Segmentation of head and neck tumours using modified U-net

Zhao, Baixiang and Soraghan, John and Di Caterina, Gaetano and Grose, Derek (2019) Segmentation of head and neck tumours using modified U-net. In: 27th European Signal Processing Conference, 2019-09-02 - 2019-09-06.

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Abstract

A new neural network for automatic head and neck cancer (HNC) segmentation from magnetic resonance imaging (MRI) is presented. The proposed neural network is based on U-net, which combines features from different resolutions to achieve end-to-end locating and segmentation of medical images. In this work, the dilated convolution is introduced into U-net, to obtain larger receptive field so that extract multi-scale features. Also, this network uses Dice loss to reduce the imbalance between classes. The proposed algorithm is trained and tested on real MRI data. The cross-validation results show that the new network outperformed the original Unet by 5% (Dice score) on head and neck tumour segmentation.

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 and Grose, Derek;
CORE (COnnecting REpositories)