Y., Carass, A., Zuo, L., Dewey, B.E., Prince, J.L.: Autoencoder based self-supervised test-time adaptation for medical image analysis. Med. Image Anal. 72, 102136 (2021)
Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. ar**v preprint ar**v:1503.02531 (2015)
Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1125–1134 (2017)
Van der Kolk, A.G., Hendrikse, J., Zwanenburg, J.J., Visser, F., Luijten, P.R.: Clinical applications of 7T MRI in the brain. Eur. J. Radiol. 82(5), 708–718 (2013)
Kumar, P., Nagar, P., Arora, C., Gupta, A.: U-SegNet: fully convolutional neural network based automated brain tissue segmentation tool. In: 2018 25th IEEE International Conference on Image Processing (ICIP), pp. 3503–3507. IEEE (2018)
Ma, X., Fu, S.: Position-aware recalibration module: learning from feature semantics and feature position. In: International Joint Conference on Artificial Intelligence, pp. 797–803 (2020)
Mao, X., Li, Q., **e, H., Lau, R.Y., Wang, Z., Paul Smolley, S.: Least squares generative adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2794–2802 (2017)
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)
Nöbauer-Huhmann, I.M., et al.: Magnetic resonance imaging contrast enhancement of brain tumors at 3 tesla versus 1.5 tesla. Invest. Radiol. 37(3), 114–119 (2002)
Oak, P.V., Kamathe, R.: Contrast enhancement of brain MRI images using histogram based techniques. Int. J. Innov. Res. Electr. Electron. Instrum. Control Eng. 1(3), 90–94 (2013)
Qu, L., Zhang, Y., Wang, S., Yap, P.T., Shen, D.: Synthesized 7T MRI from 3T MRI via deep learning in spatial and wavelet domains. Med. Image Anal. 62, 101663 (2020)
Wang, H., Li, Y., Wang, Y., Hu, H., Yang, M.H.: Collaborative distillation for ultra-resolution universal style transfer. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1860–1869 (2020)
Wang, L., **e, C., Zeng, N.: RP-Net: a 3D convolutional neural network for brain segmentation from magnetic resonance imaging. IEEE Access 7, 39670–39679 (2019)
Wang, P., Li, Y., Singh, K.K., Lu, J., Vasconcelos, N.: IMAGINE: image synthesis by image-guided model inversion. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3681–3690 (2021)
Wei, J., et al.: A cascaded nested network for 3T brain MR image segmentation guided by 7T labeling. Pattern Recognit. 124, 108420 (2021)
**ang, L., et al.: Deep embedding convolutional neural network for synthesizing CT image from T1-weighted MR image. Med. Image Anal. 47, 31–44 (2018)
Zhang, Y., Cheng, J.-Z., **ang, L., Yap, P.-T., Shen, D.: Dual-domain cascaded regression for synthesizing 7T from 3T MRI. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 410–417. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00928-1_47
Acknowledgements
This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) No. 2022-0-00959 ((Part 2) Few-Shot Learning of Causal Inference in Vision and Language for Decision Making) and by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) No. 2019-0-00079 (Department of Artificial Intelligence (Korea University)).
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Lee, J., Oh, K., Shen, D., Suk, HI. (2022). A Novel Knowledge Keeper Network for 7T-Free but 7T-Guided Brain Tissue Segmentation. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13435. Springer, Cham. https://doi.org/10.1007/978-3-031-16443-9_32
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