24,180 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
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
Medical imaging analysis with artificial neural networks
Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging
Coronary Artery Segmentation and Motion Modelling
Conventional coronary artery bypass surgery requires invasive sternotomy and the
use of a cardiopulmonary bypass, which leads to long recovery period and has high
infectious potential. Totally endoscopic coronary artery bypass (TECAB) surgery
based on image guided robotic surgical approaches have been developed to allow the
clinicians to conduct the bypass surgery off-pump with only three pin holes incisions
in the chest cavity, through which two robotic arms and one stereo endoscopic camera
are inserted. However, the restricted field of view of the stereo endoscopic images leads
to possible vessel misidentification and coronary artery mis-localization. This results
in 20-30% conversion rates from TECAB surgery to the conventional approach.
We have constructed patient-specific 3D + time coronary artery and left ventricle
motion models from preoperative 4D Computed Tomography Angiography (CTA)
scans. Through temporally and spatially aligning this model with the intraoperative
endoscopic views of the patient's beating heart, this work assists the surgeon to identify
and locate the correct coronaries during the TECAB precedures. Thus this work has
the prospect of reducing the conversion rate from TECAB to conventional coronary
bypass procedures.
This thesis mainly focus on designing segmentation and motion tracking methods
of the coronary arteries in order to build pre-operative patient-specific motion models.
Various vessel centreline extraction and lumen segmentation algorithms are presented,
including intensity based approaches, geometric model matching method and
morphology-based method. A probabilistic atlas of the coronary arteries is formed
from a group of subjects to facilitate the vascular segmentation and registration procedures.
Non-rigid registration framework based on a free-form deformation model
and multi-level multi-channel large deformation diffeomorphic metric mapping are
proposed to track the coronary motion. The methods are applied to 4D CTA images
acquired from various groups of patients and quantitatively evaluated
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