Automatic 3D detection and segmentation of head and neck cancer from MRI data

Baixiang Zhao; John Soraghan; Derek Grose; Trushali Doshi; Gaetano Di-Caterina

doi:10.1109/EUVIP.2018.8611759

Automatic 3D detection and segmentation of head and neck cancer from MRI data

Baixiang Zhao, John Soraghan, Derek Grose, Trushali Doshi, Gaetano Di-Caterina

Electronic And Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

2 Citations (Scopus)

42 Downloads (Pure)

Abstract

A novel algorithm for automatic head and neck 3D tumour segmentation from magnetic resonance imaging (MRI) is presented. The proposed algorithm pre-processes the MRI data slices to enhance quality and reduce artefacts. An intensity standardisation process is performed between slices, followed by cancer region segmentation of central slice, to get the correct intensity range and rough location of tumour regions. Fourier interpolation is applied to create isotropic 3D MR I volume. A new location-constrained 3D level set method segments the tumour from the interpolated MRI volume. The proposed algorithm is tested on real MRI data. The results show that the novel 3D tumour volume extraction algorithm has an improved dice score and F-measure when compared to the previous 2D and 3D segmentation method.

Original language	English
Title of host publication	Proceedings of the 2018 7th European Workshop on Visual Information Processing, EUVIP 2018
Editors	I. Tabus, C. Larabi, F. Battisti, K. Egiazarian, L. Oudre, A. Beghdadi
Place of Publication	Piscataway, NJ
Publisher	IEEE
Number of pages	6
Volume	2018-November
ISBN (Electronic)	9781538668979
DOIs	https://doi.org/10.1109/EUVIP.2018.8611759
Publication status	Published - 17 Jan 2019
Event	7th European Workshop on Visual Information Processing - Tampere, Finland Duration: 26 Nov 2018 → 28 Nov 2018

Conference

Conference	7th European Workshop on Visual Information Processing
Abbreviated title	EUVIP
Country/Territory	Finland
City	Tampere
Period	26/11/18 → 28/11/18

Keywords

magnetic resonance imaging
head and neck cancer
Fourier interpolation
fuzzy clustering
3D level set method

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10.1109/EUVIP.2018.8611759

Zhao-etal-EUVIP2018-Automatic-3D-detection-and-segmentation-of-head-and-neckAccepted author manuscript, 1.22 MB

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Zhao, B., Soraghan, J., Grose, D., Doshi, T., & Di-Caterina, G. (2019). Automatic 3D detection and segmentation of head and neck cancer from MRI data. In I. Tabus, C. Larabi, F. Battisti, K. Egiazarian, L. Oudre, & A. Beghdadi (Eds.), Proceedings of the 2018 7th European Workshop on Visual Information Processing, EUVIP 2018 (Vol. 2018-November). IEEE. https://doi.org/10.1109/EUVIP.2018.8611759

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title = "Automatic 3D detection and segmentation of head and neck cancer from MRI data",

abstract = "A novel algorithm for automatic head and neck 3D tumour segmentation from magnetic resonance imaging (MRI) is presented. The proposed algorithm pre-processes the MRI data slices to enhance quality and reduce artefacts. An intensity standardisation process is performed between slices, followed by cancer region segmentation of central slice, to get the correct intensity range and rough location of tumour regions. Fourier interpolation is applied to create isotropic 3D MR I volume. A new location-constrained 3D level set method segments the tumour from the interpolated MRI volume. The proposed algorithm is tested on real MRI data. The results show that the novel 3D tumour volume extraction algorithm has an improved dice score and F-measure when compared to the previous 2D and 3D segmentation method.",

keywords = "magnetic resonance imaging, head and neck cancer, Fourier interpolation, fuzzy clustering, 3D level set method",

author = "Baixiang Zhao and John Soraghan and Derek Grose and Trushali Doshi and Gaetano Di-Caterina",

note = "{\textcopyright} 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.; 7th European Workshop on Visual Information Processing, EUVIP ; Conference date: 26-11-2018 Through 28-11-2018",

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Zhao, B , Soraghan, J, Grose, D, Doshi, T & Di-Caterina, G 2019, Automatic 3D detection and segmentation of head and neck cancer from MRI data. in I Tabus, C Larabi, F Battisti, K Egiazarian, L Oudre & A Beghdadi (eds), Proceedings of the 2018 7th European Workshop on Visual Information Processing, EUVIP 2018. vol. 2018-November, IEEE, Piscataway, NJ, 7th European Workshop on Visual Information Processing, Tampere, Finland, 26/11/18. https://doi.org/10.1109/EUVIP.2018.8611759

Automatic 3D detection and segmentation of head and neck cancer from MRI data. / Zhao, Baixiang ; Soraghan, John; Grose, Derek et al.
Proceedings of the 2018 7th European Workshop on Visual Information Processing, EUVIP 2018. ed. / I. Tabus; C. Larabi; F. Battisti; K. Egiazarian; L. Oudre; A. Beghdadi. Vol. 2018-November Piscataway, NJ: IEEE, 2019.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

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AU - Zhao, Baixiang

AU - Soraghan, John

AU - Grose, Derek

AU - Doshi, Trushali

AU - Di-Caterina, Gaetano

N1 - © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

PY - 2019/1/17

Y1 - 2019/1/17

N2 - A novel algorithm for automatic head and neck 3D tumour segmentation from magnetic resonance imaging (MRI) is presented. The proposed algorithm pre-processes the MRI data slices to enhance quality and reduce artefacts. An intensity standardisation process is performed between slices, followed by cancer region segmentation of central slice, to get the correct intensity range and rough location of tumour regions. Fourier interpolation is applied to create isotropic 3D MR I volume. A new location-constrained 3D level set method segments the tumour from the interpolated MRI volume. The proposed algorithm is tested on real MRI data. The results show that the novel 3D tumour volume extraction algorithm has an improved dice score and F-measure when compared to the previous 2D and 3D segmentation method.

AB - A novel algorithm for automatic head and neck 3D tumour segmentation from magnetic resonance imaging (MRI) is presented. The proposed algorithm pre-processes the MRI data slices to enhance quality and reduce artefacts. An intensity standardisation process is performed between slices, followed by cancer region segmentation of central slice, to get the correct intensity range and rough location of tumour regions. Fourier interpolation is applied to create isotropic 3D MR I volume. A new location-constrained 3D level set method segments the tumour from the interpolated MRI volume. The proposed algorithm is tested on real MRI data. The results show that the novel 3D tumour volume extraction algorithm has an improved dice score and F-measure when compared to the previous 2D and 3D segmentation method.

KW - magnetic resonance imaging

KW - head and neck cancer

KW - Fourier interpolation

KW - fuzzy clustering

KW - 3D level set method

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Zhao B , Soraghan J, Grose D, Doshi T, Di-Caterina G. Automatic 3D detection and segmentation of head and neck cancer from MRI data. In Tabus I, Larabi C, Battisti F, Egiazarian K, Oudre L, Beghdadi A, editors, Proceedings of the 2018 7th European Workshop on Visual Information Processing, EUVIP 2018. Vol. 2018-November. Piscataway, NJ: IEEE. 2019 doi: 10.1109/EUVIP.2018.8611759

Automatic 3D detection and segmentation of head and neck cancer from MRI data

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