942 research outputs found
Brain Tumor Detection and Segmentation in Multisequence MRI
Tato práce se zabĂ˝vá detekcĂ a segmentacĂ mozkovĂ©ho nádoru v multisekvenÄŤnĂch MR obrazech se zaměřenĂm na gliomy vysokĂ©ho a nĂzkĂ©ho stupnÄ› malignity. Jsou zde pro tento účel navrĹľeny tĹ™i metody. PrvnĂ metoda se zabĂ˝vá detekcĂ prezence částĂ mozkovĂ©ho nádoru v axiálnĂch a koronárnĂch Ĺ™ezech. Jedná se o algoritmus zaloĹľenĂ˝ na analĂ˝ze symetrie pĹ™i rĹŻznĂ˝ch rozlišenĂch obrazu, kterĂ˝ byl otestován na T1, T2, T1C a FLAIR obrazech. Druhá metoda se zabĂ˝vá extrakcĂ oblasti celĂ©ho mozkovĂ©ho nádoru, zahrnujĂcĂ oblast jádra tumoru a edĂ©mu, ve FLAIR a T2 obrazech. Metoda je schopna extrahovat mozkovĂ˝ nádor z 2D i 3D obrazĹŻ. Je zde opÄ›t vyuĹľita analĂ˝za symetrie, která je následována automatickĂ˝m stanovenĂm intenzitnĂho prahu z nejvĂce asymetrickĂ˝ch částĂ. TĹ™etĂ metoda je zaloĹľena na predikci lokálnĂ struktury a je schopna segmentovat celou oblast nádoru, jeho jádro i jeho aktivnà část. Metoda vyuĹľĂvá faktu, Ĺľe vÄ›tšina lĂ©kaĹ™skĂ˝ch obrazĹŻ vykazuje vysokou podobnost intenzit sousednĂch pixelĹŻ a silnou korelaci mezi intenzitami v rĹŻznĂ˝ch obrazovĂ˝ch modalitách. JednĂm ze zpĹŻsobĹŻ, jak s touto korelacĂ pracovat a pouĹľĂvat ji, je vyuĹľitĂ lokálnĂch obrazovĂ˝ch polĂ. Podobná korelace existuje takĂ© mezi sousednĂmi pixely v anotaci obrazu. Tento pĹ™Ăznak byl vyuĹľit v predikci lokálnĂ struktury pĹ™i lokálnĂ anotaci polĂ. Jako klasifikaÄŤnĂ algoritmus je v tĂ©to metodÄ› pouĹľita konvoluÄŤnĂ neuronová sĂĹĄ vzhledem k jejĂ známe schopnosti zacházet s korelacĂ mezi pĹ™Ăznaky. Všechny tĹ™i metody byly otestovány na veĹ™ejnĂ© databázi 254 multisekvenÄŤnĂch MR obrazech a byla dosáhnuta pĹ™esnost srovnatelná s nejmodernÄ›jšĂmi metodami v mnohem kratšĂm vĂ˝poÄŤetnĂm ÄŤase (v řádu sekund pĹ™i pouĹľitĂ˝ CPU), coĹľ poskytuje moĹľnost manuálnĂch Ăşprav pĹ™i interaktivnĂ segmetaci.This work deals with the brain tumor detection and segmentation in multisequence MR images with particular focus on high- and low-grade gliomas. Three methods are propose for this purpose. The first method deals with the presence detection of brain tumor structures in axial and coronal slices. This method is based on multi-resolution symmetry analysis and it was tested for T1, T2, T1C and FLAIR images. The second method deals with extraction of the whole brain tumor region, including tumor core and edema, in FLAIR and T2 images and is suitable to extract the whole brain tumor region from both 2D and 3D. It also uses the symmetry analysis approach which is followed by automatic determination of the intensity threshold from the most asymmetric parts. The third method is based on local structure prediction and it is able to segment the whole tumor region as well as tumor core and active tumor. This method takes the advantage of a fact that most medical images feature a high similarity in intensities of nearby pixels and a strong correlation of intensity profiles across different image modalities. One way of dealing with -- and even exploiting -- this correlation is the use of local image patches. In the same way, there is a high correlation between nearby labels in image annotation, a feature that has been used in the ``local structure prediction'' of local label patches. Convolutional neural network is chosen as a learning algorithm, as it is known to be suited for dealing with correlation between features. All three methods were evaluated on a public data set of 254 multisequence MR volumes being able to reach comparable results to state-of-the-art methods in much shorter computing time (order of seconds running on CPU) providing means, for example, to do online updates when aiming at an interactive segmentation.
