Imaging Informatics and the Human Brain Project: the Role of Structure, Review

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Visual analytics methods for shape analysis of biomedical images exemplified on rodent skull morphology

In morphometrics and its application fields like medicine and biology experts are interested in causal relations of variation in organismic shape to phylogenetic, ecological, geographical, epidemiological or disease factors - or put more succinctly by Fred L. Bookstein, morphometrics is "the study of covariances of biological form". In order to reveal causes for shape variability, targeted statistical analysis correlating shape features against external and internal factors is necessary but due to the complexity of the problem often not feasible in an automated way. Therefore, a visual analytics approach is proposed in this thesis that couples interactive visualizations with automated statistical analyses in order to stimulate generation and qualitative assessment of hypotheses on relevant shape features and their potentially affecting factors. To this end long established morphometric techniques are combined with recent shape modeling approaches from geometry processing and medical imaging, leading to novel visual analytics methods for shape analysis. When used in concert these methods facilitate targeted analysis of characteristic shape differences between groups, co-variation between different structures on the same anatomy and correlation of shape to extrinsic attributes. Here a special focus is put on accurate modeling and interactive rendering of image deformations at high spatial resolution, because that allows for faithful representation and communication of diminutive shape features, large shape differences and volumetric structures. The utility of the presented methods is demonstrated in case studies conducted together with a collaborating morphometrics expert. As exemplary model structure serves the rodent skull and its mandible that are assessed via computed tomography scans

A framework for creating population specific multimodal brain atlas using clinical T1 and diffusion tensor images

International audienceSpatial normalization is one of the most important steps in population based statistical analysis of brain images. This involves normalizing all the brain images to a pre-defined template or a population specific template. With multiple emerging imaging modalities, it is quintessential to develop a method for building a joint template that is a statistical representation of the given population across different modalities. It is possible to create different population specific templates in different modalities using existing methods. However, they do not give an opportunity for voxelwise comparison of different modalities. A multimodal brain template with probabilistic region of interest (ROI) definitions will give opportunity for multivariate statistical frameworks for better understanding of brain diseases. In this paper, we propose a methodology for developing such a multimodal brain atlas using the anatomical T1 images and the diffusion tensor images (DTI), along with an automated workflow to probabilistically define the different white matter regions on the population specific multimodal template. The method will be useful to carry out ROI based statistics across different modalities even in the absence of expert segmentation. We show the effectiveness of such a template using voxelwise multivariate statistical analysis on population based group studies on HIV/AIDS patients. 1 The need for a probabilistic multimodal atlas The growth in brain imaging data across different modalities gives an opportunity to understand the disease progression and make correlations across them. Statistical analysis across different modalities and across population require spatial normalization. All the brain images are often normalized to a pre-defined template, for example the ICBM-152 or MNI template. However in [1] and [2], the authors have shown that choosing a generic template biases the statistical presently at Imaging Genetics Center, University of Southern California presently at MORPHENE team, INRIA Sophia-Antipoli

Segmentation algorithms for ear image data towards biomechanical studies

In recent years, the segmentation, i.e. the identification, of ear structures in video-otoscopy, computerised tomography (CT) and magnetic resonance (MR) image data, has gained significant importance in the medical imaging area, particularly those in CT and MR imaging. Segmentation is the fundamental step of any automated technique for supporting the medical diagnosis and, in particular, in biomechanics studies, for building realistic geometric models of ear structures. In this paper, a review of the algorithms used in ear segmentation is presented. The review includes an introduction to the usually biomechanical modelling approaches and also to the common imaging modalities. Afterwards, several segmentation algorithms for ear image data are described, and their specificities and difficulties as well as their advantages and disadvantages are identified and analysed using experimental examples. Finally, the conclusions are presented as well as a discussion about possible trends for future research concerning the ear segmentation.info:eu-repo/semantics/publishedVersio

A Scheme for Automatically Building 3D Morphometric Anatomical Atlases: application to a Skull Atlas

