Lifted auto-context forests for brain tumour segmentation

Loic Le Folgoc; Aditya V. Nori; Siddharth Ancha; Antonio Criminisi

doi:10.1007/978-3-319-55524-9_17

Lifted auto-context forests for brain tumour segmentation

Loic Le Folgoc
, Aditya V. Nori
, Siddharth Ancha
, Antonio Criminisi

Microsoft Research Cambridge

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

Résumé

We revisit Auto-Context Forests for brain tumour segmentation in multi-channel magnetic resonance images, where semantic context is progressively built and refined via successive layers of Decision Forests (DFs). Specifically, we make the following contributions: (1) improved generalization via an efficient node-splitting criterion based on holdout estimates, (2) increased compactness at a tree-level, thereby yielding shallow discriminative ensembles trained orders of magnitude faster, and (3) guided semantic bagging that exposes latent data-space semantics captured by forest pathways. The proposed framework is practical: the per-layer training is fast, modular and robust. It was a top performer in the MICCAI 2016 BRATS (Brain Tumour Segmentation) challenge, and this paper aims to discuss and provide details about the challenge entry.

langue originale	Anglais
titre	Brainlesion
Sous-titre	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
rédacteurs en chef	Bjoern Menze, Mauricio Reyes, Alessandro Crimi, Oskar Maier, Stefan Winzeck, Heinz Handels
Editeur	Springer Verlag
Pages	171-183
Nombre de pages	13
ISBN (imprimé)	9783319555232
Les DOIs	https://doi.org/10.1007/978-3-319-55524-9_17
état	Publié - 1 janv. 2016
Modification externe	Oui
Evénement	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, Grcce Durée: 17 oct. 2016 → 17 oct. 2016

Série de publications

Nom	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10154 LNCS
ISSN (imprimé)	0302-9743
ISSN (Electronique)	1611-3349

Une conférence

Une conférence	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
Pays/Territoire	Grcce
La ville	Athens
période	17/10/16 → 17/10/16

Accès au document

10.1007/978-3-319-55524-9_17

Contient cette citation

Le Folgoc, L., Nori, A. V., Ancha, S., & Criminisi, A. (2016). Lifted auto-context forests for brain tumour segmentation. Dans 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 (p. 171-183). (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_17

Le Folgoc, Loic ; Nori, Aditya V. ; Ancha, Siddharth et al. / Lifted auto-context forests for brain tumour 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. Editeur / Bjoern Menze ; Mauricio Reyes ; Alessandro Crimi ; Oskar Maier ; Stefan Winzeck ; Heinz Handels. Springer Verlag, 2016. p. 171-183 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "We revisit Auto-Context Forests for brain tumour segmentation in multi-channel magnetic resonance images, where semantic context is progressively built and refined via successive layers of Decision Forests (DFs). Specifically, we make the following contributions: (1) improved generalization via an efficient node-splitting criterion based on holdout estimates, (2) increased compactness at a tree-level, thereby yielding shallow discriminative ensembles trained orders of magnitude faster, and (3) guided semantic bagging that exposes latent data-space semantics captured by forest pathways. The proposed framework is practical: the per-layer training is fast, modular and robust. It was a top performer in the MICCAI 2016 BRATS (Brain Tumour Segmentation) challenge, and this paper aims to discuss and provide details about the challenge entry.",

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Le Folgoc, L, Nori, AV, Ancha, S & Criminisi, A 2016, Lifted auto-context forests for brain tumour segmentation. Dans 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, p. 171-183, 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, Grcce, 17/10/16. https://doi.org/10.1007/978-3-319-55524-9_17

Lifted auto-context forests for brain tumour segmentation. / Le Folgoc, Loic; Nori, Aditya V.; Ancha, Siddharth 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. 171-183 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol 10154 LNCS).

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

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T1 - Lifted auto-context forests for brain tumour segmentation

AU - Le Folgoc, Loic

AU - Nori, Aditya V.

AU - Ancha, Siddharth

AU - Criminisi, Antonio

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

PY - 2016/1/1

Y1 - 2016/1/1

N2 - We revisit Auto-Context Forests for brain tumour segmentation in multi-channel magnetic resonance images, where semantic context is progressively built and refined via successive layers of Decision Forests (DFs). Specifically, we make the following contributions: (1) improved generalization via an efficient node-splitting criterion based on holdout estimates, (2) increased compactness at a tree-level, thereby yielding shallow discriminative ensembles trained orders of magnitude faster, and (3) guided semantic bagging that exposes latent data-space semantics captured by forest pathways. The proposed framework is practical: the per-layer training is fast, modular and robust. It was a top performer in the MICCAI 2016 BRATS (Brain Tumour Segmentation) challenge, and this paper aims to discuss and provide details about the challenge entry.

AB - We revisit Auto-Context Forests for brain tumour segmentation in multi-channel magnetic resonance images, where semantic context is progressively built and refined via successive layers of Decision Forests (DFs). Specifically, we make the following contributions: (1) improved generalization via an efficient node-splitting criterion based on holdout estimates, (2) increased compactness at a tree-level, thereby yielding shallow discriminative ensembles trained orders of magnitude faster, and (3) guided semantic bagging that exposes latent data-space semantics captured by forest pathways. The proposed framework is practical: the per-layer training is fast, modular and robust. It was a top performer in the MICCAI 2016 BRATS (Brain Tumour Segmentation) challenge, and this paper aims to discuss and provide details about the challenge entry.

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DO - 10.1007/978-3-319-55524-9_17

M3 - Conference contribution

AN - SCOPUS:85018713475

SN - 9783319555232

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

SP - 171

EP - 183

BT - Brainlesion

A2 - Menze, Bjoern

A2 - Reyes, Mauricio

A2 - Crimi, Alessandro

A2 - Maier, Oskar

A2 - Winzeck, Stefan

A2 - Handels, Heinz

PB - Springer Verlag

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

Y2 - 17 October 2016 through 17 October 2016

ER -

Le Folgoc L, Nori AV, Ancha S, Criminisi A. Lifted auto-context forests for brain tumour segmentation. Dans Menze B, Reyes M, Crimi A, Maier O, Winzeck S, Handels H, rédacteurs en chef, 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. 171-183. (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_17

Lifted auto-context forests for brain tumour segmentation

Résumé

Série de publications

Une conférence

Accès au document

Empreinte digitale

Contient cette citation