Identifying lesions in paediatric epilepsy using morphometric and textural analysis of magnetic resonance images

We develop an image processing pipeline on Magnetic Resonance Imaging (MRI) sequences to identify features of Focal Cortical Dysplasia (FCD) in patients with MRIvisible FCD. We aim to use a computer-aided diagnosis system to identify epileptogenic lesions with a combination of established morphometric features and textural analysis using Gray-Level Co-occurrence Matrices (GLCM) on MRI sequences. The model will be validated on paediatric subjects. Preliminary morphometric analysis explored the use of computational models of established MRI features of FCD in aiding identification of subtle FCD on MRI-positive subjects. Following this, classification techniques were considered. The 2-Step Naive Bayes classifier was found to produce 100% subjectwise specificity and 94% subjectwise sensitivity (with 75% lesional specificity, 63% lesional sensitivity). Thus it correctly rejected 13/13 healthy subjects and colocalized lesions in 29/31 of the FCD cases with MRI visible lesions, with 63% coverage of the complete extent of the lesion using supplied lesional labels

Seizure Detection Using Deep Learning, Information Theoretic Measures and Factor Graphs

Epilepsy is a common neurological disorder that disrupts normal electrical activity in the brain causing severe impact on patients’ daily lives. Accurate seizure detection based on long-term time-series electroencephalogram (EEG) signals has gained vital importance for epileptic seizure diagnosis. However, visual analysis of these recordings is a time-consuming task for neurologists. Therefore, the purpose of this thesis is to propose an automatic hybrid model-based /data-driven algorithm that exploits inter-channel and temporal correlations. Hence, we use mutual information (MI) estimator to compute correlation between EEG channels as spatial features and employ a carefully designed 1D convolutional neural network (CNN) to extract additional information from raw EEGs. Then, seizure probabilities from combined features of MI estimator and CNN are applied to factor graphs to learn factor nodes. The performance of the algorithm is evaluated through measuring different parameters as well as comparing with previous studies. On CHB-MIT dataset, our generalized algorithm achieves state-of-the-art performance

Clinical Applicability of MRI Texture Analysis

Radiologisten kuvien tulkinta on perinteisesti perustunut asiantuntijan näköhavaintoihin. Tietokoneavusteisten menetelmien käyttö lisääntyy radiologisessa diagnostiikassa. Tekstuuria eli kuviorakennetta on käytetty erottelevana ominaisuutena kudoksia luokiteltaessa ja karakterisoitaessa. Kuvan kuviorakenteen ominaisuuksia kuvaavia tekstuuriparametreja voidaan laskea erilaisilla matemaattisilla ja signaalinkäsittelymenetelmillä. Tekstuurianalyysi on antanut lupaavia tuloksia magneettikuvien tarkastelussa. Sen avulla on voitu määrittää sekä pieniä hajanaisia että suurempia paikallisia muutoksia. Menetelmällä on mahdollista havaita ihmissilmälle näkymättömiä sekä näkyviä muutoksia. Menetelmää tulisi tutkia edelleen, koska kliinisen menetelmän kehittämistä varten tarvitaan lisätietoa sen soveltuvuudesta erilaisille aineistoille sekä analyysimenetelmän eri vaiheiden optimoimisesta. Tämän väitöstutkimuksen tavoite oli selvittää magneettikuvauksen tekstuurianalyysin kliinistä käytettävyyttä eri kannoilta. Tutkimusaineisto koostui kolmesta potilasmateriaalista ja yhdestä terveiden urheilijoiden joukosta sekä heidän verrokeistaan. Aineisto kerättiin osina Tampereen yliopistollisessa sairaalassa toteutettuja laajempia tutkimusprojekteja, ja mukaan otettiin yhteensä 220 osallistujaa. Ensimmäisessä osatyössä tarkasteltiin pehmytkudoskuvantamista, non-Hodgkin-lymfooman hoitovasteen arviointia tekstuurianalyysilla. Kaksi seuraavaa osatyötä käsitteli keskushermoston kuvantamista: lieviä aivovammoja sekä MS-tautia. Viimeisessä osatyössä arvioitiin liikunnan vaikutusta urheilijoiden ja verrokkien reisiluun kaulan luurakenteeseen. Kudosten ja muutosten vertailuissa oli edustettuna sekä ympäröivästä kudoksesta visuaalisella tarkastelulla erottumattomia että selkeästi erottuvia rakenteita. Lisäksi tutkimuksessa selvitettiin mielenkiintoalueen käsityönä tehtävän rajaamisen ja magneettikuvaussekvenssin valinnan vaikutusta analyysiin. Yhteenvetona todetaan, että tekstuurimenetelmällä on mahdollista havaita ja karakterisoida tutkimukseen valikoidun aineiston edustamia etiologialtaan erilaisia muutoksia kliinisistä 1.5 Teslan magneettikuvista. Tutkimuksessa käsitellyt yksityiskohdat MRI-kuvasarjojen valinnasta sekä mielenkiintoalueiden piirtämisestä antavat pohjaa kliinisen protokollan kehittämiseen. Osa tutkimusaineistoista oli kokeellisia, ja niiden tulokset tulisi vahvistaa laajemmilla kliinisillä tutkimuksilla.The usage of computerised methods in radiological image interpretation is becoming more common. Texture analysis has shown promising results as an image analysis method for detecting non-visible and visible lesions, with a number of applications in magnetic resonance imaging (MRI). Although several recent studies have investigated this topic, there remains a need for further analyses incorporating different clinical materials and taking protocol planning for clinical analyses into account. The purpose of this thesis was to determine the clinical applicability of MRI texture analysis from different viewpoints. This study is based on three patient materials and one collection of healthy athletes and their referents. A total of 220 participants in wider on-going study projects at Tampere University Hospital were included in this thesis. The materials include a study on non-Hodgkin lymphoma, representing soft tissue imaging with malignant disease treatment monitoring; and two studies on central nervous system diseases, mild traumatic brain injury and multiple sclerosis. A musculoskeletal imaging study investigated load-associated physiological changes in healthy participants? bones. Furthermore, manual Region of Interest (ROI) definition methods and the selection of MRI sequences for analyses of visible and non-visible lesions were evaluated. In summary, this study showed that non-visible lesions and physiological changes as well as visible focal lesions of different aetiologies could be detected and characterised by texture analysis of routine clinical 1.5 T scans. The details of MRI sequence selection and ROI definition in this study may serve as guidelines for the development of clinical protocols. However, these studies are partly experimental and need to be validated with larger sample sizes

