Abstract
Convolutional neural networks have become a powerful tool for MRI brain analysis and are the state-of-the-art in the matter of brain structure segmentation. Despite the deep learning power and advantages, most of the work is still done in classical methods, such as atlas based segmentation. The majority of those methods also uses only anatomical MRI sequences, e.g. T1- and T2-weighted images, however, other sequences of MRI could lead to much more interesting results. In this work, we are proposing the use of Convolutional Neural Networks, in a multitask approach, which is a tendency to the deep learning community, in order to segment a variety of brain structures. We used over 100 subjects with 32 directions diffusion data and manual annotation, drawn on T1 images, of 8 different brain structures. We have tested variations in the CNN architecture and input data configurations to ensure the best performance. Our results show the results of a particular CNN to segment sub-cortical structures such as Ventricle, Thalamus, Putamen, and Caudate Nucleus.
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References
Bui, T.D., Shin, J., Moon, T.: 3d densely convolutional networks for volumetric segmentation (2017). arXiv:1709.03199
Carmo, D., Silva, B., Yasuda, C., Rittner, L., Lotufo, R.: Extended 2d consensus hippocampus segmentation. In: International Conference on Medical Imaging with Deep Learning (2019)
Chang, H.H., Hsieh, C.C.: Brain segmentation in mr images using a texture-based classifier associated with mathematical morphology. In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3421–3424. IEEE (2017)
Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3d u-net: learning dense volumetric segmentation from sparse annotation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 424–432. Springer, Berlin (2016)
Cover, G., Pereira, M., Bento, M., Appenzeller, S., Rittner, L.: Data-driven corpus callosum parcellation method through diffusion tensor imaging. IEEE Access 5, 22421–22432 (2017). https://doi.org/10.1109/ACCESS.2017.2761701
Duarte, J.M., Santos, J.B.d., Melo, L.C.: Comparison of similarity coefficients based on rapd markers in the common bean. Genet. Mol. Biol. 22(3), 427–432 (1999)
Ennis, D.B., Kindlmann, G.: Orthogonal tensor invariants and the analysis of diffusion tensor magnetic resonance images. Magn. Reson. Med. 55(1), 136–146 (2006). https://doi.org/10.1002/mrm.20741
Fischl, B.: Freesurfer. Neuroimage 62(2), 774–781 (2012). https://doi.org/10.1016/j.neuroimage.2012.01.021
Fonov, V., Evans, A.C., Botteron, K., Almli, C.R., McKinstry, R.C., Collins, D.L., Group, B.D.C., et al.: Unbiased average age-appropriate atlases for pediatric studies. Neuroimage 54(1), 313–327 (2011). https://doi.org/10.1016/j.neuroimage.2010.07.033
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift (2015). arXiv:1502.03167
Jenkinson, M., Beckmann, C.F., Behrens, T.E., Woolrich, M.W., Smith, S.M.: Fsl. Neuroimage 62(2), 782–790 (2012). https://doi.org/10.1016/j.neuroimage.2011.09.015
Kingma, D.P., Ba, J.: Adam: A method for optimization (2014). arXiv:1412.6980
Kushibar, K., Valverde, S., González-Villà, S., Bernal, J., Cabezas, M., Oliver, A., Lladó, X.: Automated sub-cortical brain structure segmentation combining spatial and deep convolutional features. Med. Image Anal. 48, 177–186 (2018). https://doi.org/10.1016/j.media.2018.06.006
Manjn, J.V., Coup, P.: volbrain: An online mri brain volumetry system. Frontiers Neuroinform. 10, 30 (2016). https://doi.org/10.3389/fninf.2016.00030
Mehta, R., Sivaswamy, J.: M-net: a convolutional neural network for deep brain structure segmentation. In: 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), pp. 437–440. IEEE (2017)
Pomiecko, K., et al.: 3d convolutional neural network segmentation of white matter tract masks from mr diffusion anisotropy maps. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pp. 1–5 (2019). https://doi.org/10.1109/ISBI.2019.8759575
Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 234–241. Springer, Berlin (2015)
Wachinger, C., Reuter, M., Klein, T.: Deepnat: deep convolutional neural network for segmenting neuroanatomy. NeuroImage 170, 434–445 (2018)
Wang, G., Li, W., Ourselin, S., Vercauteren, T.: Automatic brain tumor segmentation using convolutional neural networks with test-time augmentation. In: International MICCAI Brainlesion Workshop, pp. 61–72. Springer, Berlin (2018)
Warach, S., Chien, D., Li, W., Ronthal, M., Edelman, R.R.: Fast magnetic resonance diffusion-weighted imaging of acute human stroke. Neurology 42(9), 1717–1717 (1992). https://doi.org/10.1212/WNL.42.9.1717
Warach, S., Gaa, J., Siewert, B., Wielopolski, P., Edelman, R.R.: Acute human stroke studied by whole brain echo planar diffusion-weighted magnetic resonance imaging. Ann. Neurol. 37(2), 231–241 (1995). https://doi.org/10.1002/ana.410370214
Winzeck, S., et al.: Ensemble of convolutional neural networks improves automated segmentation of acute ischemic lesions using multiparametric diffusion-weighted mri. Am. J. Neuroradiol. (2019). https://doi.org/10.3174/ajnr.A6077
Xu, B., Wang, N., Chen, T., Li, M.: Empirical evaluation of rectified activations in convolutional network (2015). arXiv:1505.00853
Yasuda, C., et al.: Dynamic changes in white and gray matter volume are associated with outcome of surgical treatment in temporal lobe epilepsy. Neuroimage 49(1), 71–79 (2010)
Van der Lijn, F., et al.: Automated brain structure segmentation based on atlas registration and appearance models. IEEE Trans. Med. Imaging 31(2), 276–286 (2011). https://doi.org/10.1109/TMI.2011.2168420
Acknowledgments
This project was supported by National Council for Scientific and Technological Development (CNPq 159829/2017-8 and 308311/2016-7) and Teaching, Research and Extension Support Fund (FAEPEX-UNICAMP). Diedre Carmo thanks FAPESP (2018/00186-0) for the scholarship.
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Pinheiro, G.R., Carmo, D.S., Yasuda, C., Lotufo, R.A., Rittner, L. (2020). Convolutional Neural Network on DTI Data for Sub-cortical Brain Structure Segmentation. In: Bonet-Carne, E., Hutter, J., Palombo, M., Pizzolato, M., Sepehrband, F., Zhang, F. (eds) Computational Diffusion MRI. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-030-52893-5_12
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