Longitudinal Multiple Sclerosis lesion segmentation using multi-view convolutional neural networks

Ariel Birenbaum; Hayit Greenspan

doi:10.1007/978-3-319-46976-8_7

Longitudinal Multiple Sclerosis lesion segmentation using multi-view convolutional neural networks

Ariel Birenbaum, Hayit Greenspan

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

62 Scopus citations

Abstract

Automatic segmentation of Multiple Sclerosis (MS) lesions is a challenging task due to their variability in shape, size, location and texture in Magnetic Resonance (MR) images. A reliable, automatic segmentation method can help diagnosis and patient follow-up while reducing the time consuming need of manual segmentation. In this paper, we present a fully automated method for MS lesion segmentation. The proposed method uses MR intensities and White Matter (WM) priors for extraction of candidate lesion voxels and uses Convolutional Neural Networks for false positive reduction. Our networks process longitudinal data, a novel contribution in the domain of MS lesion analysis. The method was tested on the ISBI 2015 dataset and obtained state-of-theart Dice results with the performance level of a trained human rater.

Original language	English
Title of host publication	Deep Learning and Data Labeling for Medical Applications - 1st International Workshop, LABELS 2016, and 2nd International Workshop, DLMIA 2016 Held in Conjunction with MICCAI 2016, Proceedings
Editors	Zhi Lu, Vasileios Belagiannis, Joao Manuel R.S. Tavares, Jaime S. Cardoso, Andrew Bradley, Joao Paulo Papa, Jacinto C. Nascimento, Marco Loog, Julien Cornebise, Gustavo Carneiro, Diana Mateus, Loic Peter
Publisher	Springer Verlag
Pages	58-67
Number of pages	10
ISBN (Print)	9783319469751
DOIs	https://doi.org/10.1007/978-3-319-46976-8_7
State	Published - 2016
Externally published	Yes
Event	1st International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2016 and 2nd International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2016 - Athens, Greece Duration: 21 Oct 2016 → 21 Oct 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10008 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	1st International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2016 and 2nd International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2016
Country/Territory	Greece
City	Athens
Period	21/10/16 → 21/10/16

Keywords

CNN
Longitudinal data
Multiple sclerosis
Segmentation

Access to Document

10.1007/978-3-319-46976-8_7

Cite this

Birenbaum, A., & Greenspan, H. (2016). Longitudinal Multiple Sclerosis lesion segmentation using multi-view convolutional neural networks. In Z. Lu, V. Belagiannis, J. M. R. S. Tavares, J. S. Cardoso, A. Bradley, J. P. Papa, J. C. Nascimento, M. Loog, J. Cornebise, G. Carneiro, D. Mateus, & L. Peter (Eds.), Deep Learning and Data Labeling for Medical Applications - 1st International Workshop, LABELS 2016, and 2nd International Workshop, DLMIA 2016 Held in Conjunction with MICCAI 2016, Proceedings (pp. 58-67). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10008 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-46976-8_7

Birenbaum, Ariel ; Greenspan, Hayit. / Longitudinal Multiple Sclerosis lesion segmentation using multi-view convolutional neural networks. Deep Learning and Data Labeling for Medical Applications - 1st International Workshop, LABELS 2016, and 2nd International Workshop, DLMIA 2016 Held in Conjunction with MICCAI 2016, Proceedings. editor / Zhi Lu ; Vasileios Belagiannis ; Joao Manuel R.S. Tavares ; Jaime S. Cardoso ; Andrew Bradley ; Joao Paulo Papa ; Jacinto C. Nascimento ; Marco Loog ; Julien Cornebise ; Gustavo Carneiro ; Diana Mateus ; Loic Peter. Springer Verlag, 2016. pp. 58-67 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{106f2b871a104bc1b0c4e1c0ebf7af3f,

title = "Longitudinal Multiple Sclerosis lesion segmentation using multi-view convolutional neural networks",

abstract = "Automatic segmentation of Multiple Sclerosis (MS) lesions is a challenging task due to their variability in shape, size, location and texture in Magnetic Resonance (MR) images. A reliable, automatic segmentation method can help diagnosis and patient follow-up while reducing the time consuming need of manual segmentation. In this paper, we present a fully automated method for MS lesion segmentation. The proposed method uses MR intensities and White Matter (WM) priors for extraction of candidate lesion voxels and uses Convolutional Neural Networks for false positive reduction. Our networks process longitudinal data, a novel contribution in the domain of MS lesion analysis. The method was tested on the ISBI 2015 dataset and obtained state-of-theart Dice results with the performance level of a trained human rater.",

keywords = "CNN, Longitudinal data, Multiple sclerosis, Segmentation",

author = "Ariel Birenbaum and Hayit Greenspan",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.; 1st International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2016 and 2nd International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2016 ; Conference date: 21-10-2016 Through 21-10-2016",

year = "2016",

doi = "10.1007/978-3-319-46976-8_7",

language = "English",

isbn = "9783319469751",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

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editor = "Zhi Lu and Vasileios Belagiannis and Tavares, {Joao Manuel R.S.} and Cardoso, {Jaime S.} and Andrew Bradley and Papa, {Joao Paulo} and Nascimento, {Jacinto C.} and Marco Loog and Julien Cornebise and Gustavo Carneiro and Diana Mateus and Loic Peter",

booktitle = "Deep Learning and Data Labeling for Medical Applications - 1st International Workshop, LABELS 2016, and 2nd International Workshop, DLMIA 2016 Held in Conjunction with MICCAI 2016, Proceedings",

