Automatic detection of larynx cancer from contrast-enhanced magnetic resonance images

Detection of larynx cancer from medical imaging is important for the quantification and for the definition of target volumes in radiotherapy treatment planning (RTP). Magnetic resonance imaging (MRI) is being increasingly used in RTP due to its high resolution and excellent soft tissue contrast. Manually detecting larynx cancer from sequential MRI is time consuming and subjective. The large diversity of cancer in terms of geometry, non-distinct boundaries combined with the presence of normal anatomical regions close to the cancer regions necessitates the development of automatic and robust algorithms for this task. A new automatic algorithm for the detection of larynx cancer from 2D gadoliniumenhanced T1-weighted (T1+Gd) MRI to assist clinicians in RTP is presented. The algorithm employs edge detection using spatial neighborhood information of pixels and incorporates this information in a fuzzy c-means clustering process to robustly separate different tissues types. Furthermore, it utilizes the information of the expected cancerous location for cancer regions labeling. Comparison of this automatic detection system with manual clinical detection on real T1+Gd axial MRI slices of 2 patients (24 MRI slices) with visible larynx cancer yields an average dice similarity coefficient of 0.78±0.04 and average root mean square error of 1.82±0.28 mm. Preliminary results show that this fully automatic system can assist clinicians in RTP by obtaining quantifiable and non-subjective repeatable detection results in a particular time-efficient and unbiased fashion

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

A novel decentralised system architecture for multi-camera target tracking

Target tracking in a multi-camera system is an active and challenging research that in many systems requires video synchronisation and knowledge of the camera set-up and layout. In this paper a highly flexible, modular and decentralised system architecture is presented for multi-camera target tracking with relaxed synchronisation constraints among camera views. Moreover, the system does not rely on positional information to handle camera hand-off events. As a practical application, the system itself can, at any time, automatically select the best target view available, to implicitly solve occlusion. Further, to validate the proposed architecture, an extension to a multi-camera environment of the colour-based IMS-SWAD tracker is used. The experimental results show that the tracker can successfully track a chosen target in multiple views, in both indoor and outdoor environments, with non-overlapping and overlapping camera views

Automatic pharynx and larynx cancer segmentation framework (PLCSF) on contrast enhanced MR images

A novel and effective pharynx and larynx cancer segmentation framework (PLCSF) is presented for automatic base of tongue and larynx cancer segmentation from gadolinium-enhanced T1-weighted magnetic resonance images (MRI). The aim of the proposed PLCSF is to assist clinicians in radiotherapy treatment planning. The initial processing of MRI data in PLCSF includes cropping of region of interest; reduction of artefacts and detection of the throat region for the location prior. Further, modified fuzzy c-means clustering is developed to robustly separate candidate cancer pixels from other tissue types. In addition, region-based level set method is evolved to ensure spatial smoothness for the final segmentation boundary after noise removal using non-linear and morphological filtering. Validation study of PLCSF on 102 axial MRI slices demonstrate mean dice similarity coefficient of 0.79 and mean modified Hausdorff distance of 2.2 mm when compared with manual segmentations. Comparison of PLCSF with other algorithms validates the robustness of the PLCSF. Inter- and intra-variability calculations from manual segmentations suggest that PLCSF can help to reduce the human subjectivity

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

3-dimensional throat region segmentation from MRI data based on fourier interpolation and 3-dimensional level set methods

A new algorithm for 3D throat region segmentation from magnetic resonance imaging (MRI) is presented. The proposed algorithm initially pre-processes the MRI data to increase the contrast between the throat region and its surrounding tissues and also to reduce artifacts. Isotropic 3D volume is reconstructed using Fast Fourier Transform based interpolation. Furthermore, a cube encompassing the throat region is evolved using level set method to form a smooth 3D boundary of the throat region. The results of the proposed algorithm on real and synthetic MRI data are used to validate the robustness and accuracy of the algorithm

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

A rapid high-performance semi-automated tool to measure total kidney volume from MRI in autosomal dominant polycystic kidney disease.

OBJECTIVES: To develop a high-performance, rapid semi-automated method (Sheffield TKV Tool) for measuring total kidney volume (TKV) from magnetic resonance images (MRI) in patients with autosomal dominant polycystic kidney disease (ADPKD). METHODS: TKV was initially measured in 61 patients with ADPKD using the Sheffield TKV Tool and its performance compared to manual segmentation and other published methods (ellipsoidal, mid-slice, MIROS). It was then validated using an external dataset of MRI scans from 65 patients with ADPKD. RESULTS: Sixty-one patients (mean age 45 ± 14 years, baseline eGFR 76 ± 32 ml/min/1.73 m2) with ADPKD had a wide range of TKV (258-3680 ml) measured manually. The Sheffield TKV Tool was highly accurate (mean volume error 0.5 ± 5.3% for right kidney, - 0.7 ± 5.5% for left kidney), reproducible (intra-operator variability - 0.2 ± 1.3%; inter-operator variability 1.1 ± 2.9%) and outperformed published methods. It took less than 6 min to execute and performed consistently with high accuracy in an external MRI dataset of T2-weighted sequences with TKV acquired using three different scanners and measured using a different segmentation methodology (mean volume error was 3.45 ± 3.96%, n = 65). CONCLUSIONS: The Sheffield TKV Tool is operator friendly, requiring minimal user interaction to rapidly, accurately and reproducibly measure TKV in this, the largest reported unselected European patient cohort with ADPKD. It is more accurate than estimating equations and its accuracy is maintained at larger kidney volumes than previously reported with other semi-automated methods. It is free to use, can run as an independent executable and will accelerate the application of TKV as a prognostic biomarker for ADPKD into clinical practice. KEY POINTS: • This new semi-automated method (Sheffield TKV Tool) to measure total kidney volume (TKV) will facilitate the routine clinical assessment of patients with ADPKD. • Measuring TKV manually is time consuming and laborious. • TKV is a prognostic indicator in ADPKD and the only imaging biomarker approved by the FDA and EMA

