Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

Fuzzy Hybrid Method for the Reconstruction of 3D Models Based on CT/MRI Data

This research proposes a hybrid method for improving the segmentation accuracy of reconstructed 3D models from computed tomography/magnetic resonance imaging (CT/MRI) data. A semi-automatic hybrid method based on combination of Fuzzy C-Means clustering (FCM) and region growing (RG) is proposed. In this approach, FCM is used in the first stage as a preprocessing step in order to classify and improve images by assigning pixels to the clusters for which they have the maximum membership, and manual selection of the membership intensity map with the best contrast separation. Afterwards, automatic seed selection is performed for RG, for which a new parameter standard deviation (STD) of pixel intensities, is included. It is based on the selection of an initial seed inside a region with maximum value of STD. To evaluate the performance of the proposed method, it was compared to several other segmentation methods. Experimental results show that the proposed method overall provides better results compared to other methods in terms of accuracy. The average sensitivity and accuracy rates for cone-beam computed tomography CBCT 1 and CBCT 2 datasets are 99 %, 98.4 %, 47.2 % and 89.9 %, respectively. For MRI 1 and MRI 2 datasets, the average sensitivity and accuracy values are 99.1 %, 100 %, 75.6 % and 99.6 %, respectively. The average values for the Dice coefficient and Jaccard index for the CBCT 1 and CBCT 2 datasets are 95.88, 0.88, 0.6, and 0.51, respectively, while for MRI 1 and MRI 2 datasets, average values are 0.96, 0.93, 0.81 and 0.7, respectively, which confirms the high accuracy of the proposed method

Fuzzy Hybrid Method for the Reconstruction of 3D Models Based on CT/MRI Data

This research proposes a hybrid method for improving the segmentation accuracy of reconstructed 3D models from computed tomography/magnetic resonance imaging (CT/MRI) data. A semi-automatic hybrid method based on combination of Fuzzy C-Means clustering (FCM) and region growing (RG) is proposed. In this approach, FCM is used in the first stage as a preprocessing step in order to classify and improve images by assigning pixels to the clusters for which they have the maximum membership, and manual selection of the membership intensity map with the best contrast separation. Afterwards, automatic seed selection is performed for RG, for which a new parameter standard deviation (STD) of pixel intensities, is included. It is based on the selection of an initial seed inside a region with maximum value of STD. To evaluate the performance of the proposed method, it was compared to several other segmentation methods. Experimental results show that the proposed method overall provides better results compared to other methods in terms of accuracy. The average sensitivity and accuracy rates for cone-beam computed tomography CBCT 1 and CBCT 2 datasets are 99 %, 98.4 %, 47.2 % and 89.9 %, respectively. For MRI 1 and MRI 2 datasets, the average sensitivity and accuracy values are 99.1 %, 100 %, 75.6 % and 99.6 %, respectively. The average values for the Dice coefficient and Jaccard index for the CBCT 1 and CBCT 2 datasets are 95.88, 0.88, 0.6, and 0.51, respectively, while for MRI 1 and MRI 2 datasets, average values are 0.96, 0.93, 0.81 and 0.7, respectively, which confirms the high accuracy of the proposed method

