Abstract
Many deep learning (DL) based methods for brain tumor segmentation have been proposed. Most of them put emphasis on elaborating deep network’s internal structure to enhance the capacity of learning tumor-related features, while other valuable related information, such as normal brain appearance, is often ignored. Inspired by the fact that radiologists are often trained to compare with normal tissues when identifying tumor regions, in this paper, we propose a novel brain tumor segmentation framework by adopting normal brain images as reference to compare with tumor brain images in the learned feature space. In this way, tumor-related features can be highlighted and enhanced for accurate tumor segmentation. Considering that the routine tumor brain images are multimodal while the normal brain images are often monomodal, a new contrastive learning based feature comparison module is proposed to solve incomparable issue between features learned from multimodal and monomodal images. In the experiments, both in-house and public (BraTS2019) multimodal tumor brain image datasets are used to evaluate our proposed framework, demonstrating better performance compared to the state-of-the-art methods in terms of Dice score, sensitivity, and Hausdorff distance. Code: https://github.com/hbliu98/CLFC-Brain-Tumor-Segmentation.
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References
Astaraki, M., Toma-Dasu, I., Smedby, Ö., Wang, C.: Normal appearance autoencoder for lung cancer detection and segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 249–256. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32226-7_28
Baur, C., Denner, S., Wiestler, B., Navab, N., Albarqouni, S.: Autoencoders for unsupervised anomaly segmentation in brain MR images: a comparative study. Med. Image Anal., 101952 (2021)
Baur, C., Wiestler, B., Albarqouni, S., Navab, N.: Deep autoencoding models for unsupervised anomaly segmentation in brain MR images. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11383, pp. 161–169. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11723-8_16
Chen, X., He, K.: Exploring simple siamese representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 15750–15758 (2021)
Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26(3), 297–302 (1945)
Dong, H., Yang, G., Liu, F., Mo, Y., Guo, Y.: Automatic brain tumor detection and segmentation using U-Net based fully convolutional networks. In: Valdés Hernández, M., González-Castro, V. (eds.) MIUA 2017. CCIS, vol. 723, pp. 506–517. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-60964-5_44
Evans, A., Collins, D., Milner, B.: An MRI-based stereotactic brain atlas from 300 young normal subjects, 408, Anaheim. In: Proceedings of the 22nd Symposium of the Society for Neuroscience (1992)
Huang, H., He, R., Sun, Z., Tan, T., et al.: Introvae: introspective variational autoencoders for photographic image synthesis. In: Advances in Neural Information Processing Systems 31 (2018)
Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203–211 (2021)
IXI: Information extraction from images. www.brain-development.org
Kamnitsas, K., et al.: Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med. Image Anal. 36, 61–78 (2017)
Luo, Y., et al.: Adaptive rectification based adversarial network with spectrum constraint for high-quality pet image synthesis. Med. Image Anal. 77, 102335 (2022)
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 (2019)
Radue, E.W., Weigel, M., Wiest, R., Urbach, H.: Introduction to magnetic resonance imaging for neurologists. Continuum Lifelong Learn. Neurol. 22(5), 1379–1398 (2016)
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
Siddiquee, M.M.R., et al.: Learning fixed points in generative adversarial networks: from image-to-image translation to disease detection and localization. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 191–200 (2019)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems 30 (2017)
Wang, K., et al.: Semi-supervised medical image segmentation via a tripled-uncertainty guided mean teacher model with contrastive learning. Med. Image Anal. 79, 102447 (2022)
Wang, L., Shi, F., Lin, W., Gilmore, J.H., Shen, D.: Automatic segmentation of neonatal images using convex optimization and coupled level sets. NeuroImage 58(3), 805–817 (2011)
Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: multimodal brain tumor segmentation using transformer. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12901, pp. 109–119. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87193-2_11
Xiang, L., et al.: Deep embedding convolutional neural network for synthesizing CT image from T1-weighted MR image. Med. Image Anal. 47, 31–44 (2018)
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Liu, H., Nie, D., Shen, D., Wang, J., Tang, Z. (2022). Multimodal Brain Tumor Segmentation Using Contrastive Learning Based Feature Comparison with Monomodal Normal Brain Images. 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_12
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