Spatial decision forests for MS lesion segmentation in multi-channel MR images

Ezequiel Geremia; Bjoern H. Menze; Olivier Clatz; Ender Konukoglu; Antonio Criminisi; Nicholas Ayache

doi:10.1007/978-3-642-15705-9_14

Spatial decision forests for MS lesion segmentation in multi-channel MR images

Ezequiel Geremia
, Bjoern H. Menze
, Olivier Clatz
, Ender Konukoglu
, Antonio Criminisi
, Nicholas Ayache

I.N.R.I.A.
Microsoft Research Cambridge
Massachusetts Institute of Technology

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

86 Scopus citations

Abstract

A new algorithm is presented for the automatic segmentation of Multiple Sclerosis (MS) lesions in 3D MR images. It builds on the discriminative random decision forest framework to provide a voxel-wise probabilistic classification of the volume. Our method uses multi-channel MR intensities (T1, T2, Flair), spatial prior and long-range comparisons with 3D regions to discriminate lesions. A symmetry feature is introduced accounting for the fact that some MS lesions tend to develop in an asymmetric way. Quantitative evaluation of the data is carried out on publicly available labeled cases from the MS Lesion Segmentation Challenge 2008 dataset and demonstrates improved results over the state of the art.

Original language	English
Title of host publication	Medical Image Computing and Computer-Assisted Intervention, MICCAI2010 - 13th International Conference, Proceedings
Pages	111-118
Number of pages	8
Edition	PART 1
DOIs	https://doi.org/10.1007/978-3-642-15705-9_14
State	Published - 2010
Externally published	Yes
Event	13th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2010 - Beijing, China Duration: 20 Sep 2010 → 24 Sep 2010

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	6361 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	13th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2010
Country/Territory	China
City	Beijing
Period	20/09/10 → 24/09/10

Access to Document

10.1007/978-3-642-15705-9_14

Cite this

Geremia, E., Menze, B. H., Clatz, O., Konukoglu, E., Criminisi, A., & Ayache, N. (2010). Spatial decision forests for MS lesion segmentation in multi-channel MR images. In Medical Image Computing and Computer-Assisted Intervention, MICCAI2010 - 13th International Conference, Proceedings (PART 1 ed., pp. 111-118). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6361 LNCS, No. PART 1). https://doi.org/10.1007/978-3-642-15705-9_14

Geremia, Ezequiel ; Menze, Bjoern H. ; Clatz, Olivier et al. / Spatial decision forests for MS lesion segmentation in multi-channel MR images. Medical Image Computing and Computer-Assisted Intervention, MICCAI2010 - 13th International Conference, Proceedings. PART 1. ed. 2010. pp. 111-118 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

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title = "Spatial decision forests for MS lesion segmentation in multi-channel MR images",

abstract = "A new algorithm is presented for the automatic segmentation of Multiple Sclerosis (MS) lesions in 3D MR images. It builds on the discriminative random decision forest framework to provide a voxel-wise probabilistic classification of the volume. Our method uses multi-channel MR intensities (T1, T2, Flair), spatial prior and long-range comparisons with 3D regions to discriminate lesions. A symmetry feature is introduced accounting for the fact that some MS lesions tend to develop in an asymmetric way. Quantitative evaluation of the data is carried out on publicly available labeled cases from the MS Lesion Segmentation Challenge 2008 dataset and demonstrates improved results over the state of the art.",

author = "Ezequiel Geremia and Menze, \{Bjoern H.\} and Olivier Clatz and Ender Konukoglu and Antonio Criminisi and Nicholas Ayache",

year = "2010",

doi = "10.1007/978-3-642-15705-9\_14",

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Geremia, E, Menze, BH, Clatz, O, Konukoglu, E, Criminisi, A & Ayache, N 2010, Spatial decision forests for MS lesion segmentation in multi-channel MR images. in Medical Image Computing and Computer-Assisted Intervention, MICCAI2010 - 13th International Conference, Proceedings. PART 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 6361 LNCS, pp. 111-118, 13th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2010, Beijing, China, 20/09/10. https://doi.org/10.1007/978-3-642-15705-9_14

Spatial decision forests for MS lesion segmentation in multi-channel MR images. / Geremia, Ezequiel; Menze, Bjoern H.; Clatz, Olivier et al.
Medical Image Computing and Computer-Assisted Intervention, MICCAI2010 - 13th International Conference, Proceedings. PART 1. ed. 2010. p. 111-118 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6361 LNCS, No. PART 1).

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

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AU - Criminisi, Antonio

AU - Ayache, Nicholas

PY - 2010

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N2 - A new algorithm is presented for the automatic segmentation of Multiple Sclerosis (MS) lesions in 3D MR images. It builds on the discriminative random decision forest framework to provide a voxel-wise probabilistic classification of the volume. Our method uses multi-channel MR intensities (T1, T2, Flair), spatial prior and long-range comparisons with 3D regions to discriminate lesions. A symmetry feature is introduced accounting for the fact that some MS lesions tend to develop in an asymmetric way. Quantitative evaluation of the data is carried out on publicly available labeled cases from the MS Lesion Segmentation Challenge 2008 dataset and demonstrates improved results over the state of the art.

AB - A new algorithm is presented for the automatic segmentation of Multiple Sclerosis (MS) lesions in 3D MR images. It builds on the discriminative random decision forest framework to provide a voxel-wise probabilistic classification of the volume. Our method uses multi-channel MR intensities (T1, T2, Flair), spatial prior and long-range comparisons with 3D regions to discriminate lesions. A symmetry feature is introduced accounting for the fact that some MS lesions tend to develop in an asymmetric way. Quantitative evaluation of the data is carried out on publicly available labeled cases from the MS Lesion Segmentation Challenge 2008 dataset and demonstrates improved results over the state of the art.

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Geremia E, Menze BH, Clatz O, Konukoglu E, Criminisi A, Ayache N. Spatial decision forests for MS lesion segmentation in multi-channel MR images. In Medical Image Computing and Computer-Assisted Intervention, MICCAI2010 - 13th International Conference, Proceedings. PART 1 ed. 2010. p. 111-118. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). doi: 10.1007/978-3-642-15705-9_14

Spatial decision forests for MS lesion segmentation in multi-channel MR images

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