Classifying malignant brain tumours from 1H-MRS data using Breadth Ensemble Learning

In neuro oncology, the accurate diagnostic identification and characterization of tumours is paramount for determining their prognosis and the adequate course of treatment. This is usually a difficult problem per se, due to the localization of the tumour in an extremely sensitive and difficult to reach organ such as the brain. The clinical analysis of brain tumours often requires the use of non-invasive measurement methods, the most common of which resort to imaging techniques. The discrimination between high-grade malignant tumours of different origin but similar characteristics, such as glioblastomas and metastases, is a particularly difficult problem in this context. This is because imaging techniques are often not sensitive enough and their spectroscopic signal is overall too similar. In spite of this, machine learning techniques, coupled with robust feature selection procedures, have recently made substantial inroads into the problem. In this study, magnetic resonance spectroscopy data from an international, multicentre database were used to discriminate between these two types of malignant brain tumours using ensemble learning techniques, with a focus on the definition of a feature selection method specifically designed for ensembles. This method, Breadth Ensemble Learning, takes advantage of the fact that many of the frequencies of the available spectra convey no relevant information for the discrimination of the tumours. The potential of the proposed method is supported by some of the best results reported to date for this problem.Postprint (author's final draft

Intraoperative Guidance for Pediatric Brain Surgery based on Optical Techniques

For most of the patients with brain tumors and/or epilepsy, surgical resection of brain lesions, when applicable, remains one of the optimal treatment options. The success of the surgery hinges on accurate demarcation of neoplastic and epileptogenic brain tissue. The primary goal of this PhD dissertation is to demonstrate the feasibility of using various optical techniques in conjunction with sophisticated signal processing algorithms to differentiate brain tumor and epileptogenic cortex from normal brain tissue intraoperatively. In this dissertation, a new tissue differentiation algorithm was developed to detect brain tumors in vivo using a probe-based diffuse reflectance spectroscopy system. The system as well as the algorithm were validated experimentally on 20 pediatric patients undergoing brain tumor surgery at Nicklaus Children’s Hospital. Based on the three indicative parameters, which reflect hemodynamic and structural characteristics, the new algorithm was able to differentiate brain tumors from the normal brain with a very high accuracy. The main drawbacks of the probe-based system were its high susceptibility to artifacts induced by hand motion and its interference to the surgical procedure. Therefore, a new optical measurement scheme and its companion spectral interpretation algorithm were devised. The new measurement scheme was evaluated both theoretically with Monte Carlo simulation and experimentally using optical phantoms, which confirms the system is capable of consistently acquiring total diffuse reflectance spectra and accurately converting them to the ratio of reduced scattering coefficient to absorption coefficient (µs’(λ)/µa(λ)). The spectral interpretation algorithm for µs’(λ)/µa(λ) was also validated based on Monte Carlo simulation. In addition, it has been demonstrated that the new measurement scheme and the spectral interpretation algorithm together are capable of detecting significant hemodynamic and scattering variations from the Wistar rats’ somatosensory cortex under forepaw stimulation. Finally, the feasibility of using dynamic intrinsic optical imaging to distinguish epileptogenic and normal cortex was validated in an in vivo study involving 11 pediatric patients with intractable epilepsy. Novel data analysis methods were devised and applied to the data from the study; identification of the epileptogenic cortex was achieved with a high accuracy

ELM ZA KLASIFIKACIJU TUMORA MOZGA KOD 3D MR SNIMAKA

Extreme Learning machine (ELM) a widely adopted algorithm in machine learning field is proposed for the use of pattern classification model using 3D MRI images for identifying tissue abnormalities in brain histology. The four class classification includes gray matter, white matter, cerebrospinal-fluid and tumor. The 3D MRI assessed by a pathologist indicates the ROI and the images are normalized. Texture features for each of the sub-regions is based on the Run-length Matrix, Co-occurence Matrix, Intensity, Euclidean distance, Gradient vector and neighbourhood statistics. Genetic Algorithm is custom designed to extract and sub-select a decisive optimal bank of features which are then used to model the ELM classifier and best selection of ELM algorithm parameters to handle sparse image data. The algorithm is explored using different activation function and the effect of number of neurons in the hidden layer by using different ratios of the number of features in the training and test data. The ELM classification outperformed in terms of accuracy, sensitivity and specificity as 93.20 %, 91.6 %, and 97.98% for discrimination of brain and pathological tumor tissue classification against state-of-the-art feature extraction methods and classifiers in the literature for publicly available SPL dataset.ELM, široko prihvaćen algoritam strojnog učenja se predlaže za korištenje u uzorkovanju pomoću klasifikacijskog modela 3D MRI slika za identifikaciju abnormalnosti tkiva u histologiji mozga. Četiri klase obuhvaćaju sive, bijele tvari, cerebrospinalne tekućine-i tumore. 3D MRI koji ocjenjuje patolog, ukazuje na ROI, a slike su normalizirane. Značajke tekstura za svaku od podregija se temelje na Run-length matrici, ponovnom pojavljivanju matrice, intenzitet, euklidska udaljenost, gradijent vektora i statistike susjedstva. Genetski algoritam je obično dizajniran za izdvajanje i sub-optimalan odabir odlučujući o značajkama koje se onda koriste za model ELM klasifikatora i najbolji izbor ELM parametra algoritama za obradu rijetkih slikovnih podataka. Algoritam se istražuje koristeći različite aktivacijske funkcije i utjecaj broja neurona u skrivenom sloju pomoću različitih omjera broja značajki kod trening i test podataka. ELM klasifikacija je nadmašila u smislu točnosti, osjetljivosti i specifičnosti, kao 93,20%, 91,6% i 97,98% za diskriminaciju mozga i patološki kod tumora i sistematizacije metode za prikupljanje podataka i klasifikatore u literaturi za javno dostupne SPL skup podataka

