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2021 | OriginalPaper | Buchkapitel

Spatio-Temporal Learning from Longitudinal Data for Multiple Sclerosis Lesion Segmentation

verfasst von : Stefan Denner, Ashkan Khakzar, Moiz Sajid, Mahdi Saleh, Ziga Spiclin, Seong Tae Kim, Nassir Navab

Erschienen in: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

Verlag: Springer International Publishing

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Abstract

Segmentation of Multiple Sclerosis (MS) lesions in longitudinal brain MR scans is performed for monitoring the progression of MS lesions. We hypothesize that the spatio-temporal cues in longitudinal data can aid the segmentation algorithm. Therefore, we propose a multi-task learning approach by defining an auxiliary self-supervised task of deformable registration between two time-points to guide the neural network toward learning from spatio-temporal changes. We show the efficacy of our method on a clinical dataset comprised of 70 patients with one follow-up study for each patient. Our results show that spatio-temporal information in longitudinal data is a beneficial cue for improving segmentation. We improve the result of current state-of-the-art by 2.6% in terms of overall score (p < 0.05). Code is publicly available (https://github.com/StefanDenn3r/Spatio-temporal-MS-Lesion-Segmentation).

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Vorheriges Kapitel Label-Efficient Multi-task Segmentation Using Contrastive Learning

Nächstes Kapitel MMSSD: Multi-scale and Multi-level Single Shot Detector for Brain Metastases Detection

Andermatt, S., Pezold, S., Cattin, P.C.: Automated segmentation of multiple sclerosis lesions using multi-dimensional gated recurrent units. In: Crimi, A., Bakas, S., Kuijf, H., Menze, B., Reyes, M. (eds.) BrainLes 2017. LNCS, vol. 10670, pp. 31–42. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75238-9_3CrossRef

Aslani, S., Dayan, M., Storelli, L., Filippi, M., Murino, V., Rocca, M.A., Sona, D.: Multi-branch convolutional neural network for multiple sclerosis lesion segmentation. NeuroImage 196, 1–15 (2019)CrossRef

Balakrishnan, G., Zhao, A., Sabuncu, M.R., Dalca, A.V., Guttag, J.: An unsupervised learning model for deformable medical image registration. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2018). https://doi.org/10.1109/CVPR.2018.00964

Balakrishnan, G., Zhao, A., Sabuncu, M.R., Guttag, J., Dalca, A.V.: VoxelMorph: a learning framework for deformable medical image registration. IEEE Trans. Med. Imaging (2019). https://doi.org/10.1109/TMI.2019.2897538CrossRef

Birenbaum, A., Greenspan, H.: Longitudinal multiple sclerosis lesion segmentation using multi-view convolutional neural networks. In: Carneiro, G., et al. (eds.) LABELS/DLMIA -2016. LNCS, vol. 10008, pp. 58–67. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46976-8_7CrossRef

Carass, A., et al.: Longitudinal multiple sclerosis lesion segmentation: resource and challenge. NeuroImage 148, 77–102 (2017)CrossRef

Cardoso, M.J., et al.: Geodesic information flows: spatially-variant graphs and their application to segmentation and fusion. IEEE Trans. Med. Imaging 34(9), 1976–1988 (2015)CrossRef

Chen, Z., Badrinarayanan, V., Lee, C.Y., Rabinovich, A.: GradNorm: gradient normalization for adaptive loss balancing in deep multitask networks. In: 35th International Conference on Machine Learning, ICML 2018 (2018)

Compston, A., Coles, A.: Multiple sclerosis (2008). https://doi.org/10.1016/S0140-6736(08)61620-7

10.

Galimzianova, A., Pernuš, F., Likar, B., Špiclin, Ž: Stratified mixture modeling for segmentation of white-matter lesions in brain MR images. NeuroImage 124, 1031–1043 (2016)CrossRef

11.

