Multimodal image analysis of the human brain

Gedurende de laatste decennia heeft de snelle ontwikkeling van multi-modale en niet-invasieve hersenbeeldvorming technologieën een revolutie teweeg gebracht in de mogelijkheid om de structuur en functionaliteit van de hersens te bestuderen. Er is grote vooruitgang geboekt in het beoordelen van hersenschade door gebruik te maken van Magnetic Reconance Imaging (MRI), terwijl Elektroencefalografie (EEG) beschouwd wordt als de gouden standaard voor diagnose van neurologische afwijkingen. In deze thesis focussen we op de ontwikkeling van nieuwe technieken voor multi-modale beeldanalyse van het menselijke brein, waaronder MRI segmentatie en EEG bronlokalisatie. Hierdoor voegen we theorie en praktijk samen waarbij we focussen op twee medische applicaties: (1) automatische 3D MRI segmentatie van de volwassen hersens en (2) multi-modale EEG-MRI data analyse van de hersens van een pasgeborene met perinatale hersenschade. We besteden veel aandacht aan de verbetering en ontwikkeling van nieuwe methoden voor accurate en ruisrobuuste beeldsegmentatie, dewelke daarna succesvol gebruikt worden voor de segmentatie van hersens in MRI van zowel volwassen als pasgeborenen. Daarenboven ontwikkelden we een geïntegreerd multi-modaal methode voor de EEG bronlokalisatie in de hersenen van een pasgeborene. Deze lokalisatie wordt gebruikt voor de vergelijkende studie tussen een EEG aanval bij pasgeborenen en acute perinatale hersenletsels zichtbaar in MRI

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

Fast and robust hybrid framework for infant brain classification from structural MRI : a case study for early diagnosis of autism.

The ultimate goal of this work is to develop a computer-aided diagnosis (CAD) system for early autism diagnosis from infant structural magnetic resonance imaging (MRI). The vital step to achieve this goal is to get accurate segmentation of the different brain structures: whitematter, graymatter, and cerebrospinal fluid, which will be the main focus of this thesis. The proposed brain classification approach consists of two major steps. First, the brain is extracted based on the integration of a stochastic model that serves to learn the visual appearance of the brain texture, and a geometric model that preserves the brain geometry during the extraction process. Secondly, the brain tissues are segmented based on shape priors, built using a subset of co-aligned training images, that is adapted during the segmentation process using first- and second-order visual appearance features of infant MRIs. The accuracy of the presented segmentation approach has been tested on 300 infant subjects and evaluated blindly on 15 adult subjects. The experimental results have been evaluated by the MICCAI MR Brain Image Segmentation (MRBrainS13) challenge organizers using three metrics: Dice coefficient, 95-percentile Hausdorff distance, and absolute volume difference. The proposed method has been ranked the first in terms of performance and speed

Segmentation of the Cerebrospinal Fluid from MRI Images for the Treatment of Disc Herniations

About 80 percent of people are affected at some point in their lives by lower back pain, which is one of the most common neurological diseases and reasons for long-term disability in the United States. The symptoms are primarily caused by overly heavy lifting and/or overstretching of the back, leading to a rupture and an outward bulge of an intervertebral disc, which puts pressure on and pinches the nerve fibers of the spine. The most common form is a lumbar disc herniation between the fourth and fifth lumbar vertebra and between the fifth lumbar vertebra and the sacrum. In recent years the diagnosis of lower back pain has improved, mainly due to enhanced imaging techniques and imaging quality, but the surgical therapy remains hazardous. Reasons for this include low visibility when accessing the lumbar area and the high risk of causing permanent damage when touching the nerve fibers. A new approach for increasing patient safety is the segmentation and visualization of the cerebrospinal fluid in the lower lumbar region of the vertebral column. For this purpose a new fully-automatic and a semi-automatic approach were developed for separating the cerebrospinal fluid from its surroundings on T2-weighted MRI scans of the lumbar vertebra. While the fully-automatic algorithm is realized by a model-based searching method and a volume-based segmentation, the semi-automatic algorithm requires a seed point and performs the segmentation on individual axial planes through a combination of a region-based segmentation algorithm and a thresholding filter. Both algorithms have been applied to four T2-weighted MRI datasets and are compared with a gold-standard segmentation. The segmentation overlap with the gold-standard was 78.7 percent for the fully-automatic algorithm and 93.1 percent for the semi-automatic algorithm. In the pathological region the fully-automatic algorithm obtained a similarity of 56.6 percent, compared to 87.8 percent for the semi-automatic algorithm

The LONI QC System: A Semi-Automated, Web-Based and Freely-Available Environment for the Comprehensive Quality Control of Neuroimaging Data.

Quantifying, controlling, and monitoring image quality is an essential prerequisite for ensuring the validity and reproducibility of many types of neuroimaging data analyses. Implementation of quality control (QC) procedures is the key to ensuring that neuroimaging data are of high-quality and their validity in the subsequent analyses. We introduce the QC system of the Laboratory of Neuro Imaging (LONI): a web-based system featuring a workflow for the assessment of various modality and contrast brain imaging data. The design allows users to anonymously upload imaging data to the LONI-QC system. It then computes an exhaustive set of QC metrics which aids users to perform a standardized QC by generating a range of scalar and vector statistics. These procedures are performed in parallel using a large compute cluster. Finally, the system offers an automated QC procedure for structural MRI, which can flag each QC metric as being 'good' or 'bad.' Validation using various sets of data acquired from a single scanner and from multiple sites demonstrated the reproducibility of our QC metrics, and the sensitivity and specificity of the proposed Auto QC to 'bad' quality images in comparison to visual inspection. To the best of our knowledge, LONI-QC is the first online QC system that uniquely supports the variety of functionality where we compute numerous QC metrics and perform visual/automated image QC of multi-contrast and multi-modal brain imaging data. The LONI-QC system has been used to assess the quality of large neuroimaging datasets acquired as part of various multi-site studies such as the Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Study and the Alzheimer's Disease Neuroimaging Initiative (ADNI). LONI-QC's functionality is freely available to users worldwide and its adoption by imaging researchers is likely to contribute substantially to upholding high standards of brain image data quality and to implementing these standards across the neuroimaging community