Statistical Neuroimage Modeling, Processing and Synthesis based on Texture and Component Analysis: Tackling the Small Sample Size Problem

The rise of neuroimaging in the last years has provided physicians and radiologist with the ability to study the brain with unprecedented ease. This led to a new biological perspective in the study of neurodegenerative diseases, allowing the characterization of different anatomical and functional patterns associated with them. CAD systems use statistical techniques for preparing, processing and extracting information from neuroimaging data pursuing a major goal: optimize the process of analysis and diagnosis of neurodegenerative diseases and mental conditions. With this thesis we focus on three different stages of the CAD pipeline: preprocessing, feature extraction and validation. For preprocessing, we have developed a method that target a relatively recent concern: the confounding effect of false positives due to differences in the acquisition at multiple sites. Our method can effectively merge datasets while reducing the acquisition site effects. Regarding feature extraction, we have studied decomposition algorithms (independent component analysis, factor analysis), texture features and a complete framework called Spherical Brain Mapping, that reduces the 3-dimensional brain images to two-dimensional statistical maps. This allowed us to improve the performance of automatic systems for detecting Alzheimer's and Parkinson's diseases. Finally, we developed a brain simulation technique that can be used to validate new functional datasets as well as for educational purposes

Small bowel motility quantitation using MRI and its relationship to gastrointestinal symptoms

The small bowel is difficult to analyse due to its deep anatomical location and the large variation seen in individuals, in regard to both anatomy and function including motility. Dynamic MRI allows small bowel motility to be captured and visually assessed by radiologists, but there is often large inter-observer variation and a lack of complicated motility patterns being investigated. This thesis aims to explore the link between abnormal motility and gastrointestinal (GI) symptoms in Crohn’s disease (CD) and irritable bowel syndrome (IBS) using MRI. Firstly, a scan duration of 15 seconds and a temporal resolution of 1 image per second were shown to be sufficient for robust small bowel MRI motility measurements. Next, a validation study confirmed an association between aberrant motility and CD patient symptoms, particularly diarrhoeal stools (rho = -0.29). The strongest association was in patients with higher symptom severity (rho = -0.633). Building on this work, more complex motility metrics were developed and compared to subjective radiological scoring. Spatial and temporal variation were found to be associated with CD patient symptoms and were also particularly difficult to visually assess. The motility metrics were applied in clinical IBS data to explore differences in IBS subgroups. Significantly reduced temporal variation of motility (P < 0.001) and area of motile bowel (P < 0.001) was found in IBS-C (constipation-predominant) compared to IBS-M (mixed constipation and diarrhoea). Finally, texture analysis (TA) terminal ileum (TI) to colon ratios were found to be higher for TA contrast (P = 0.005) and lower for TA energy (P = 0.03) in IBS-C compared to healthy controls (HCs). Ascending colon diameter was shown to be significantly larger in IBS-C than HCs (P = 0.005)

Deep Learning and parallelization of Meta-heuristic Methods for IoT Cloud

Healthcare 4.0 is one of the Fourth Industrial Revolution’s outcomes that make a big revolution in the medical field. Healthcare 4.0 came with more facilities advantages that improved the average life expectancy and reduced population mortality. This paradigm depends on intelligent medical devices (wearable devices, sensors), which are supposed to generate a massive amount of data that need to be analyzed and treated with appropriate data-driven algorithms powered by Artificial Intelligence such as machine learning and deep learning (DL). However, one of the most significant limits of DL techniques is the long time required for the training process. Meanwhile, the realtime application of DL techniques, especially in sensitive domains such as healthcare, is still an open question that needs to be treated. On the other hand, meta-heuristic achieved good results in optimizing machine learning models. The Internet of Things (IoT) integrates billions of smart devices that can communicate with one another with minimal human intervention. IoT technologies are crucial in enhancing several real-life smart applications that can improve life quality. Cloud Computing has emerged as a key enabler for IoT applications because it provides scalable and on-demand, anytime, anywhere access to the computing resources. In this thesis, we are interested in improving the efficacity and performance of Computer-aided diagnosis systems in the medical field by decreasing the complexity of the model and increasing the quality of data. To accomplish this, three contributions have been proposed. First, we proposed a computer aid diagnosis system for neonatal seizures detection using metaheuristics and convolutional neural network (CNN) model to enhance the system’s performance by optimizing the CNN model. Secondly, we focused our interest on the covid-19 pandemic and proposed a computer-aided diagnosis system for its detection. In this contribution, we investigate Marine Predator Algorithm to optimize the configuration of the CNN model that will improve the system’s performance. In the third contribution, we aimed to improve the performance of the computer aid diagnosis system for covid-19. This contribution aims to discover the power of optimizing the data using different AI methods such as Principal Component Analysis (PCA), Discrete wavelet transform (DWT), and Teager Kaiser Energy Operator (TKEO). The proposed methods and the obtained results were validated with comparative studies using benchmark and public medical data