A Review on Data Fusion of Multidimensional Medical and Biomedical Data

Data fusion aims to provide a more accurate description of a sample than any one source of data alone. At the same time, data fusion minimizes the uncertainty of the results by combining data from multiple sources. Both aim to improve the characterization of samples and might improve clinical diagnosis and prognosis. In this paper, we present an overview of the advances achieved over the last decades in data fusion approaches in the context of the medical and biomedical fields. We collected approaches for interpreting multiple sources of data in different combinations: image to image, image to biomarker, spectra to image, spectra to spectra, spectra to biomarker, and others. We found that the most prevalent combination is the image-to-image fusion and that most data fusion approaches were applied together with deep learning or machine learning methods

Metabolite profiles of medulloblastoma for rapid and non-invasive detection of molecular disease groups

\ua9 2024 The AuthorsBackground: The malignant childhood brain tumour, medulloblastoma, is classified clinically into molecular groups which guide therapy. DNA-methylation profiling is the current classification ‘gold-standard’, typically delivered 3–4 weeks post-surgery. Pre-surgery non-invasive diagnostics thus offer significant potential to improve early diagnosis and clinical management. Here, we determine tumour metabolite profiles of the four medulloblastoma groups, assess their diagnostic utility using tumour tissue and potential for non-invasive diagnosis using in vivo magnetic resonance spectroscopy (MRS). Methods: Metabolite profiles were acquired by high-resolution magic-angle spinning NMR spectroscopy (MAS) from 86 medulloblastomas (from 59 male and 27 female patients), previously classified by DNA-methylation array (WNT (n = 9), SHH (n = 22), Group3 (n = 21), Group4 (n = 34)); RNA-seq data was available for sixty. Unsupervised class-discovery was performed and a support vector machine (SVM) constructed to assess diagnostic performance. The SVM classifier was adapted to use only metabolites (n = 10) routinely quantified from in vivo MRS data, and re-tested. Glutamate was assessed as a predictor of overall survival. Findings: Group-specific metabolite profiles were identified; tumours clustered with good concordance to their reference molecular group (93%). GABA was only detected in WNT, taurine was low in SHH and lipids were high in Group3. The tissue-based metabolite SVM classifier had a cross-validated accuracy of 89% (100% for WNT) and, adapted to use metabolites routinely quantified in vivo, gave a combined classification accuracy of 90% for SHH, Group3 and Group4. Glutamate predicted survival after incorporating known risk-factors (HR = 3.39, 95% CI 1.4–8.1, p = 0.025). Interpretation: Tissue metabolite profiles characterise medulloblastoma molecular groups. Their combination with machine learning can aid rapid diagnosis from tissue and potentially in vivo. Specific metabolites provide important information; GABA identifying WNT and glutamate conferring poor prognosis. Funding: Children with Cancer UK, Cancer Research UK, Children\u27s Cancer North and a Newcastle University PhD studentship

Biomedical Sensing and Imaging

This book mainly deals with recent advances in biomedical sensing and imaging. More recently, wearable/smart biosensors and devices, which facilitate diagnostics in a non-clinical setting, have become a hot topic. Combined with machine learning and artificial intelligence, they could revolutionize the biomedical diagnostic field. The aim of this book is to provide a research forum in biomedical sensing and imaging and extend the scientific frontier of this very important and significant biomedical endeavor

MR görüntüleri ve MR spektroskopi verileri ile yapay öğrenme tabanlı beyin tümörü tespit yöntemi ve uygulaması