A robust framework for medical image segmentation through adaptable class-specific representation
Medical image segmentation is an increasingly important component in virtual pathology, diagnostic imaging and computer-assisted surgery. Better hardware for image acquisition and a variety of advanced visualisation methods have paved the way for the development of computer based tools for medical image analysis and interpretation. The routine use of medical imaging scans of multiple modalities has been growing over the last decades and data sets such as the Visible Human Project have introduced a new modality in the form of colour cryo section data. These developments have given rise to an increasing need for better automatic and semiautomatic segmentation methods. The work presented in this thesis concerns the development of a new framework for robust semi-automatic segmentation of medical imaging data of multiple modalities. Following the specification of a set of conceptual and technical requirements, the framework known as ACSR (Adaptable Class-Specific Representation) is developed in the first case for 2D colour cryo section
segmentation. This is achieved through the development of a novel algorithm for adaptable class-specific sampling of point neighbourhoods, known as the PGA (Path Growing Algorithm), combined with Learning Vector Quantization. The framework is extended to accommodate 3D volume segmentation of cryo section data and subsequently segmentation of single and multi-channel greyscale MRl data. For the latter the issues of inhomogeneity and noise are specifically addressed. Evaluation is based on comparison with previously published results on standard simulated and real data sets, using visual presentation, ground truth comparison and human observer experiments. ACSR provides the user with a simple and intuitive visual initialisation process followed by a fully automatic segmentation. Results on both cryo section and MRI data compare favourably to existing methods, demonstrating robustness both to common artefacts and multiple user initialisations. Further developments into specific clinical applications are discussed in the future work section
Methodology for Jointly Assessing Myocardial Infarct Extent and Regional Contraction in 3-D CMRI
Automated extraction of quantitative parameters from Cardiac Magnetic
Resonance Images (CMRI) is crucial for the management of patients with
myocardial infarct. This work proposes a post-processing procedure to jointly
analyze Cine and Delayed-Enhanced (DE) acquisitions in order to provide an
automatic quantification of myocardial contraction and enhancement parameters
and a study of their relationship. For that purpose, the following processes
are performed: 1) DE/Cine temporal synchronization and 3D scan alignment, 2) 3D
DE/Cine rigid registration in a region about the heart, 3) segmentation of the
myocardium on Cine MRI and superimposition of the epicardial and endocardial
contours on the DE images, 4) quantification of the Myocardial Infarct Extent
(MIE), 5) study of the regional contractile function using a new index, the
Amplitude to Time Ratio (ATR). The whole procedure was applied to 10 patients
with clinically proven myocardial infarction. The comparison between the MIE
and the visually assessed regional function scores demonstrated that the MIE is
highly related to the severity of the wall motion abnormality. In addition, it
was shown that the newly developed regional myocardial contraction parameter
(ATR) decreases significantly in delayed enhanced regions. This largely
automated approach enables a combined study of regional MIE and left
ventricular function
Computational methods to predict and enhance decision-making with biomedical data.
The proposed research applies machine learning techniques to healthcare applications. The core ideas were using intelligent techniques to find automatic methods to analyze healthcare applications. Different classification and feature extraction techniques on various clinical datasets are applied. The datasets include: brain MR images, breathing curves from vessels around tumor cells during in time, breathing curves extracted from patients with successful or rejected lung transplants, and lung cancer patients diagnosed in US from in 2004-2009 extracted from SEER database. The novel idea on brain MR images segmentation is to develop a multi-scale technique to segment blood vessel tissues from similar tissues in the brain. By analyzing the vascularization of the cancer tissue during time and the behavior of vessels (arteries and veins provided in time), a new feature extraction technique developed and classification techniques was used to rank the vascularization of each tumor type. Lung transplantation is a critical surgery for which predicting the acceptance or rejection of the transplant would be very important. A review of classification techniques on the SEER database was developed to analyze the survival rates of lung cancer patients, and the best feature vector that can be used to predict the most similar patients are analyzed
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
Improving deep neural network training with batch size and learning rate optimization for head and neck tumor segmentation on 2D and 3D medical images
Medical imaging is a key tool used in healthcare to diagnose and prognose patients by aiding the detection of a variety of diseases and conditions. In practice, medical image screening must be performed by clinical practitioners who rely primarily on their expertise and experience for disease diagnosis. The ability of convolutional neural networks (CNNs) to extract hierarchical features and determine classifications directly from raw image data makes CNNs a potentially useful adjunct to the medical image analysis process. A common challenge in successfully implementing CNNs is optimizing hyperparameters for training. In this study, we propose a method which utilizes scheduled hyperparameters and Bayesian optimization to classify cancerous and noncancerous tissues (i.e., segmentation) from head and neck computed tomography (CT) and positron emission tomography (PET) scans. The results of this method are compared using CT imaging with and without PET imaging for 2D and 3D image segmentation models
A review on the rule-based filtering structure with applications on computational biomedical images
concepts in the filtering structure. It is crucial for understanding and discussing different principles associated with fuzzy filter design procedures. A number of typical fuzzy multichannel filtering approaches are provided in order to clarify the different fuzzy filter designs and compare different algorithms. In particular, in most practical applications (i.e., biomedical image analysis), the emphasis is placed primarily on fuzzy filtering algorithms, with the main advantages of restoration of corrupted medical images and the interpretation capability, along with the capability of edge preservation and relevant image information for accurate diagnosis of diseases
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