2,097 research outputs found
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
Straight trajectory planning for keyhole neurosurgery in sheep with automatic brain structures segmentation
In a translational neuroscience/neurosurgery perspective, sheep are considered good candidates to study because of the similarity between their brain and the human one. Automatic planning systems for safe keyhole neurosurgery maximize the probe/catheter distance from vessels and risky structures. This work consists in the development of a trajectories planner for straight catheters placement intended to be used for investigating the drug diffusivity mechanisms in sheep brain. Automatic brain segmentation of gray matter, white matter and cerebrospinal fluid is achieved using an online available sheep atlas. Ventricles, midbrain and cerebellum segmentation have been also carried out. The veterinary surgeon is asked to select a target point within the white matter to be reached by the probe and to define an entry area on the brain cortex. To mitigate the risk of hemorrhage during the insertion process, which can prevent the success of the insertion procedure, the trajectory planner performs a curvature analysis of the brain cortex and wipes out from the poll of possible entry points the sulci, as part of brain cortex where superficial blood vessels are naturally located. A limited set of trajectories is then computed and presented to the surgeon, satisfying an optimality criteria based on a cost function which considers the distance from critical brain areas and the whole trajectory length. The planner proved to be effective in defining rectilinear trajectories accounting for the safety constraints determined by the brain morphology. It also demonstrated a short computational time and good capability in segmenting gyri and sulci surfaces
Semi-automatic GUI platform to characterize brain development in preterm children using ultrasound images
The third trimester of pregnancy is the most critical period for human brain development, during which significant changes occur in the morphology of the brain. The development of sulci and gyri allows for a considerable increase in the brain surface. In preterm newborns, these changes occur in an extrauterine environment that may cause a disruption of the normal brain maturation process. We hypothesize that a normalized atlas of brain maturation with cerebral ultrasound images from birth to term equivalent age will help clinicians assess these changes. This work proposes a semi-automatic Graphical User Interface (GUI) platform for segmenting the main cerebral sulci in the clinical setting from ultrasound images. This platform has been obtained from images of a cerebral ultrasound neonatal database images provided by two clinical researchers from the Hospital Sant Joan de Déu in Barcelona, Spain. The primary objective is to provide a user-friendly design platform for clinicians for running and visualizing an atlas of images validated by medical experts. This GUI offers different segmentation approaches and pre-processing tools and is user-friendly and designed for running, visualizing images, and segmenting the principal sulci. The presented results are discussed in detail in this paper, providing an exhaustive analysis of the proposed approach’s effectiveness.Peer ReviewedPostprint (published version
Radiotherapy planning for glioblastoma based on a tumor growth model: Improving target volume delineation
Glioblastoma are known to infiltrate the brain parenchyma instead of forming
a solid tumor mass with a defined boundary. Only the part of the tumor with
high tumor cell density can be localized through imaging directly. In contrast,
brain tissue infiltrated by tumor cells at low density appears normal on
current imaging modalities. In clinical practice, a uniform margin is applied
to account for microscopic spread of disease.
The current treatment planning procedure can potentially be improved by
accounting for the anisotropy of tumor growth: Anatomical barriers such as the
falx cerebri represent boundaries for migrating tumor cells. In addition, tumor
cells primarily spread in white matter and infiltrate gray matter at lower
rate. We investigate the use of a phenomenological tumor growth model for
treatment planning. The model is based on the Fisher-Kolmogorov equation, which
formalizes these growth characteristics and estimates the spatial distribution
of tumor cells in normal appearing regions of the brain. The target volume for
radiotherapy planning can be defined as an isoline of the simulated tumor cell
density.
A retrospective study involving 10 glioblastoma patients has been performed.
To illustrate the main findings of the study, a detailed case study is
presented for a glioblastoma located close to the falx. In this situation, the
falx represents a boundary for migrating tumor cells, whereas the corpus
callosum provides a route for the tumor to spread to the contralateral
hemisphere. We further discuss the sensitivity of the model with respect to the
input parameters. Correct segmentation of the brain appears to be the most
crucial model input.
We conclude that the tumor growth model provides a method to account for
anisotropic growth patterns of glioblastoma, and may therefore provide a tool
to make target delineation more objective and automated
- …