Modified fuzzy c-means clustering for automatic tongue base tumour extraction from MRI data

Magnetic resonance imaging (MRI) is a widely used imaging modality to extract tumour regions to assist in radiotherapy and surgery planning. Extraction of a tongue base tumour from MRI is challenging due to variability in its shape, size, intensities and fuzzy boundaries. This paper presents a new automatic algorithm that is shown to be able to extract tongue base tumour from gadolinium-enhanced T1-weighted (T1+Gd) MRI slices. In this algorithm, knowledge of tumour location is added to the objective function of standard fuzzy c-means (FCM) to extract the tumour region. Experimental results on 9 real MRI slices demonstrate that there is good agreement between manual and automatic extraction results with dice similarity coefficient (DSC) of 0.77±0.08

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

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

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

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

Validation of a magnetic resonance imaging-based auto-contouring software tool for gross tumour delineation in head and neck cancer radiotheraphy planning

To perform statistical validation of a newly developed magnetic resonance imaging (MRI) auto-contouring software tool for gross tumour volume (GTV) delineation in head and neck tumours to assist in radiotherapy planning. Axial MRI baseline scans were obtained for 10 oropharyngeal and laryngeal cancer patients. GTV was present on 102 axial slices and auto-contoured using the modified fuzzy c-means clustering integrated with level set method (FCLSM). Peer reviewed (C-gold) manual contours were used as the reference standard to validate auto-contoured GTVs (C-auto) and mean manual contours (C-manual) from 2 expert clinicians (C1 and C2). Multiple geometrical metrics, including Dice Similarity Coefficient (DSC) were used for quantitative validation. A DSC ≥0.7 was deemed acceptable. Inter-and intra-variabilities amongst the manual contours were also validated. The 2-dimension (2D) contours were then reconstructed in 3D for GTV volume calculation, comparison and 3D visualisation. The mean DSC between C-gold and C-auto was 0.79. The mean DSC bet ween C-gold and C-manual was 0.79 and that between C1 and C2 was 0.80. The average time for GTV auto-contouring per patient was 8 minutes (range 6-13mins; mean 45seconds per axial slice) compared to 15 minutes (range 6-23mins; mean 88 seconds per axial slice) for C1. The average volume concordance between C-gold and C-auto volumes was 86. 51% compared to 74.16% between C-gold and C-manual. The average volume concordance between C1 and C2 volumes was 86.82%. This newly-designed MRI-based auto-contouring software tool shows initial acceptable results in GTV delineation of oropharyngeal and laryngeal tumours using FCLSM. This auto-contouring software tool may help reduce inter-and intra- variability and can assist clinical oncologists with time-consuming, complex radiotherapy planning