7,484 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
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
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
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