Ghafoorian, M., Platel, B.: Convolutional neural networks for MS lesion segmentation, method description of diag team. In: Proceedings of the 2015 Longitudinal Multiple Sclerosis Lesion Segmentation Challenge, pp. 1–2 (2015)

12.

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 (2018)CrossRef

13.

Jégou, S., Drozdzal, M., Vazquez, D., Romero, A., Bengio, Y.: The one hundred layers tiramisu: fully convolutional densenets for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 11–19 (2017)

14.

Klein, S., Staring, M., Murphy, K., Viergever, M.A., Pluim, J.P.W.: elastix: a toolbox for intensity-based medical image registration. IEEE Trans. Med. Imaging 29(1), 196–205 (2010)CrossRef

15.

Lesjak, Ž, et al.: A novel public MR image dataset of multiple sclerosis patients with lesion segmentations based on multi-rater consensus. Neuroinformatics 16(1), 51–63 (2018)CrossRef

16.

Manjón, J.V., Coupé, P., Buades, A., Louis Collins, D., Robles, M.: New methods for MRI denoising based on sparseness and self-similarity. Med. Image Anal. 16(1), 18–27 (2012). https://doi.org/10.1016/j.media.2011.04.003. http://www.sciencedirect.com/science/article/pii/S1361841511000491

17.

Paszke, A., et al.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems, pp. 8024–8035 (2019)

18.

Reddi, S.J., Kale, S., Kumar, S.: On the convergence of Adam and beyond (2018)

19.

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_65CrossRef

20.

Roy, A.G., Conjeti, S., Navab, N., Wachinger, C., Initiative, A.D.N., et al.: QuickNAT: a fully convolutional network for quick and accurate segmentation of neuroanatomy. NeuroImage 186, 713–727 (2019)CrossRef

21.

Stangel, M., Penner, I.K., Kallmann, B.A., Lukas, C., Kieseier, B.C.: Towards the implementation of ‘no evidence of disease activity’ in multiple sclerosis treatment: the multiple sclerosis decision model. Therap. Adv. Neurol. Disord. 8(1), 3–13 (2015)CrossRef

22.

Steinman, L.: Multiple sclerosis: A coordinated immunological attack against myelin in the central nervous system (1996). https://doi.org/10.1016/S0092-8674(00)81107-1

23.

Styner, M., et al.: 3D segmentation in the clinic: a grand challenge II: MS lesion segmentation. Midas J. 2008, 1–6 (2008)

24.

Tustison, N.J., et al.: N4ITK: improved N3 bias correction. IEEE Trans. Med. Imaging 29(6), 1310–1320 (2010)CrossRef

25.

Uher, T., et al.: Combining clinical and magnetic resonance imaging markers enhances prediction of 12-year disability in multiple sclerosis. Multiple Sclerosis 23(1), 51–61 (2017)CrossRef

26.

Valverde, S., et al.: Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach. NeuroImage 155, 159–168 (2017)CrossRef

27.

Wachinger, C., Reuter, M., Klein, T.: DeepNAT: deep convolutional neural network for segmenting neuroanatomy. NeuroImage 170, 434–445 (2018)CrossRef

28.

Xu, Z., Niethammer, M.: DeepAtlas: joint semi-supervised learning of image registration and segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 420–429. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32245-8_47CrossRef

29.

Zhang, H., et al.: Multiple sclerosis lesion segmentation with tiramisu and 2.5D stacked slices. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11766, pp. 338–346. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32248-9_38CrossRef

Titel: Spatio-Temporal Learning from Longitudinal Data for Multiple Sclerosis Lesion Segmentation
verfasst von: Stefan Denner
Ashkan Khakzar
Moiz Sajid
Mahdi Saleh
Ziga Spiclin
Seong Tae Kim
Nassir Navab
Verlag: Springer International Publishing
Buch: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries
Print ISBN: 978-3-030-72083-4

Electronic ISBN: 978-3-030-72084-1

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-72084-1_11

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