International audienceWe present a general scheme for automatically building a morphometric anatomical atlas. We detail each stage of the method, including the non-rigid registration algorithm, three-dimensional line averaging and statistical processes. We apply the method to obtain a quantitative atlas of skull crest lines. Finally, we use the resulting atlas to study a craniofacial disease; we show how we can obtain qualitative and quantitative results by contrasting a skull affected by a mandible deformation with the atlas

Automatic segmentation of Nucleus Accumbens

Segmentation of subcortical structures in the brain has become an increasingly important topic in contemporary medicine. The ability to effi ciently isolate different regions of the human brain has allowed doctors and technicians to become more e fficient in the diagnosis of mental disorders and the evaluation of the patient conditions. An area of the brain whose possible segmentation has received particular attention is the Nucleus Accumbens, which is believed to play a central role in the reward circuit. In fact, studies of volumetric brain magnetic resonance imaging (MRI) have shown neuroanatomical abnormalities of this structure in adult attention defficit/hyperactivity disorder (ADHD), and speci cally a smaller average volume of the region. The use of a reliable automated segmentation method would therefore represent an extremely helpful and e fficient tool for identifying this disorder, especially when compared to manual volume labeling methods, which often turn out to be tedious and extremely time-consuming. However, automatic segmentation of the Accumbens is extremely di fficult to obtain, due to the lack of contrast with the surrounding structures. This means that most conventional segmentation methods are useless for this purpose, and makes the segmentation method selection a very delicate procedure. Consequently, the main objective of the thesis is the implementation of a robust algorithm for segmenting the Nucleus Accumbens structure. The research project aims to apply pre-existing segmentation methods to the Nucleus Accumbens, moving then to an evaluation of such methods and an estimation of how e ffective they are. Diff erent segmentation methods were used for this purpose; firstly, the standard Atlas Segmentation Approach was used, showing generally poor results paired with long computational times and high complexity. Moreover, this method has shown potential problems in the individuation of the correct region, leading, in some cases, to completely wrong segmentations. In addition to the fi rst method, Multi Atlas Segmentation and Adaptive Multi Atlas Segmentation methods have been implemented. The results have shown improved accuracy and better performance than the original method. Judging by the results, the segmentation of the Nucleus Accumbens has proven to be an extremely complicated task, both for the dimension of the structure itself and for the lack of contrast with the surrounding structures. In order to improve detection accuracy, combination of multiple methods is necessary, as using a single method for the segmentation process can lead to an incorrect labeling

Spatial Normalization of Diffusion Tensor MRI Using Multiple Channels

Diffusion Tensor MRI (DT-MRI) can provide important in vivo information for the detection of brain abnormalities in diseases characterized by compromised neural connectivity. To quantify diffusion tensor abnormalities based on voxel-based statistical analysis, spatial normalization is required to minimize the anatomical variability between studied brain structures. In this article, we used a multiple input channel registration algorithm based on a demons algorithm and evaluated the spatial normalization of diffusion tensor image in terms of the input information used for registration. Registration was performed on 16 DT-MRI data sets using different combinations of the channels, including a channel of T2-weighted intensity, a channel of the fractional anisotropy, a channel of the difference of the first and second eigenvalues, two channels of the fractional anisotropy and the trace of tensor, three channels of the eigenvalues of the tensor, and the six channel tensor components. To evaluate the registration of tensor data, we defined two similarity measures, i.e., the endpoint divergence and the mean square error, which we applied to the fiber bundles of target images and registered images at the same seed points in white matter segmentation. We also evaluated the tensor registration by examining the voxel-by-voxel alignment of tensors in a sample of 15 normalized DT-MRIs. In all evaluations, nonlinear warping using six independent tensor components as input channels showed the best performance in effectively normalizing the tract morphology and tensor orientation. We also present a nonlinear method for creating a group diffusion tensor atlas using the average tensor field and the average deformation field, which we believe is a better approach than a strict linear one for representing both tensor distribution and morphological distribution of the population.ope