Nabla-net: A deep dag-like convolutional architecture for biomedical image segmentation

Richard McKinley; Rik Wepfer; Tom Gundersen; Franca Wagner; Andrew Chan; Roland Wiest; Mauricio Reyes

doi:10.1007/978-3-319-55524-9_12

Nabla-net: A deep dag-like convolutional architecture for biomedical image segmentation

Richard McKinley
, Rik Wepfer
, Tom Gundersen
, Franca Wagner
, Andrew Chan
, Roland Wiest
, Mauricio Reyes

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

41 Scopus citations

Abstract

Biomedical image segmentation requires both voxel-level information and global context. We report on a deep convolutional architecture which combines a fully-convolutional network for local features and an encoder-decoder network in which convolutional layers and maxpooling compute high-level features, which are then upsampled to the resolution of the initial image using further convolutional layers and tied unpooling. We apply the method to segmenting multiple sclerosis lesions and gliomas.

Original language	English
Title of host publication	Brainlesion
Subtitle of host publication	Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - Second International Workshop, BrainLes 2016, with the Challenges on BRATS, ISLES and mTOP 2016 Held in Conjunction with MICCAI 2016, Revised Selected Papers
Editors	Bjoern Menze, Mauricio Reyes, Alessandro Crimi, Oskar Maier, Stefan Winzeck, Heinz Handels
Publisher	Springer Verlag
Pages	119-128
Number of pages	10
ISBN (Print)	9783319555232
DOIs	https://doi.org/10.1007/978-3-319-55524-9_12
State	Published - 2016
Externally published	Yes
Event	2nd International Workshop on Brain Lesion, BrainLes 2016, with the challenges on Brain Tumor Segmentation BRATS, Ischemic Stroke Lesion Image Segmentation ISLES, and the Mild Traumatic Brain Injury Outcome Prediction mTOP held in conjunction with the International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016 - Athens, Greece Duration: 17 Oct 2016 → 17 Oct 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10154 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	2nd International Workshop on Brain Lesion, BrainLes 2016, with the challenges on Brain Tumor Segmentation BRATS, Ischemic Stroke Lesion Image Segmentation ISLES, and the Mild Traumatic Brain Injury Outcome Prediction mTOP held in conjunction with the International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016
Country/Territory	Greece
City	Athens
Period	17/10/16 → 17/10/16

Access to Document

10.1007/978-3-319-55524-9_12

Cite this

McKinley, R., Wepfer, R., Gundersen, T., Wagner, F., Chan, A., Wiest, R., & Reyes, M. (2016). Nabla-net: A deep dag-like convolutional architecture for biomedical image segmentation. In B. Menze, M. Reyes, A. Crimi, O. Maier, S. Winzeck, & H. Handels (Eds.), Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - Second International Workshop, BrainLes 2016, with the Challenges on BRATS, ISLES and mTOP 2016 Held in Conjunction with MICCAI 2016, Revised Selected Papers (pp. 119-128). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10154 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-55524-9_12

McKinley, Richard ; Wepfer, Rik ; Gundersen, Tom et al. / Nabla-net : A deep dag-like convolutional architecture for biomedical image segmentation. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - Second International Workshop, BrainLes 2016, with the Challenges on BRATS, ISLES and mTOP 2016 Held in Conjunction with MICCAI 2016, Revised Selected Papers. editor / Bjoern Menze ; Mauricio Reyes ; Alessandro Crimi ; Oskar Maier ; Stefan Winzeck ; Heinz Handels. Springer Verlag, 2016. pp. 119-128 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{e694dfa55c584e259bf112e06869c66c,

title = "Nabla-net: A deep dag-like convolutional architecture for biomedical image segmentation",

abstract = "Biomedical image segmentation requires both voxel-level information and global context. We report on a deep convolutional architecture which combines a fully-convolutional network for local features and an encoder-decoder network in which convolutional layers and maxpooling compute high-level features, which are then upsampled to the resolution of the initial image using further convolutional layers and tied unpooling. We apply the method to segmenting multiple sclerosis lesions and gliomas.",

author = "Richard McKinley and Rik Wepfer and Tom Gundersen and Franca Wagner and Andrew Chan and Roland Wiest and Mauricio Reyes",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.; 2nd International Workshop on Brain Lesion, BrainLes 2016, with the challenges on Brain Tumor Segmentation BRATS, Ischemic Stroke Lesion Image Segmentation ISLES, and the Mild Traumatic Brain Injury Outcome Prediction mTOP held in conjunction with the International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016 ; Conference date: 17-10-2016 Through 17-10-2016",

year = "2016",

doi = "10.1007/978-3-319-55524-9\_12",

language = "English",

isbn = "9783319555232",

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

publisher = "Springer Verlag",

pages = "119--128",

editor = "Bjoern Menze and Mauricio Reyes and Alessandro Crimi and Oskar Maier and Stefan Winzeck and Heinz Handels",

