Abstract
In this paper, we present a fully convolutional densely connected network (Tiramisu) for multiple sclerosis (MS) lesion segmentation. Different from existing methods, we use stacked slices from all three anatomical planes to achieve a 2.5D method. Individual slices from a given orientation provide global context along the plane and the stack of adjacent slices adds local context. By taking stacked data from three orientations, the network has access to more samples for training and can make more accurate segmentation by combining information of different forms. The conducted experiments demonstrated the competitive performance of our method. For an ablation study, we simulated lesions on healthy controls to generate images with ground truth lesion masks. This experiment confirmed that the use of 2.5D patches, stacked data and the Tiramisu model improve the MS lesion segmentation performance. In addition, we evaluated our approach on the Longitudinal MS Lesion Segmentation Challenge. The overall score of 93.1 places the \(L_2\)-loss variant of our method in the first position on the leaderboard, while the focal-loss variant has obtained the best Dice coefficient and lesion-wise true positive rate with 69.3% and 60.2%, respectively.
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References
Aslani, S., et al.: Multi-branch Convolutional Neural Network for Multiple Sclerosis Lesion Segmentation. arXiv:1811.02942 [cs], November 2018
Avants, B.B., Tustison, N.J., Song, G., Cook, P.A., Klein, A., Gee, J.C.: A reproducible evaluation of ANTs similarity metric performance in brain image registration. NeuroImage 54(3), 2033–2044 (2011)
Carass, A., et al.: Longitudinal multiple sclerosis lesion segmentation: resource and challenge. NeuroImage 148, 77–102 (2017)
Feng, Y., Pan, H., Meyer, C., Feng, X.: A Self-Adaptive Network For Multiple Sclerosis Lesion Segmentation From Multi-Contrast MRI With Various Imaging Protocols. arXiv:1811.07491 [cs] November 2018
Hashemi, S.R., Salehi, S.S.M., Erdogmus, D., Prabhu, S.P., Warfield, S.K., Gholipour, A.: Asymmetric loss functions and deep densely-connected networks for highly-imbalanced medical image segmentation: application to multiple sclerosis lesion detection. IEEE Access 7, 1721–1735 (2019)
Jégou, S., Drozdzal, M., Vazquez, D., Romero, A., Bengio, Y.: The one hundred layers tiramisu: fully convolutional densenets for semantic segmentation. CVPRW 2017, 1175–1183 (2017)
Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] December 2014
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollar, P.: Focal loss for dense object detection. In: ICCV 2017, pp. 2999–3007. IEEE, Venice, October 2017
Meier, D.S., et al.: Dual-sensitivity multiple sclerosis lesion and CSF segmentation for multichannel 3T Brain MRI. J. Neuroimaging 28(1), 36–47 (2018)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. MICCAI 2015, 234–241 (2015)
Roth, H.R., et al.: A new 2.5D representation for lymph node detection using random sets of deep convolutional neural network observations. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014. LNCS, vol. 8673, pp. 520–527. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10404-1_65
Roy, S., Butman, J.A., Reich, D.S., Calabresi, P.A., Pham, D.L.: Multiple Sclerosis Lesion Segmentation from Brain MRI via Fully Convolutional Neural Networks. arXiv:1803.09172 [cs] March 2018
Smith, S.M.: Fast robust automated brain extraction. Hum. Brain Mapp. 17(3), 143–155 (2002)
Tustison, N.J., et al.: N4itk: improved N3 bias correction. IEEE TMI 29(6), 1310–1320 (2010)
Valcarcel, A.M., et al.: MIMoSA: an automated method for intermodal segmentation analysis of multiple sclerosis brain lesions. J. Neuroimaging 28(4), 389–398 (2018)
Acknowledgements
This work was supported in part by the NIH grants R01-NS094456, R01-NS085211, R01-NS060910, and R01-MH112847, as well as the National Multiple Sclerosis Society grant RG-1707-28586.
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Zhang, H. et al. (2019). Multiple Sclerosis Lesion Segmentation with Tiramisu and 2.5D Stacked Slices. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11766. Springer, Cham. https://doi.org/10.1007/978-3-030-32248-9_38
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DOI: https://doi.org/10.1007/978-3-030-32248-9_38
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