Automatic Analysis of Brain Tissue and Structural Connectivity in MRI

Studies of the brain using magnetic resonance imaging (MRI) can provide insights in physiology and pathology that can eventually aid clinical diagnosis and therapy monitoring. MRI data acquired in these studies can be difficult, as well as laborious, to interpret and analyze by human observers. Moreover, analysis by human observers can hamper the reproducibility by both inter- and intra-observer variability. These studies do, therefore, require accurate and reproducible quantitative image analysis techniques to optimally benefit from the valuable information contained in the MRI data. In this thesis, we focus on the development and evaluation of quantitative analysis techniques for brain MRI data. In the first part of this thesis, we focus on automatic brain tissue and white matter lesion (WML) segmentation. We propose an automatic WML segmentation method based on fluid-attenuated inversion recovery (FLAIR) scans that can be added as an extension to brain tissue segmentation methods. We optimize and evaluate a previously proposed automatic brain tissue segmentation method in combination with the WML segmentation extension. We compare the accuracy and reproducibility of this newly developed segmentation framework to several other methods, some of which are publicly available. Additionally, we compare two brain tissue segmentation methods on the segmentation of longitudinal brain MRI data. The second part of this thesis is about structural brain connectivity based on diffusion MRI data. We propose a framework for analysis of structural connectivity in large groups of subjects. Structural connectivity is established using minimum cost paths based on the diffusion weighted images and is summarized in brain networks. Using statistical methods, we demonstrate that the obtained networks contain information regarding subject age, white matter lesion load and white matter atrophy. Finally, we evaluate the reproducibility of the proposed brain connectivity framework

Adversarial training and dilated convolutions for brain MRI segmentation

Convolutional neural networks (CNNs) have been applied to various automatic image segmentation tasks in medical image analysis, including brain MRI segmentation. Generative adversarial networks have recently gained popularity because of their power in generating images that are difficult to distinguish from real images. In this study we use an adversarial training approach to improve CNN-based brain MRI segmentation. To this end, we include an additional loss function that motivates the network to generate segmentations that are difficult to distinguish from manual segmentations. During training, this loss function is optimised together with the conventional average per-voxel cross entropy loss. The results show improved segmentation performance using this adversarial training procedure for segmentation of two different sets of images and using two different network architectures, both visually and in terms of Dice coefficients.Comment: MICCAI 2017 Workshop on Deep Learning in Medical Image Analysi

Scalable multimodal convolutional networks for brain tumour segmentation

Brain tumour segmentation plays a key role in computer-assisted surgery. Deep neural networks have increased the accuracy of automatic segmentation significantly, however these models tend to generalise poorly to different imaging modalities than those for which they have been designed, thereby limiting their applications. For example, a network architecture initially designed for brain parcellation of monomodal T1 MRI can not be easily translated into an efficient tumour segmentation network that jointly utilises T1, T1c, Flair and T2 MRI. To tackle this, we propose a novel scalable multimodal deep learning architecture using new nested structures that explicitly leverage deep features within or across modalities. This aims at making the early layers of the architecture structured and sparse so that the final architecture becomes scalable to the number of modalities. We evaluate the scalable architecture for brain tumour segmentation and give evidence of its regularisation effect compared to the conventional concatenation approach.Comment: Paper accepted at MICCAI 201

Dilatation of Lateral Ventricles with Brain Volumes in Infants with 3D Transfontanelle US

Ultrasound (US) can be used to assess brain development in newborns, as MRI is challenging due to immobilization issues, and may require sedation. Dilatation of the lateral ventricles in the brain is a risk factor for poorer neurodevelopment outcomes in infants. Hence, 3D US has the ability to assess the volume of the lateral ventricles similar to clinically standard MRI, but manual segmentation is time consuming. The objective of this study is to develop an approach quantifying the ratio of lateral ventricular dilatation with respect to total brain volume using 3D US, which can assess the severity of macrocephaly. Automatic segmentation of the lateral ventricles is achieved with a multi-atlas deformable registration approach using locally linear correlation metrics for US-MRI fusion, followed by a refinement step using deformable mesh models. Total brain volume is estimated using a 3D ellipsoid modeling approach. Validation was performed on a cohort of 12 infants, ranging from 2 to 8.5 months old, where 3D US and MRI were used to compare brain volumes and segmented lateral ventricles. Automatically extracted volumes from 3D US show a high correlation and no statistically significant difference when compared to ground truth measurements. Differences in volume ratios was 6.0 +/- 4.8% compared to MRI, while lateral ventricular segmentation yielded a mean Dice coefficient of 70.8 +/- 3.6% and a mean absolute distance (MAD) of 0.88 +/- 0.2mm, demonstrating the clinical benefit of this tool in paediatric ultrasound

Automatic MRI 2D Brain Segmentation using Graph SearchingTechnique

Accurate and efficient segmentation of the whole brain in magnetic resonance (MR) images is a key task in many neuroscience and medical studies either because the whole brain is the final anatomical structure of interest or because the automatic extraction facilitates further analysis. The problem of segmenting brain MRI images has been extensively addressed by many researchers. Despite the relevant achievements obtained, automated segmentation of brain MRI imagery is still a challenging problem whose solution has to cope with critical aspects such as anatomical variability and pathological deformation. In the present paper, we describe and experimentally evaluate a method for segmenting brain from MRI images basing on two-dimensional graph searching principles for border detection. The segmentation of the whole brain over the entire volume is accomplished slice by slice, automatically detecting frames including eyes. The method is fully automatic and easily reproducible by computing the internal main parameters directly from the image data. The segmentation procedure is conceived as a tool of general applicability, although design requirements are especially commensurate with the accuracy required in clinical tasks such as surgical planning and post-surgical assessment. Several experiments were performed to assess the performance of the algorithm on a varied set of MRI images obtaining good results in terms of accuracy and stabilit

Fully Automatic MRI Brain Tumor Segmentation

Today in the area of medical research, the care of brain tumor patient attracts a lot of attention. Brain tumor segmentation consists of separating the different brain tumor tissues from normal tissues. In the past, many researchers in the field of medical imaging and soft computing have made significant survey in the field of brain tumor segmentation. Both semiautomatic and fully automatic methods have been proposed. Clinical acceptance of segmentation techniques has depended on the simplicity of the segmentation and the degree of user supervision. Additionally, with the development of particular software tools for automatic segmentation and brain tumor detection, which reduce the doctors’ time spent on manual segmentation, more effective and efficient results are provided. In this paper BraTumIA software tool has been used for automated segmentation on MRI brain tumor images in order to perform fully segmentation by separating different brain tumor tissues from the normal ones