Multivariate methods for interpretable analysis of magnetic resonance spectroscopy data in brain tumour diagnosis

Malignant tumours of the brain represent one of the most difficult to treat types of cancer due to the sensitive organ they affect. Clinical management of the pathology becomes even more intricate as the tumour mass increases due to proliferation, suggesting that an early and accurate diagnosis is vital for preventing it from its normal course of development. The standard clinical practise for diagnosis includes invasive techniques that might be harmful for the patient, a fact that has fostered intensive research towards the discovery of alternative non-invasive brain tissue measurement methods, such as nuclear magnetic resonance. One of its variants, magnetic resonance imaging, is already used in a regular basis to locate and bound the brain tumour; but a complementary variant, magnetic resonance spectroscopy, despite its higher spatial resolution and its capability to identify biochemical metabolites that might become biomarkers of tumour within a delimited area, lags behind in terms of clinical use, mainly due to its difficult interpretability. The interpretation of magnetic resonance spectra corresponding to brain tissue thus becomes an interesting field of research for automated methods of knowledge extraction such as machine learning, always understanding its secondary role behind human expert medical decision making. The current thesis aims at contributing to the state of the art in this domain by providing novel techniques for assistance of radiology experts, focusing on complex problems and delivering interpretable solutions. In this respect, an ensemble learning technique to accurately discriminate amongst the most aggressive brain tumours, namely glioblastomas and metastases, has been designed; moreover, a strategy to increase the stability of biomarker identification in the spectra by means of instance weighting is provided. From a different analytical perspective, a tool based on signal source separation, guided by tumour type-specific information has been developed to assess the existence of different tissues in the tumoural mass, quantifying their influence in the vicinity of tumoural areas. This development has led to the derivation of a probabilistic interpretation of some source separation techniques, which provide support for uncertainty handling and strategies for the estimation of the most accurate number of differentiated tissues within the analysed tumour volumes. The provided strategies should assist human experts through the use of automated decision support tools and by tackling interpretability and accuracy from different anglesEls tumors cerebrals malignes representen un dels tipus de càncer més difícils de tractar degut a la sensibilitat de l’òrgan que afecten. La gestió clínica de la patologia esdevé encara més complexa quan la massa tumoral s'incrementa degut a la proliferació incontrolada de cèl·lules; suggerint que una diagnosis precoç i acurada és vital per prevenir el curs natural de desenvolupament. La pràctica clínica estàndard per a la diagnosis inclou la utilització de tècniques invasives que poden arribar a ser molt perjudicials per al pacient, factor que ha fomentat la recerca intensiva cap al descobriment de mètodes alternatius de mesurament dels teixits del cervell, tals com la ressonància magnètica nuclear. Una de les seves variants, la imatge de ressonància magnètica, ja s'està actualment utilitzant de forma regular per localitzar i delimitar el tumor. Així mateix, una variant complementària, la espectroscòpia de ressonància magnètica, malgrat la seva alta resolució espacial i la seva capacitat d'identificar metabòlits bioquímics que poden esdevenir biomarcadors de tumor en una àrea delimitada, està molt per darrera en termes d'ús clínic, principalment per la seva difícil interpretació. Per aquest motiu, la interpretació dels espectres de ressonància magnètica corresponents a teixits del cervell esdevé un interessant camp de recerca en mètodes automàtics d'extracció de coneixement tals com l'aprenentatge automàtic, sempre entesos com a una eina d'ajuda per a la presa de decisions per part d'un metge expert humà. La tesis actual té com a propòsit la contribució a l'estat de l'art en aquest camp mitjançant l'aportació de noves tècniques per a l'assistència d'experts radiòlegs, centrades en problemes complexes i proporcionant solucions interpretables. En aquest sentit, s'ha dissenyat una tècnica basada en comitè d'experts per a una discriminació acurada dels diferents tipus de tumors cerebrals agressius, anomenats glioblastomes i metàstasis; a més, es proporciona una estratègia per a incrementar l'estabilitat en la identificació de biomarcadors presents en un espectre mitjançant una ponderació d'instàncies. Des d'una perspectiva analítica diferent, s'ha desenvolupat una eina basada en la separació de fonts, guiada per informació específica de tipus de tumor per a avaluar l'existència de diferents tipus de teixits existents en una massa tumoral, quantificant-ne la seva influència a les regions tumorals veïnes. Aquest desenvolupament ha portat cap a la derivació d'una interpretació probabilística d'algunes d'aquestes tècniques de separació de fonts, proporcionant suport per a la gestió de la incertesa i estratègies d'estimació del nombre més acurat de teixits diferenciats en cada un dels volums tumorals analitzats. Les estratègies proporcionades haurien d'assistir els experts humans en l'ús d'eines automatitzades de suport a la decisió, donada la interpretabilitat i precisió que presenten des de diferents angles