A new approach for initial callus growth during fracture healing in long bones

The incidence of bone fracture has become a major clinical problem on a worldwide scale. In the past two decades there has been an increase in the use of computational tools to analyse the bone fracture problem. In several works, various study cases have been analysed to compare human and animal bone fracture healing. Unfortunately, there are not many publications about computational advances in this field and the existing approaches to the problem are usually similar. In this context, the objective of this work is the application of a diffusion problem in the model of the bone fragments resulting from fracture, working together with a mesh-growing algorithm that allows free growth of the callus depending on the established conditions, without a pre-meshed domain. The diffusion problem concerns the different biological magnitudes controlling the callus growth, among which Mesenchymal Stem Cells and chondrocytes concentrations were chosen, together with Tumour Necrosis Factor α and Bone Morphogenetic Protein as the factors influencing the velocity in the callus formation. A Finite Element approach was used to solve the corresponding diffusion problems, obtaining the concentration values along the entire domain and allowing detecting the zones in which biological magnitudes reach the necessary thresholds for callus growth. The callus growth is guided by a geometrical algorithm which performs an additional mesh generation process (self-added mesh) at each step of the iterative procedure until complete callus formation. The proposed approach was applied to different types of diaphyseal femoral fractures treated by means of intramedullary nailing. Axisymmetric models based on triangular quadratic elements were used, obtaining results in good agreement with clinical evidence of these kinds of fractures. The algorithm proposed has the advantage of a natural callus growth, without the existence of a previous mesh that may affect the conditions and direction of growth. The approach is intended for the initial phase of callus growth. Future work will address the implementation of the corresponding formulations for tissue transformation and bone remodelling in order to achieve complete fracture healing

Development of advanced 3D medical analysis tools for clinical training, diagnosis and treatment

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The objective of this PhD research was the development of novel 3D interactive medical platforms for medical image analysis, simulation and visualisation, with a focus on oncology images to support clinicians in managing the increasing amount of data provided by several medical image modalities. DoctorEye and Automatic Tumour Detector platforms were developed through constant interaction and feedback from expert clinicians, integrating a number of innovations in algorithms and methods, concerning image handling, segmentation, annotation, visualisation and plug-in technologies. DoctorEye is already being used in a related tumour modelling EC project (ContraCancrum) and offers several robust algorithms and tools for fast annotation, 3D visualisation and measurements to assist the clinician in better understanding the pathology of the brain area and define the treatment. It is free to use upon request and offers a user friendly environment for clinicians as it simplifies the implementation of complex algorithms and methods. It integrates a sophisticated, simple-to-use plug-in technology allowing researchers to add algorithms and methods (e.g. tumour growth and simulation algorithms for improving therapy planning) and interactively check the results. Apart from diagnostic and research purposes, it supports clinical training as it allows an expert clinician to evaluate a clinical delineation by different clinical users. The Automatic Tumour Detector focuses on abdominal images, which are more complex than those of the brain. It supports full automatic 3D detection of kidney pathology in real-time as well as 3D advanced visualisation and measurements. This is achieved through an innovative method implementing Templates. They contain rules and parameters for the Automatic Recognition Framework defined interactively by engineers based on clinicians’ 3D Golden Standard models. The Templates enable the automatic detection of kidneys and their possible abnormalities (tumours, stones and cysts). The system also supports the transmission of these Templates to another expert for a second opinion. Future versions of the proposed platforms could integrate even more sophisticated algorithms and tools and offer fully computer-aided identification of a variety of other organs and their dysfunctions

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

Tensor based multichannel reconstruction for breast tumours identification from DCE-MRIs

A new methodology based on tensor algebra that uses a higher order singular value decomposition to perform three-dimensional voxel reconstruction from a series of temporal images obtained using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is proposed. Principal component analysis (PCA) is used to robustly extract the spatial and temporal image features and simultaneously de-noise the datasets. Tumour segmentation on enhanced scaled (ES) images performed using a fuzzy C-means (FCM) cluster algorithm is compared with that achieved using the proposed tensorial framework. The proposed algorithm explores the correlations between spatial and temporal features in the tumours. The multi-channel reconstruction enables improved breast tumour identification through enhanced de-noising and improved intensity consistency. The reconstructed tumours have clear and continuous boundaries; furthermore the reconstruction shows better voxel clustering in tumour regions of interest. A more homogenous intensity distribution is also observed, enabling improved image contrast between tumours and background, especially in places where fatty tissue is imaged. The fidelity of reconstruction is further evaluated on the basis of five new qualitative metrics. Results confirm the superiority of the tensorial approach. The proposed reconstruction metrics should also find future applications in the assessment of other reconstruction algorithms