Brain semantic segmentation: a deep learning approach in human and Rat MRI studies

Dissertação de mestrado em Biomedical Engineering Dissertation (área de especialização em Field of Medical informatics)Magnetic Resonance Imaging (MRI) provides information about anatomy and pathology. This type of technique is the most popular used for the study of rat and human brain. Classifying voxels according to the presence of relevant anatomic features is an important step in the pre-processing of the data. A precise delineation and automatic segmentation of the brain structures is required in preclinical rodent imaging field and can substitute the manual segmentation where time consuming or human-error problems can occur. Current solutions are based on traditional segmentation algorithms that raise accuracy issues and generally need human intervention during or after the segmentation process. In the humans’ field, most of the tools created in DL (deep learning) are used in tumour or lesion segmentation. Brain segmentation tissues are not as explored as oncology problems and lesions complications. In the rats’ field, there are no segmentation studies in DL. It was decided to use a DL approach in Rats to solve some of the old techniques’ problems. This dissertation will present an approach on semantic segmentation of white matter and gray matter in Human’s images, evaluate the algorithm’s performance with outliers. It will also present an FCN (fully convolutional network) solution for on semantic segmentation using rat’s and human’s MRI of anatomical features. A two-dimensional convolution (slice-by-slice) approach and a three-dimensional (volume) convolutions approach were evaluated. At the end, the results found, using FCN U-NET in rats’ MRI for a 2D convolutions approach, DSC were 94.65 % for WM, 91.03% for GM and 76.89 % for cerebrospinal fluid. Using the 3D convolutions approach, the results using DSC found are 93.81 % for WM, 89.69 % for GM and 74.68 % for cerebrospinal fluid. The results using humans’ MRI using DSC were 91.59% for WM and 84,58% for GM.Imagens de ressonância magnética providenciam informação acerca da anatomia humana e possíveis patologias existentes. Este é o tipo de técnica mais popular entre os estudos na área da neurociência, tanto em humanos como em roedores. A classificação de voxéis de acordo com a presença de informação anatómica relevante é um importante passo no pré-processamento de dados na comunidade científica na área da neurociência. Uma delineação precisa das várias estruturas do cérebro humano ou roedor é uma das requisições para a maioria dos estudos clínicos de imagens de ressonância magnética. A segmentação automática através de inteligência artificial pode vir a substituir ferramentas ou algoritmos semiautomáticos já existentes ou substituir também a segmentação manual que se trata de um processo muito demorado que está ligado a erro-humano. O avanço tecnológico provocou um estudo mais aprofundado no Deep Learning (DL) a partir de 2012, provando que estas técnicas de inteligência artificial estão a revelar-se melhores do que o que já existe na área médica. Dos estudos com ressonância magnética em humanos, a maioria das ferramentas criadas que utilizam DL são usadas na segmentação de tumores ou lesões cerebrais. A segmentação de tecidos cerebrais não está tão explorada como problemas oncológicos ou lesões cerebrais. Dos estudos com ressonância magnética em roedores, não existem ferramentas que utilizam as técnicas de DL. Tendo em conta que as técnicas de segmentação que já existem ainda têm muitas complicações e erros, foi decidido tentar uma abordagem de DL em Roedores, também. Esta dissertação irá apresentar uma abordagem de segmentação semântica de massa branca e massa cinzenta utilizando técnicas de DL em humanos. Irá também verificar a capacidade de generalização com casos de pacientes idosos. Irá ser apresentado uma técnica de DL nas imagens de ressonância magnética em roedores para a segmentação semântica de massa branca, massa cinzenta e líquido cérebroespinal. No final irá ser comparado as técnicas entre as duas espécies e também entre a utilização de convoluções com duas dimensões e de convoluções com três dimensões nos roedores. No final, os resultados encontrados utilizando uma FCN em Ratazanas numa abordagem 2D, os valores de DSC foram 94,65 % para massa branca, 91.03% para massa cinzenta e 76.89 % para o líquido cérebroespinal. Na abordagem 3D ,os valores de DSC encontrados foram 93.81 % % para massa branca, 89.69 % para massa cinzenta e 74.68 % para o líquido cérebroespinal. Os resultados utilizando as imagens humanas, foram 91.59% para massa branca e 84,58% para massa cinzenta.This work is part of the SIGMA project with the reference FCT-ANR/NEU-OSD/0258/2012, co-financed by the French public funding agency ANR (Agence Nationale pour la Recherche, APP Blanc International II 2012), the Portuguese FCT (Fundação para a Ciência e Tecnologia) and the Portuguese North Regional Operational Program (ON.2 – O Novo Norte) under the National Strategic Reference Framework (QREN), through the European Regional Development Fund (FEDER) as well as the Projecto Estratégico co-funded by FCT (PEst-C/SAU/LA0026-/2013) and the European Regional Development Fund COMPETE (FCOMP-01-0124-FEDER-037298).This work was also supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT within the Project Scope: UID/CEC/00319/2013

Automatic quantification of brain midline shift in CT images

Ph.DDOCTOR OF PHILOSOPH