A review on a deep learning perspective in brain cancer classification

AWorld Health Organization (WHO) Feb 2018 report has recently shown that mortality rate due to brain or central nervous system (CNS) cancer is the highest in the Asian continent. It is of critical importance that cancer be detected earlier so that many of these lives can be saved. Cancer grading is an important aspect for targeted therapy. As cancer diagnosis is highly invasive, time consuming and expensive, there is an immediate requirement to develop a non-invasive, cost-effective and efficient tools for brain cancer characterization and grade estimation. Brain scans using magnetic resonance imaging (MRI), computed tomography (CT), as well as other imaging modalities, are fast and safer methods for tumor detection. In this paper, we tried to summarize the pathophysiology of brain cancer, imaging modalities of brain cancer and automatic computer assisted methods for brain cancer characterization in a machine and deep learning paradigm. Another objective of this paper is to find the current issues in existing engineering methods and also project a future paradigm. Further, we have highlighted the relationship between brain cancer and other brain disorders like stroke, Alzheimer’s, Parkinson’s, andWilson’s disease, leukoriaosis, and other neurological disorders in the context of machine learning and the deep learning paradigm

Texture analysis of multimodal magnetic resonance images in support of diagnostic classification of childhood brain tumours

Primary brain tumours are recognised as the most common form of solid tumours in children, with pilocytic astrocytoma, medulloblastoma and ependymoma being found most frequently. Despite their high mortality rate, early detection can be facilitated through the use of Magnetic Resonance Imaging (MRI), which is the preferred scanning technique for paediatric patients. MRI offers a variety of imaging sequences through structural and functional imaging, as well as providing complementary tissue information. However visual examination of MR images provides limited ability to characterise distinct histological types of brain tumours. In order to improve diagnostic classification, we explore the use of a computer-aided system based on texture analysis (TA) methods. TA has been applied on conventional MRI but has been less commonly studied on diffusion MRI of brain-related pathology. Furthermore, the combination of textural features derived from both imaging approaches has not yet been widely studied. In this thesis, the aim of the research is to investigate TA based on multi-centre multimodal MRI, in order to provide more comprehensive information and develop an automated processing framework for the classification of childhood brain tumours

Artificial Intelligence in Brain Tumour Surgery—An Emerging Paradigm

Artificial intelligence (AI) platforms have the potential to cause a paradigm shift in brain tumour surgery. Brain tumour surgery augmented with AI can result in safer and more effective treatment. In this review article, we explore the current and future role of AI in patients undergoing brain tumour surgery, including aiding diagnosis, optimising the surgical plan, providing support during the operation, and better predicting the prognosis. Finally, we discuss barriers to the successful clinical implementation, the ethical concerns, and we provide our perspective on how the field could be advanced

The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences. Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low-and high-grade glioma patients-manually annotated by up to four raters-and to 65 comparable scans generated using tumor image simulation software. Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74%-85%), illustrating the difficulty of this task. We found that different algorithms worked best for different sub-regions (reaching performance comparable to human inter-rater variability), but that no single algorithm ranked in the top for all sub-regions simultaneously. Fusing several good algorithms using a hierarchical majority vote yielded segmentations that consistently ranked above all individual algorithms, indicating remaining opportunities for further methodological improvements. The BRATS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource

Investigating the role of machine learning and deep learning techniques in medical image segmentation

openThis work originates from the growing interest of the medical imaging community in the application of machine learning techniques and, from deep learning to improve the accuracy of cancerscreening. The thesis is structured into two different tasks. In the first part, magnetic resonance images were analysed in order to support clinical experts in the treatment of patients with brain tumour metastases (BM). The main topic related to this study was to investigate whether BM segmentation may be approached successfully by two supervised ML classifiers belonging to feature-based and deep learning approaches, respectively. SVM and V-Net Convolutional Neural Network model are selected from the literature as representative of the two approaches. The second task related to this thesisis illustrated the development of a deep learning study aimed to process and classify lesions in mammograms with the use of slender neural networks. Mammography has a central role in screening and diagnosis of breast lesions. Deep Convolutional Neural Networks have shown a great potentiality to address the issue of early detection of breast cancer with an acceptable level of accuracy and reproducibility. A traditional convolution network was compared with a novel one obtained making use of much more efficient depth wise separable convolution layers. As a final goal to integrate the system developed in clinical practice, for both fields studied, all the Medical Imaging and Pattern Recognition algorithmic solutions have been integrated into a MATLAB® software packageopenInformatica e matematica del calcologonella gloriaGonella, Glori

Supervised learning-based multimodal MRI brain image analysis

Medical imaging plays an important role in clinical procedures related to cancer, such as diagnosis, treatment selection, and therapy response evaluation. Magnetic resonance imaging (MRI) is one of the most popular acquisition modalities which is widely used in brain tumour analysis and can be acquired with different acquisition protocols, e.g. conventional and advanced. Automated segmentation of brain tumours in MR images is a difficult task due to their high variation in size, shape and appearance. Although many studies have been conducted, it still remains a challenging task and improving accuracy of tumour segmentation is an ongoing field. The aim of this thesis is to develop a fully automated method for detection and segmentation of the abnormal tissue associated with brain tumour (tumour core and oedema) from multimodal MRI images. In this thesis, firstly, the whole brain tumour is segmented from fluid attenuated inversion recovery (FLAIR) MRI, which is commonly acquired in clinics. The segmentation is achieved using region-wise classification, in which regions are derived from superpixels. Several image features including intensity-based, Gabor textons, fractal analysis and curvatures are calculated from each superpixel within the entire brain area in FLAIR MRI to ensure a robust classification. Extremely randomised trees (ERT) classifies each superpixel into tumour and non-tumour. Secondly, the method is extended to 3D supervoxel based learning for segmentation and classification of tumour tissue subtypes in multimodal MRI brain images. Supervoxels are generated using the information across the multimodal MRI data set. This is then followed by a random forests (RF) classifier to classify each supervoxel into tumour core, oedema or healthy brain tissue. The information from the advanced protocols of diffusion tensor imaging (DTI), i.e. isotropic (p) and anisotropic (q) components is also incorporated to the conventional MRI to improve segmentation accuracy. Thirdly, to further improve the segmentation of tumour tissue subtypes, the machine-learned features from fully convolutional neural network (FCN) are investigated and combined with hand-designed texton features to encode global information and local dependencies into feature representation. The score map with pixel-wise predictions is used as a feature map which is learned from multimodal MRI training dataset using the FCN. The machine-learned features, along with hand-designed texton features are then applied to random forests to classify each MRI image voxel into normal brain tissues and different parts of tumour. The methods are evaluated on two datasets: 1) clinical dataset, and 2) publicly available Multimodal Brain Tumour Image Segmentation Benchmark (BRATS) 2013 and 2017 dataset. The experimental results demonstrate the high detection and segmentation performance of the III single modal (FLAIR) method. The average detection sensitivity, balanced error rate (BER) and the Dice overlap measure for the segmented tumour against the ground truth for the clinical data are 89.48%, 6% and 0.91, respectively; whilst, for the BRATS dataset, the corresponding evaluation results are 88.09%, 6% and 0.88, respectively. The corresponding results for the tumour (including tumour core and oedema) in the case of multimodal MRI method are 86%, 7%, 0.84, for the clinical dataset and 96%, 2% and 0.89 for the BRATS 2013 dataset. The results of the FCN based method show that the application of the RF classifier to multimodal MRI images using machine-learned features based on FCN and hand-designed features based on textons provides promising segmentations. The Dice overlap measure for automatic brain tumor segmentation against ground truth for the BRATS 2013 dataset is 0.88, 0.80 and 0.73 for complete tumor, core and enhancing tumor, respectively, which is competitive to the state-of-the-art methods. The corresponding results for BRATS 2017 dataset are 0.86, 0.78 and 0.66 respectively. The methods demonstrate promising results in the segmentation of brain tumours. This provides a close match to expert delineation across all grades of glioma, leading to a faster and more reproducible method of brain tumour detection and delineation to aid patient management. In the experiments, texton has demonstrated its advantages of providing significant information to distinguish various patterns in both 2D and 3D spaces. The segmentation accuracy has also been largely increased by fusing information from multimodal MRI images. Moreover, a unified framework is present which complementarily integrates hand-designed features with machine-learned features to produce more accurate segmentation. The hand-designed features from shallow network (with designable filters) encode the prior-knowledge and context while the machine-learned features from a deep network (with trainable filters) learn the intrinsic features. Both global and local information are combined using these two types of networks that improve the segmentation accuracy