Segmentation and quantification of oropharynx and larynx tumours from MRI data

Radiation therapy (RT) is often offered as the primary treatment for the head and neck cancer. Quantitative analysis and volumetric measurements in RT require segmentation of a tumour (gross tumour volume (GTV)) and other anatomical structures (organs at risk). The current tumour segmentation technique, manual segmentation, using medical imaging is subject to high observer variability. Thus, this thesis describes new image processing methods for oropharynx and larynx tumours (segmentation and quantification) analysis from magnetic resonance imaging (MRI). An integrated approach has been developed, including data size and MRI artefacts reduction, throat region detection, extraction and segmentation oftumour (GTV) region with 3D reconstruction and quantification. Initially, a novel 2D automatic pharyngeal and laryngeal cancer segmentation framework (PLCSF) is proposed for oropharynx and larynx tumours segmentation from contrast enhanced T1-weighted axial MRI slices. In PLCSF, prior to segmentation, local entropy minimisation technique is employed to reduce intensity in homogeneity and new fuzzy rules based method is used for the throat region detection. Moreover a novel modified fuzzy c-means (FCM) clustering algorithm ispresented that is shown to robustly extract a tumour region compared to standard FCM clustering. Then a tumour segmentation boundary (outline) is obtained using region-based level set method after noise removal using non-linear filtering. The advantage of the proposed PLCSF lies in its ability to obtain a comparable tumour outlines even in presence of artefacts, heterogeneous tumour profile and fuzzy boundaries. Further an approach for three dimensional (3D) reconstruction and quantification of a tumour is presented. In this approach tumour outlines obtained from contiguous 2D slices are reconstructed in 3D using interpolation and rectangular mesh generation technique and volume is calculated using slice profile. Experimental results of PLCSF with volumetric measurements for oropharynx and larynx tumours on synthetic and real MRI data demonstrate that this tool may help reduce observer variations and can assist clinical oncologists with time-consuming,complex radiotherapy treatment planning. Finally, a novel automatic 3D throat region segmentation algorithm is presented. This algorithm efficiently combines Fourier interpolation and 3D level set segmentation technique to improve the accuracy of the segmentation results.Radiation therapy (RT) is often offered as the primary treatment for the head and neck cancer. Quantitative analysis and volumetric measurements in RT require segmentation of a tumour (gross tumour volume (GTV)) and other anatomical structures (organs at risk). The current tumour segmentation technique, manual segmentation, using medical imaging is subject to high observer variability. Thus, this thesis describes new image processing methods for oropharynx and larynx tumours (segmentation and quantification) analysis from magnetic resonance imaging (MRI). An integrated approach has been developed, including data size and MRI artefacts reduction, throat region detection, extraction and segmentation oftumour (GTV) region with 3D reconstruction and quantification. Initially, a novel 2D automatic pharyngeal and laryngeal cancer segmentation framework (PLCSF) is proposed for oropharynx and larynx tumours segmentation from contrast enhanced T1-weighted axial MRI slices. In PLCSF, prior to segmentation, local entropy minimisation technique is employed to reduce intensity in homogeneity and new fuzzy rules based method is used for the throat region detection. Moreover a novel modified fuzzy c-means (FCM) clustering algorithm ispresented that is shown to robustly extract a tumour region compared to standard FCM clustering. Then a tumour segmentation boundary (outline) is obtained using region-based level set method after noise removal using non-linear filtering. The advantage of the proposed PLCSF lies in its ability to obtain a comparable tumour outlines even in presence of artefacts, heterogeneous tumour profile and fuzzy boundaries. Further an approach for three dimensional (3D) reconstruction and quantification of a tumour is presented. In this approach tumour outlines obtained from contiguous 2D slices are reconstructed in 3D using interpolation and rectangular mesh generation technique and volume is calculated using slice profile. Experimental results of PLCSF with volumetric measurements for oropharynx and larynx tumours on synthetic and real MRI data demonstrate that this tool may help reduce observer variations and can assist clinical oncologists with time-consuming,complex radiotherapy treatment planning. Finally, a novel automatic 3D throat region segmentation algorithm is presented. This algorithm efficiently combines Fourier interpolation and 3D level set segmentation technique to improve the accuracy of the segmentation results

Semi-automatic segmentation of tongue tumors from magnetic resonance imaging

Radiation therapy is one of the most effective modalities for treatment of tongue cancer. In order to optimize radiation dose to the tumor region, it is necessary to segment the tumor from normal region. This paper presents a new semiautomatic algorithm that is demonstrated to be able to segment tongue tumor from gadolinium-enhanced T1-weighted magnetic resonance imaging (MRI) to support radiation planning. This algorithm takes sequential MRI slices with visible tongue tumor. The Tumor's region from each slice is segmented using three steps (i) preprocessing, (ii) initialization and (iii) localized region-based level set segmentation. The segmentation results obtained from proposed algorithm are compared with manual segmentation from clinical expert. Results from 9 MRI slices show that there is a good overlap between semi-automatic and manual segmentation results with dice similarity coefficient (DSC) of 0.87±0.05