8,018 research outputs found
FRNET: Flattened Residual Network for Infant MRI Skull Stripping
Skull stripping for brain MR images is a basic segmentation task. Although
many methods have been proposed, most of them focused mainly on the adult MR
images. Skull stripping for infant MR images is more challenging due to the
small size and dynamic intensity changes of brain tissues during the early
ages. In this paper, we propose a novel CNN based framework to robustly extract
brain region from infant MR image without any human assistance. Specifically,
we propose a simplified but more robust flattened residual network architecture
(FRnet). We also introduce a new boundary loss function to highlight ambiguous
and low contrast regions between brain and non-brain regions. To make the whole
framework more robust to MR images with different imaging quality, we further
introduce an artifact simulator for data augmentation. We have trained and
tested our proposed framework on a large dataset (N=343), covering newborns to
48-month-olds, and obtained performance better than the state-of-the-art
methods in all age groups.Comment: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI
The Effects of Amygdalar Size Normalization on Group Analysis in Late-Life Depression
Structural MRI has been utilized in numerous ways to measure morphologic characteristics of subcortical brain regions. Volumetric analysis is frequently used to quantify the size of brain structures to ultimately compare size differences between individuals. In order to make such comparisons, inter-subject variability in brain and/or head size must be taken into consideration. A heterogeneous set of methods are commonly used to normalize regional volume by brain and/or head size yielding inconsistent findings making it diffcult to interpret and compare results from published volumetric studies. This study investigated the effect that various volume normalization methodologies might have on group analysis. Specifically, the amygdalae were the regions of interest in elderly, healthy and depressed individuals. Normalization methods investigated included spatial transformations, brain and head volume, and tissue volume techniques. Group analyses were conducted with independent t-tests by dividing amygdalar volumes by various volume measures, as well as with univariate analysis of covariance (ANCOVA) analyses by using amygdalar volumes as dependent variables and various volume measures as covariates. Repeated measures ANOVA was performed to assess the effect of each normalization procedure. Results indicate that volumetric differences between groups varied based on the normalization method utilized, which may explain, in part, the discrepancy found in amygdalar volumetric studies. We believe the findings of this study are extensible to other brain regions and demographics, and thus, investigators should carefully consider the normalization methods utilized in volumetric studies to properly interpret the results and conclusions
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
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