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

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

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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 languageEnglish
Title of host publicationProceedings of the 2018 7th European Workshop on Visual Information Processing, EUVIP 2018
EditorsI. Tabus, C. Larabi, F. Battisti, K. Egiazarian, L. Oudre, A. Beghdadi
Place of PublicationPiscataway, NJ
PublisherIEEE
Number of pages6
Volume2018-November
ISBN (Electronic)9781538668979
DOIs
Publication statusPublished - 17 Jan 2019
Event7th European Workshop on Visual Information Processing - Tampere, Finland
Duration: 26 Nov 201828 Nov 2018

Conference

Conference7th European Workshop on Visual Information Processing
Abbreviated titleEUVIP
CountryFinland
CityTampere
Period26/11/1828/11/18

Keywords

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

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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). Piscataway, NJ: IEEE. https://doi.org/10.1109/EUVIP.2018.8611759