18,468 research outputs found
Group-wise 3D registration based templates to study the evolution of ant worker neuroanatomy
The evolutionary success of ants and other social insects is considered to be
intrinsically linked to division of labor and emergent collective intelligence.
The role of the brains of individual ants in generating these processes,
however, is poorly understood. One genus of ant of special interest is
Pheidole, which includes more than a thousand species, most of which are
dimorphic, i.e. their colonies contain two subcastes of workers: minors and
majors. Using confocal imaging and manual annotations, it has been demonstrated
that minor and major workers of different ages of three species of Pheidole
have distinct patterns of brain size and subregion scaling. However, these
studies require laborious effort to quantify brain region volumes and are
subject to potential bias. To address these issues, we propose a group-wise 3D
registration approach to build for the first time bias-free brain atlases of
intra- and inter-subcaste individuals and automatize the segmentation of new
individuals.Comment: 10 pages, 5 figures, preprint for conference (not reviewed
Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction.
Recent contributions to the body of knowledge on traumatic brain injury (TBI) favor the view that multimodal neuroimaging using structural and functional magnetic resonance imaging (MRI and fMRI, respectively) as well as diffusion tensor imaging (DTI) has excellent potential to identify novel biomarkers and predictors of TBI outcome. This is particularly the case when such methods are appropriately combined with volumetric/morphometric analysis of brain structures and with the exploration of TBI-related changes in brain network properties at the level of the connectome. In this context, our present review summarizes recent developments on the roles of these two techniques in the search for novel structural neuroimaging biomarkers that have TBI outcome prognostication value. The themes being explored cover notable trends in this area of research, including (1) the role of advanced MRI processing methods in the analysis of structural pathology, (2) the use of brain connectomics and network analysis to identify outcome biomarkers, and (3) the application of multivariate statistics to predict outcome using neuroimaging metrics. The goal of the review is to draw the community's attention to these recent advances on TBI outcome prediction methods and to encourage the development of new methodologies whereby structural neuroimaging can be used to identify biomarkers of TBI outcome
Atlas construction and spatial normalisation to facilitate radiation-induced late effects research in childhood cancer
Reducing radiation-induced side effects is one of the most important challenges in paediatric cancer treatment. Recently, there has been growing interest in using spatial normalisation to enable voxel-based analysis of radiation-induced toxicities in a variety of patient groups. The need to consider three-dimensional distribution of doses, rather than dose-volume histograms, is desirable but not yet explored in paediatric populations. In this paper, we investigate the feasibility of atlas construction and spatial normalisation in paediatric radiotherapy. We used planning computed tomography (CT) scans from twenty paediatric patients historically treated with craniospinal irradiation to generate a template CT that is suitable for spatial normalisation. This childhood cancer population representative template was constructed using groupwise image registration. An independent set of 53 subjects from a variety of childhood malignancies was then used to assess the quality of the propagation of new subjects to this common reference space using deformable image registration (i.e., spatial normalisation). The method was evaluated in terms of overall image similarity metrics, contour similarity and preservation of dose-volume properties. After spatial normalisation, we report a dice similarity coefficient of 0.95±0.05, 0.85±0.04, 0.96±0.01, 0.91±0.03, 0.83±0.06 and 0.65±0.16 for brain and spinal canal, ocular globes, lungs, liver, kidneys and bladder. We then demonstrated the potential advantages of an atlas-based approach to study the risk of second malignant neoplasms after radiotherapy. Our findings indicate satisfactory mapping between a heterogeneous group of patients and the template CT. The poorest performance was for organs in the abdominal and pelvic region, likely due to respiratory and physiological motion and to the highly deformable nature of abdominal organs. More specialised algorithms should be explored in the future to improve mapping in these regions. This study is the first step toward voxel-based analysis in radiation-induced toxicities following paediatric radiotherapy
Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates
The study of cerebral anatomy in developing neonates is of great importance for
the understanding of brain development during the early period of life. This
dissertation therefore focuses on three challenges in the modelling of cerebral
anatomy in neonates during brain development. The methods that have been
developed all use Magnetic Resonance Images (MRI) as source data.
To facilitate study of vascular development in the neonatal period, a set of image
analysis algorithms are developed to automatically extract and model cerebral
vessel trees. The whole process consists of cerebral vessel tracking from
automatically placed seed points, vessel tree generation, and vasculature
registration and matching. These algorithms have been tested on clinical Time-of-
Flight (TOF) MR angiographic datasets.
To facilitate study of the neonatal cortex a complete cerebral cortex segmentation
and reconstruction pipeline has been developed. Segmentation of the neonatal
cortex is not effectively done by existing algorithms designed for the adult brain
because the contrast between grey and white matter is reversed. This causes pixels
containing tissue mixtures to be incorrectly labelled by conventional methods. The
neonatal cortical segmentation method that has been developed is based on a novel
expectation-maximization (EM) method with explicit correction for mislabelled
partial volume voxels. Based on the resulting cortical segmentation, an implicit
surface evolution technique is adopted for the reconstruction of the cortex in
neonates. The performance of the method is investigated by performing a detailed
landmark study.
To facilitate study of cortical development, a cortical surface registration algorithm
for aligning the cortical surface is developed. The method first inflates extracted
cortical surfaces and then performs a non-rigid surface registration using free-form
deformations (FFDs) to remove residual alignment. Validation experiments using
data labelled by an expert observer demonstrate that the method can capture local
changes and follow the growth of specific sulcus
Keypoint Transfer for Fast Whole-Body Segmentation
We introduce an approach for image segmentation based on sparse
correspondences between keypoints in testing and training images. Keypoints
represent automatically identified distinctive image locations, where each
keypoint correspondence suggests a transformation between images. We use these
correspondences to transfer label maps of entire organs from the training
images to the test image. The keypoint transfer algorithm includes three steps:
(i) keypoint matching, (ii) voting-based keypoint labeling, and (iii)
keypoint-based probabilistic transfer of organ segmentations. We report
segmentation results for abdominal organs in whole-body CT and MRI, as well as
in contrast-enhanced CT and MRI. Our method offers a speed-up of about three
orders of magnitude in comparison to common multi-atlas segmentation, while
achieving an accuracy that compares favorably. Moreover, keypoint transfer does
not require the registration to an atlas or a training phase. Finally, the
method allows for the segmentation of scans with highly variable field-of-view.Comment: Accepted for publication at IEEE Transactions on Medical Imagin
- …