Abstract
Automatic brain tumor segmentation is one of the crucial problems nowadays among other directions and domains where daily clinical workflow requires to put a lot of efforts while studying computer tomography (CT) or structural magnetic resonance imaging (MRI) scans of patients with various pathologies. The MRI is the most common method of primary detection, non-invasive diagnostics and a source of recommendations for further treatment. The brain is a complex structure, different areas of which have different functional significance.
In this paper, we propose a robust pre-processing technique which allows to consider all available information from MRI scans by composition of T1, T1C and FLAIR sequences in the unique input. Such approach enriches the input data for the automatic segmentation process and helps to improve the accuracy of the segmentation performance.
Proposed method demonstrates significant improvement on the binary segmentation problem with respect to Dice and Recall metrics compare to similar training/evaluation procedure based on any single sequence regardless of the chosen neural network architecture.
Obtained results demonstrates significant evaluation improvement while combining three MRI sequences either as weighted mixture to get 1-channel mixed up image or in the 3-channel RGB like image for both considered problems - binary brain tumor segmentation with and without inclusion of edema in the region of interest (ROI). Final improvements on the test part of data set are in the range of 5.6–9.1% on the single-fold trained model according to the Dice metric with the best value of 0.902 without considering a priori “empty” slides. We also demonstrate strong impact on the Recall metric with the growth up to 9.5%. Additionally this approach demonstrates significant improvement according to the Recall metric getting the increase by up to 11%.
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References
Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R.L., Torre, L.A., Jemal, A.: Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 68, 1–31 (2018)
Bobinski, M., Greco, C.M., Schrot, R.J.: Giant intracranial medullary thyroid carcinoma metastasis presenting as apoplexy. J. Skull Base 19, 359–362 (2009)
Chrastina, J., Novak, Z., Riha, I., et al.: Diagnostic value of brain tumor neuroendoscopic biopsy and correlation with open tumor resection. J. Neurolog. Surg. Part A 75(2), 110–115 (2012)
Kamnitsas, K., et al.: Ensembles of multiple models and architectures for robust brain tumour segmentation. In: Crimi, A., Bakas, S., Kuijf, H., Menze, B., Reyes, M. (eds.) BrainLes 2017. LNCS, vol. 10670, pp. 450–462. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75238-9_38
Li, C., Wang, S., Serra, A., Torheim, T., Yan, J.L., et al.: Multi-parametric and multi-regional histogram analysis of MRI: modality integration reveals imaging phenotypes of glioblastoma. Eur. Radiol. 29, 1–12 (2019)
Myronenko, A.: 3D MRI brain tumor segmentation using autoencoder regularization. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11384, pp. 311–320. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11726-9_28
Chaurasia, A., Culurciello, E.: LinkNet: exploiting encoder representations for efficient semantic segmentation, CoRR, vol. abs/1707.03718 (2017)
Milletari, F., et al.: V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision, pp. 565–571. IEEE (2016)
Xie, S., Girshick, R., Dollár, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, vol. 1, pp. 5987–5995 (2017)
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks, CoRR abs/1709.01507 (2017). https://arxiv.org/abs/1709.01507
Ge, C., Gu, I.Y., Store Jakola, A., Yang, J.: Cross-modality augmentation of brain MR images using a novel pairwise generative adversarial network for enhanced glioma classification. In: 2019 IEEE International Conference on Image Processing (ICIP), pp. 559–563 (2019)
Havaei, M., Guizard, N., Chapados, N., Bengio, Y.: HeMIS: hetero-modal image segmentation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 469–477. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_54
Varsavsky, T., Eaton-Rosen, Z., Sudre, C.H., Nachev, P., Cardoso, M.J.: PIMMS: permutation invariant multi-modal segmentation, CoRR, vol. abs/1807.06537 (2018). http://arxiv.org/abs/1807.06537
Dorent, R., Joutard, S., Modat, M., Ourselin, S., Vercauteren, T.: Hetero-modal variational encoder-decoder for joint modality completion and segmentation, arXiv e-prints. arXiv:1907.11150, July 2019
Roy, A.G., Navab, N., Wachinger, C.: Recalibrating fully convolutional networks with spatial and channel ‘squeeze & excitation’ blocks, CoRR abs/1808.08127 (2018). https://arxiv.org/abs/1808.08127
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
Buslaev, A., Parinov, A., Khvedchenya, E., Iglovikov, V., Kalinin, A.: Albumentations: fast and flexible image augmentations. ArXiv e-prints, 1809.06839 (2018)
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The reported study was funded by RFBR according to the research project No 19-29-01103.
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Groza, V. et al. (2020). Data Preprocessing via Multi-sequences MRI Mixture to Improve Brain Tumor Segmentation. In: Rojas, I., Valenzuela, O., Rojas, F., Herrera, L., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2020. Lecture Notes in Computer Science(), vol 12108. Springer, Cham. https://doi.org/10.1007/978-3-030-45385-5_62
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DOI: https://doi.org/10.1007/978-3-030-45385-5_62
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