845 research outputs found
Automated Detection of Regions of Interest for Brain Perfusion MR Images
Images with abnormal brain anatomy produce problems for automatic
segmentation techniques, and as a result poor ROI detection affects both
quantitative measurements and visual assessment of perfusion data. This paper
presents a new approach for fully automated and relatively accurate ROI
detection from dynamic susceptibility contrast perfusion magnetic resonance and
can therefore be applied excellently in the perfusion analysis. In the proposed
approach the segmentation output is a binary mask of perfusion ROI that has
zero values for air pixels, pixels that represent non-brain tissues, and
cerebrospinal fluid pixels. The process of binary mask producing starts with
extracting low intensity pixels by thresholding. Optimal low-threshold value is
solved by obtaining intensity pixels information from the approximate
anatomical brain location. Holes filling algorithm and binary region growing
algorithm are used to remove falsely detected regions and produce region of
only brain tissues. Further, CSF pixels extraction is provided by thresholding
of high intensity pixels from region of only brain tissues. Each time-point
image of the perfusion sequence is used for adjustment of CSF pixels location.
The segmentation results were compared with the manual segmentation performed
by experienced radiologists, considered as the reference standard for
evaluation of proposed approach. On average of 120 images the segmentation
results have a good agreement with the reference standard. All detected
perfusion ROIs were deemed by two experienced radiologists as satisfactory
enough for clinical use. The results show that proposed approach is suitable to
be used for perfusion ROI detection from DSC head scans. Segmentation tool
based on the proposed approach can be implemented as a part of any automatic
brain image processing system for clinical use
Towards fully automated deep-learning-based brain tumor segmentation: is brain extraction still necessary?
State-of-the-art brain tumor segmentation is based on deep learning models
applied to multi-modal MRIs. Currently, these models are trained on images
after a preprocessing stage that involves registration, interpolation, brain
extraction (BE, also known as skull-stripping) and manual correction by an
expert. However, for clinical practice, this last step is tedious and
time-consuming and, therefore, not always feasible, resulting in
skull-stripping faults that can negatively impact the tumor segmentation
quality. Still, the extent of this impact has never been measured for any of
the many different BE methods available. In this work, we propose an automatic
brain tumor segmentation pipeline and evaluate its performance with multiple BE
methods. Our experiments show that the choice of a BE method can compromise up
to 15.7% of the tumor segmentation performance. Moreover, we propose training
and testing tumor segmentation models on non-skull-stripped images, effectively
discarding the BE step from the pipeline. Our results show that this approach
leads to a competitive performance at a fraction of the time. We conclude that,
in contrast to the current paradigm, training tumor segmentation models on
non-skull-stripped images can be the best option when high performance in
clinical practice is desired.Comment: 15 pages, 9 figure
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
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
Skull Stripping of Neonatal Brain MRI: Using Prior Shape Information with Graph Cuts
ISSN:0897-1889ISSN:1618-727
A simple rapid process for semi-automated brain extraction from magnetic resonance images of the whole mouse head
Background: Magnetic resonance imaging (MRI) is a well-developed technique in neuroscience. Limitations in applying MRI to rodent models of neuropsychiatric disorders include the large number of animals required to achieve statistical significance, and the paucity of automation tools for the critical early step in processing, brain extraction, which prepares brain images for alignment and voxel-wise statistics.
New Method: This novel timesaving automation of template-based brain extraction (“skull-stripping”) is capable of quickly and reliably extracting the brain from large numbers of whole head images in a single step. The method is simple to install and requires minimal user interaction.
Results: This method is equally applicable to different types of MR images. Results were evaluated with Dice and Jacquard similarity indices and compared in 3D surface projections with other stripping approaches. Statistical comparisons demonstrate that individual variation of brain volumes are preserved.
Comparison with Existing Methods: A downloadable software package not otherwise available for extraction of brains from whole head images is included here. This software tool increases speed, can be used with an atlas or a template from within the dataset, and produces masks that need little further refinement.
Conclusions: Our new automation can be applied to any MR dataset, since the starting point is a template mask generated specifically for that dataset. The method reliably and rapidly extracts brain images from whole head images, rendering them useable for subsequent analytical processing. This software tool will accelerate the exploitation of mouse models for the investigation of human brain disorders by MRI
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