Automatic 3D segmentation of MRI data for detection of head and neck cancerous lymph nodes

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Zhao, Baixiang and Soraghan, John and Di Caterina, Gaetano and Petropoulakis, Lykourgos and Grose, Derek and Doshi, Trushali; (2018) Automatic 3D segmentation of MRI data for detection of head and neck cancerous lymph nodes. In: 2018 Signal Processing. IEEE, POL, pp. 298-303. ISBN 9788362065332 (https://doi.org/10.23919/SPA.2018.8563420)

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Abstract

A novel algorithm for automatic 3D segmentation of magnetic resonance imaging (MRI) data for detection of head and neck cancerous lymph nodes (LN)) is presented in this paper. The proposed algorithm pre-processes the MRI data slices to enhance quality and reduce artefacts. A modified Fuzzy c-mean process is performed through all slices, followed by a probability map which refines the clustering results, to detect the approximate position of cancerous lymph nodes. Fourier interpolation is applied to create an isotropic 3D MRI volume. A new 3D level set method segments the tumour from the interpolated MRI volume. The proposed algorithm is tested on synthetic and real MRI data. The results show that the novel cancerous lymph nodes 3D volume extraction algorithm has over 0.9 Dice similarity score on synthetic data and 0.7 on real MRI data. The F-measure is 0.92 on synthetic data and 0.75 on real data.

ORCID iDs

Zhao, Baixiang ORCID logoORCID: https://orcid.org/0000-0002-3855-8718, Soraghan, John ORCID logoORCID: https://orcid.org/0000-0003-4418-7391, Di Caterina, Gaetano ORCID logoORCID: https://orcid.org/0000-0002-7256-0897, Petropoulakis, Lykourgos ORCID logoORCID: https://orcid.org/0000-0003-3230-9670, Grose, Derek and Doshi, Trushali;
CORE (COnnecting REpositories)