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McKinley, R, Wepfer, R, Gundersen, T, Wagner, F, Chan, A, Wiest, R & Reyes, M 2016, Nabla-net: A deep dag-like convolutional architecture for biomedical image segmentation. in B Menze, M Reyes, A Crimi, O Maier, S Winzeck & H Handels (eds), Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - Second International Workshop, BrainLes 2016, with the Challenges on BRATS, ISLES and mTOP 2016 Held in Conjunction with MICCAI 2016, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10154 LNCS, Springer Verlag, pp. 119-128, 2nd International Workshop on Brain Lesion, BrainLes 2016, with the challenges on Brain Tumor Segmentation BRATS, Ischemic Stroke Lesion Image Segmentation ISLES, and the Mild Traumatic Brain Injury Outcome Prediction mTOP held in conjunction with the International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, Athens, Greece, 17/10/16. https://doi.org/10.1007/978-3-319-55524-9_12

Nabla-net: A deep dag-like convolutional architecture for biomedical image segmentation. / McKinley, Richard; Wepfer, Rik; Gundersen, Tom et al.
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - Second International Workshop, BrainLes 2016, with the Challenges on BRATS, ISLES and mTOP 2016 Held in Conjunction with MICCAI 2016, Revised Selected Papers. ed. / Bjoern Menze; Mauricio Reyes; Alessandro Crimi; Oskar Maier; Stefan Winzeck; Heinz Handels. Springer Verlag, 2016. p. 119-128 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10154 LNCS).

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

TY - GEN

T1 - Nabla-net

T2 - 2nd International Workshop on Brain Lesion, BrainLes 2016, with the challenges on Brain Tumor Segmentation BRATS, Ischemic Stroke Lesion Image Segmentation ISLES, and the Mild Traumatic Brain Injury Outcome Prediction mTOP held in conjunction with the International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016

AU - McKinley, Richard

AU - Wepfer, Rik

AU - Gundersen, Tom

AU - Wagner, Franca

AU - Chan, Andrew

AU - Wiest, Roland

AU - Reyes, Mauricio

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

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N2 - Biomedical image segmentation requires both voxel-level information and global context. We report on a deep convolutional architecture which combines a fully-convolutional network for local features and an encoder-decoder network in which convolutional layers and maxpooling compute high-level features, which are then upsampled to the resolution of the initial image using further convolutional layers and tied unpooling. We apply the method to segmenting multiple sclerosis lesions and gliomas.

AB - Biomedical image segmentation requires both voxel-level information and global context. We report on a deep convolutional architecture which combines a fully-convolutional network for local features and an encoder-decoder network in which convolutional layers and maxpooling compute high-level features, which are then upsampled to the resolution of the initial image using further convolutional layers and tied unpooling. We apply the method to segmenting multiple sclerosis lesions and gliomas.

UR - https://www.scopus.com/pages/publications/85018680658

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DO - 10.1007/978-3-319-55524-9_12

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SN - 9783319555232

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

SP - 119

EP - 128

BT - Brainlesion

A2 - Menze, Bjoern

A2 - Reyes, Mauricio

A2 - Crimi, Alessandro

A2 - Maier, Oskar

A2 - Winzeck, Stefan

A2 - Handels, Heinz

PB - Springer Verlag

Y2 - 17 October 2016 through 17 October 2016

ER -

McKinley R, Wepfer R, Gundersen T, Wagner F, Chan A, Wiest R et al. Nabla-net: A deep dag-like convolutional architecture for biomedical image segmentation. In Menze B, Reyes M, Crimi A, Maier O, Winzeck S, Handels H, editors, Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - Second International Workshop, BrainLes 2016, with the Challenges on BRATS, ISLES and mTOP 2016 Held in Conjunction with MICCAI 2016, Revised Selected Papers. Springer Verlag. 2016. p. 119-128. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-55524-9_12

Nabla-net: A deep dag-like convolutional architecture for biomedical image segmentation

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