Current Applications of Diffusion Tensor Imaging and Tractography in Intracranial Tumor Resection

In the treatment of brain tumors, surgical intervention remains a common and effective therapeutic option. Recent advances in neuroimaging have provided neurosurgeons with new tools to overcome the challenge of differentiating healthy tissue from tumor-infiltrated tissue, with the aim of increasing the likelihood of maximizing the extent of resection volume while minimizing injury to functionally important regions. Novel applications of diffusion tensor imaging (DTI), and DTI-derived tractography (DDT) have demonstrated that preoperative, non-invasive mapping of eloquent cortical regions and functionally relevant white matter tracts (WMT) is critical during surgical planning to reduce postoperative deficits, which can decrease quality of life and overall survival. In this review, we summarize the latest developments of applying DTI and tractography in the context of resective surgery and highlight its utility within each stage of the neurosurgical workflow: preoperative planning and intraoperative management to improve postoperative outcomes

An automated classification system to determine malignant grades of brain tumour (glioma) in magnetic resonance images based on meta-trainable multiple classifier schemes

The accurate classification of malignant grades of brain tumours is crucial for therapeutic planning as it impacts on the tumour’s prognosis, where the higher the malignancy levels of the brain tumour are, the higher the mortality rate is. It is also essential to provide patients with appropriate clinical management that may prolong survival and improve their quality of life. Determining the malignant grade of a brain tumour is a critical challenge because different malignant grades of brain tumours, in some cases, have inconsistent and mixed morphological characteristics. Consequently, the visual diagnosis using only the naked eye is a very complex and challenging task. The most common type of brain tumour is glioma. According to the World Health Organisation, low-grade glioma, which includes grade I and grade II are the least malignant, slow growing, and respond well to treatment. While, high-grade gliomas, which include grade III and grade IV are extremely malignant, have a poor prognosis and may lead to a high mortality rate. Hence, the motivation to develop an automated classification system to predict the malignant grade of glioma is the aim of this research. To achieve this aim, several novel methods were developed and this includes new methods for the extraction of statistical measures, selection of the dominant predictors, and the fusion of multi-classification models. The integration of these stages generates an accurate and automated decision system to determine the malignant grade of glioma. The feature extraction starts from the viewpoint that the objective measure of the brain tumour descriptors in MR images lead to an accurate classification of malignant brain tumours. This work starts from the standpoint that meta-trainable fusion of multiple classifier models can offer a better classification accuracy to recognise the malignant grade of glioma in MR images. This study developed a novel strategy based on two stages of multiple classifier systems for glioma grades. In the first stage, different machine learning algorithms were used. In the second stage, a systematic trainable combiner was designed based on deep neural networks. This research was validated using four benchmark datasets of MR images, which are publicly available and confirmed with the histopathological diagnosis. The proposed system was also evaluated and compared against different traditional algorithms; the experimental results showed that the proposed system has successfully achieved better and optimal discrimination in glioma grades on all dataset