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Birenbaum, A & Greenspan, H 2016, Longitudinal Multiple Sclerosis lesion segmentation using multi-view convolutional neural networks. in Z Lu, V Belagiannis, JMRS Tavares, JS Cardoso, A Bradley, JP Papa, JC Nascimento, M Loog, J Cornebise, G Carneiro, D Mateus & L Peter (eds), Deep Learning and Data Labeling for Medical Applications - 1st International Workshop, LABELS 2016, and 2nd International Workshop, DLMIA 2016 Held in Conjunction with MICCAI 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10008 LNCS, Springer Verlag, pp. 58-67, 1st International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2016 and 2nd International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2016, Athens, Greece, 21/10/16. https://doi.org/10.1007/978-3-319-46976-8_7

Longitudinal Multiple Sclerosis lesion segmentation using multi-view convolutional neural networks. / Birenbaum, Ariel; Greenspan, Hayit.
Deep Learning and Data Labeling for Medical Applications - 1st International Workshop, LABELS 2016, and 2nd International Workshop, DLMIA 2016 Held in Conjunction with MICCAI 2016, Proceedings. ed. / Zhi Lu; Vasileios Belagiannis; Joao Manuel R.S. Tavares; Jaime S. Cardoso; Andrew Bradley; Joao Paulo Papa; Jacinto C. Nascimento; Marco Loog; Julien Cornebise; Gustavo Carneiro; Diana Mateus; Loic Peter. Springer Verlag, 2016. p. 58-67 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10008 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Longitudinal Multiple Sclerosis lesion segmentation using multi-view convolutional neural networks

AU - Birenbaum, Ariel

AU - Greenspan, Hayit

N1 - Publisher Copyright: © Springer International Publishing AG 2016.

PY - 2016

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N2 - Automatic segmentation of Multiple Sclerosis (MS) lesions is a challenging task due to their variability in shape, size, location and texture in Magnetic Resonance (MR) images. A reliable, automatic segmentation method can help diagnosis and patient follow-up while reducing the time consuming need of manual segmentation. In this paper, we present a fully automated method for MS lesion segmentation. The proposed method uses MR intensities and White Matter (WM) priors for extraction of candidate lesion voxels and uses Convolutional Neural Networks for false positive reduction. Our networks process longitudinal data, a novel contribution in the domain of MS lesion analysis. The method was tested on the ISBI 2015 dataset and obtained state-of-theart Dice results with the performance level of a trained human rater.

AB - Automatic segmentation of Multiple Sclerosis (MS) lesions is a challenging task due to their variability in shape, size, location and texture in Magnetic Resonance (MR) images. A reliable, automatic segmentation method can help diagnosis and patient follow-up while reducing the time consuming need of manual segmentation. In this paper, we present a fully automated method for MS lesion segmentation. The proposed method uses MR intensities and White Matter (WM) priors for extraction of candidate lesion voxels and uses Convolutional Neural Networks for false positive reduction. Our networks process longitudinal data, a novel contribution in the domain of MS lesion analysis. The method was tested on the ISBI 2015 dataset and obtained state-of-theart Dice results with the performance level of a trained human rater.

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KW - Longitudinal data

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KW - Segmentation

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Deep Learning and Data Labeling for Medical Applications - 1st International Workshop, LABELS 2016, and 2nd International Workshop, DLMIA 2016 Held in Conjunction with MICCAI 2016, Proceedings

A2 - Lu, Zhi

A2 - Belagiannis, Vasileios

A2 - Tavares, Joao Manuel R.S.

A2 - Cardoso, Jaime S.

A2 - Bradley, Andrew

A2 - Papa, Joao Paulo

A2 - Nascimento, Jacinto C.

A2 - Loog, Marco

A2 - Cornebise, Julien

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Y2 - 21 October 2016 through 21 October 2016

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Birenbaum A, Greenspan H. Longitudinal Multiple Sclerosis lesion segmentation using multi-view convolutional neural networks. In Lu Z, Belagiannis V, Tavares JMRS, Cardoso JS, Bradley A, Papa JP, Nascimento JC, Loog M, Cornebise J, Carneiro G, Mateus D, Peter L, editors, Deep Learning and Data Labeling for Medical Applications - 1st International Workshop, LABELS 2016, and 2nd International Workshop, DLMIA 2016 Held in Conjunction with MICCAI 2016, Proceedings. Springer Verlag. 2016. p. 58-67. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-46976-8_7

Longitudinal Multiple Sclerosis lesion segmentation using multi-view convolutional neural networks

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Keywords

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