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Multimodal Patho-Connectomics of Brain Injury Read chapter Read first chapter

2019 | OriginalPaper | Chapter

Deep 2D Encoder-Decoder Convolutional Neural Network for Multiple Sclerosis Lesion Segmentation in Brain MRI

Authors : Shahab Aslani, Michael Dayan, Vittorio Murino, Diego Sona

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

Publisher: Springer International Publishing

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Abstract

In this paper, we propose an automated segmentation approach based on a deep two-dimensional fully convolutional neural network to segment brain multiple sclerosis lesions from multimodal magnetic resonance images. The proposed model is made as a combination of two deep subnetworks. An encoding network extracts different feature maps at various resolutions. A decoding part upconvolves the feature maps combining them through shortcut connections during an upsampling procedure. To the best of our knowledge, the proposed model is the first slice-based fully convolutional neural network for the purpose of multiple sclerosis lesion segmentation. We evaluated our network on a freely available dataset from ISBI MS challenge with encouraging results from a clinical perspective.

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previous chapter Characterizing Peritumoral Tissue Using DTI-Based Free Water Elimination
next chapter Shallow vs Deep Learning Architectures for White Matter Lesion Segmentation in the Early Stages of Multiple Sclerosis
Footnotes
Literature
1.
Brosch, T., Tang, L.Y., Yoo, Y., Li, D.K., Traboulsee, A., Tam, R.: Deep 3D convolutional encoder networks with shortcuts for multiscale feature integration applied to multiple sclerosis lesion segmentation. IEEE Trans. Med. Imaging 35(5), 1229–1239 (2016)CrossRef
2.
Carass, A., et al.: Longitudinal multiple sclerosis lesion segmentation: resource and challenge. NeuroImage 148, 77–102 (2017)CrossRef
3.
4.
García-Lorenzo, D., Francis, S., Narayanan, S., Arnold, D.L., Collins, D.L.: Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging. Med. Image Anal. 17(1), 1–18 (2013)CrossRef
5.
Havaei, M., et al.: Brain tumor segmentation with deep neural networks. Med. Image Anal. 35, 18–31 (2017)CrossRef
6.
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)
7.
He, R., Narayana, P.A.: Automatic delineation of Gd enhancements on magnetic resonance images in multiple sclerosis. Med. Phys. 29(7), 1536–1546 (2002)CrossRef
8.
Jesson, A., Arbel, T.: Hierarchical MRF and random forest segmentation of ms lesions and healthy tissues in brain MRI. In: The Longitudinal MS Lesion Segmentation Challenge (2015)
9.
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
10.
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)
11.
Maier, O., Handels, H.: MS lesion segmentation in MRI with random forests. In: Proceedings of the 2015 Longitudinal Multiple Sclerosis Lesion Segmentation Challenge, pp. 1–2 (2015)
12.
Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (ICML 2010), pp. 807–814 (2010)
13.
14.
Steinman, L.: Multiple sclerosis: a coordinated immunological attack against myelin in the central nervous system. Cell 85(3), 299–302 (1996)CrossRef
15.
Valverde, S., et al.: Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach. NeuroImage 155, 159–168 (2017)CrossRef
16.
Metadata
Title
Deep 2D Encoder-Decoder Convolutional Neural Network for Multiple Sclerosis Lesion Segmentation in Brain MRI
Authors
Shahab Aslani
Michael Dayan
Vittorio Murino
Diego Sona
Copyright Year
2019
Book
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

Print ISBN: 978-3-030-11722-1

Electronic ISBN: 978-3-030-11723-8

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-11723-8_13

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