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Beyinde büyüyen ve gelişen kötü huylu tümörler son zamanlarda insan ölümlerinin en önde gelen nedenlerinden birisi olmaya başlamıştır. Beyin tümörleri için en uygun tedavi yönteminin belirlenmesi hekim tarafından tümörün türünün ve evresinin belirlenmesine bağlıdır. Beyin tümörünün tecrübeli radyologlar tarafından tam olarak teşhis edilebilmesi, Manyetik Rezonans (MR görüntüleri), MR spektroskopi verileri ve patolojik değerlendirmeleri içerisine alan karmaşık bir süreçtir. Genel olarak bir radyolog bu süreçle ilgili olarak önemli doğruluk ve hassaslıkta karar verebiliyor olsa da, hataları en aza indirebilmek için sürekli yeni yöntemler araştırılmaktadır. Bu yüzden radyolog ya da hekimlerin beyin tümörlerinin ayrımını yüksek oranda yapabilecek Bilgisayar Destekli Teşhis (Computer-Aided Detection, CAD / BDT) sistemlerinden yararlanması oldukça önemlidir. Bu tez çalışmasında, hem MR görüntüleri ile hem de MR Spektroskopi (MRS) verileri kullanarak, radyologların karar verme aşamalarında yardımcı olabilecek, beyin tümörlerinin tespitini başarılı bir şekilde yapan yeni bilgisayar destekli yaklaşımlar önerilmiştir. Tez kapsamında geliştirilen ilk yöntem MR görüntüleri üzerinde çalışmakta ve beyin tümörlerinin iyi/kötü huylu ayrımlarını görüntü işleme ve örüntü tanıma teknikleri ile gerçekleştirmektedir. Bu işlemi gerçekleştirmek amacıyla MR görüntüleri üzerinde kafatası kısmını çıkarma için yeni bir görüntü ön-işleme tekniği önerilmiştir. Ayrıca, tümör ayrımlarında sınıflandırıcı etkisini görebilmek için farklı sınıflandırıcıların başarımları kıyaslanmıştır. 188 adet MR görüntüsü üzerinde yapılan detaylı deney sonuçlarına göre, önerilen yöntem ile %96.81 doğruluk oranı ile beyin tümörlerinin iyi / kötü huylu ayrımı gerçekleştirilebilmiştir. Tez kapsamında önerilen bir diğer yöntemde ise, MR spektroskopi sinyalleri üzerinde çalışan ve Yapay Bağışıklık Sistemi (YBS) tabanlı yeni bir BDT yaklaşımı geliştirilmiştir. Önerilen yöntem ile MRS verileri kullanılarak iyi huylu / kötü huylu tümör ayrımı, beyin tümörünün evrelemesi, normal beyin dokusu ile beyin tümörünün ayrımı, metastaz beyin tümörleri ile birincil beyin tümörlerinin ayrımı ve sahte tümörlerin belirlenmesi yüksek başarımla mümkün olmuştur. Çok uluslu ve merkezli bir proje kapsamında elde edilen geniş bir veri seti ile gerçekleştirilen deney sonuçlarına göre sırasıyla %96.97, %100, %100, %98.33 ve %98.44 başarım elde edilmiştir.Malignant tumors growing and developing in the brain have recently become one of the leading causes of death in humans. Determination of the most suitable treatment for brain tumors depends on accurate detection of malignancy, type and grade of the tumor by the physician. Diagnosis of brain tumors by radiologists is a complex process which includes MR images, MR spectroscopy data and pathological assessments. Generally, a radiologist makes a decision with reasonable accuracy and specifity rates. However new methods have been investigated by the researchers to minimize the diagnosis mistakes. Therefore, it is crucial for radiologists or physicians to use a Computer-Aided Diagnosis (CAD) system which will help detection of brain tumors with high success rates. In this thesis, novel computer aided methods, which use MR images and MR Spectroscopy data, have been proposed for the detection of brain tumors to support decision process of the radiologists. The first method developed in the thesis differentiates brain tumors as benign or malignant by image processing and pattern recognition techniques on MR images. To perform this operation, a new image pre-processing technique has been proposed to strip the skull region. Moreover, to evaluate the effect of classifier performance on tumor differentiation, different classifiers have been compared. According to detailed test results performed on 188 MR images, benign or malignant differentiation of brain tumors can be detected with 96.81% accuracy rate by proposed method. In the second method, a novel Artificial Immune System (AIS) based computer-aided diagnosis system has been proposed. This system utilizes MR Spectroscopy signals to make a decision about brain tumors. The system can perform differentiation of benign / malign, metastatic / primary, pseudo / normal tumors and grading of brain tumors with high accuracy rates. According to the experimental results performed on large dataset obtained from an international and multi-center project, the detection performance has been achieved 96.97%, 100%, 100%, 98.33% and 98.44% success rates respectively

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

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