570 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
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
Advanced Algorithms for 3D Medical Image Data Fusion in Specific Medical Problems
FĂșze obrazu je dnes jednou z nejbÄĆŸnÄjĆĄĂch avĆĄak stĂĄle velmi diskutovanou oblastĂ v lĂ©kaĆskĂ©m zobrazovĂĄnĂ a hraje dĆŻleĆŸitou roli ve vĆĄech oblastech lĂ©kaĆskĂ© pĂ©Äe jako je diagnĂłza, lĂ©Äba a chirurgie. V tĂ©to dizertaÄnĂ prĂĄci jsou pĆedstaveny tĆi projekty, kterĂ© jsou velmi Ășzce spojeny s oblastĂ fĂșze medicĂnskĂœch dat. PrvnĂ projekt pojednĂĄvĂĄ o 3D CT subtrakÄnĂ angiografii dolnĂch konÄetin. V prĂĄci je vyuĆŸito kombinace kontrastnĂch a nekontrastnĂch dat pro zĂskĂĄnĂ kompletnĂho cĂ©vnĂho stromu. DruhĂœ projekt se zabĂœvĂĄ fĂșzĂ DTI a T1 vĂĄhovanĂœch MRI dat mozku. CĂlem tohoto projektu je zkombinovat stukturĂĄlnĂ a funkÄnĂ informace, kterĂ© umoĆŸĆujĂ zlepĆĄit znalosti konektivity v mozkovĂ© tkĂĄni. TĆetĂ projekt se zabĂœvĂĄ metastĂĄzemi v CT ÄasovĂœch datech pĂĄteĆe. Tento projekt je zamÄĆen na studium vĂœvoje metastĂĄz uvnitĆ obratlĆŻ ve fĂșzovanĂ© ÄasovĂ© ĆadÄ snĂmkĆŻ. Tato dizertaÄnĂ prĂĄce pĆedstavuje novou metodologii pro klasifikaci tÄchto metastĂĄz. VĆĄechny projekty zmĂnÄnĂ© v tĂ©to dizertaÄnĂ prĂĄci byly ĆeĆĄeny v rĂĄmci pracovnĂ skupiny zabĂœvajĂcĂ se analĂœzou lĂ©kaĆskĂœch dat, kterou vedl pan Prof. JiĆĂ Jan. Tato dizertaÄnĂ prĂĄce obsahuje registraÄnĂ ÄĂĄst prvnĂho a klasifikaÄnĂ ÄĂĄst tĆetĂho projektu. DruhĂœ projekt je pĆedstaven kompletnÄ. DalĆĄĂ ÄĂĄst prvnĂho a tĆetĂho projektu, obsahujĂcĂ specifickĂ© pĆedzpracovĂĄnĂ dat, jsou obsaĆŸeny v disertaÄnĂ prĂĄci mĂ©ho kolegy Ing. Romana Petera.Image fusion is one of todayÂŽs most common and still challenging tasks in medical imaging and it plays crucial role in all areas of medical care such as diagnosis, treatment and surgery. Three projects crucially dependent on image fusion are introduced in this thesis. The first project deals with the 3D CT subtraction angiography of lower limbs. It combines pre-contrast and contrast enhanced data to extract the blood vessel tree. The second project fuses the DTI and T1-weighted MRI brain data. The aim of this project is to combine the brain structural and functional information that purvey improved knowledge about intrinsic brain connectivity. The third project deals with the time series of CT spine data where the metastases occur. In this project the progression of metastases within the vertebrae is studied based on fusion of the successive elements of the image series. This thesis introduces new methodology of classifying metastatic tissue. All the projects mentioned in this thesis have been solved by the medical image analysis group led by Prof. JiĆĂ Jan. This dissertation concerns primarily the registration part of the first project and the classification part of the third project. The second project is described completely. The other parts of the first and third project, including the specific preprocessing of the data, are introduced in detail in the dissertation thesis of my colleague Roman Peter, M.Sc.
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
Towards development of automatic path planning system in image-guided neurosurgery
With the advent of advanced computer technology, many computer-aided systems have evolved to assist in medical related work including treatment, diagnosis, and even surgery. In modern neurosurgery, Magnetic Resonance Image guided stereotactic surgery exactly complies with this trend. It is a minimally invasive operation being much safer than the traditional open-skull surgery, and offers higher precision and more effective operating procedures compared to conventional craniotomy. However, such operations still face significant challenges of planning the optimal neurosurgical path in order to reach the ideal position without damage to important internal structures. This research aims to address this major challenge. The work begins with an investigation of the problem of distortion induced by MR images. It then goes on to build a template of the Circle of Wills brain vessels, realized from a collection of Magnetic Resonance Angiography images, which is needed to maintain operating standards when, as in many cases, Magnetic Resonance Angiography images are not available for patients. Demographic data of brain tumours are also studied to obtain further understanding of diseased human brains through the development of an effect classifier. The developed system allows the internal brain structure to be âseenâ clearly before the surgery, giving surgeons a clear picture and thereby makes a significant contribution to the eventual development of a fully automatic path planning system
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