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Automatic 3D Reconstruction of Coronary Artery Centerlines from Monoplane X-ray Angiogram Images
We present a new method for the fully automatic 3D reconstruction of the coronary artery centerlines, using two X-ray angiogram projection images from a single rotating monoplane acquisition system. During the first stage, the input images are smoothed using curve evolution techniques. Next, a simple yet efficient multiscale method, based on the information of the Hessian matrix, for the enhancement of the vascular structure is introduced. Hysteresis thresholding using different image quantiles, is used to threshold the arteries. This stage is followed by a thinning procedure to extract the centerlines. The resulting skeleton image is then pruned using morphological and pattern recognition techniques to remove non-vessel like structures. Finally, edge-based stereo correspondence is solved using a parallel evolutionary optimization method based on f symbiosis. The detected 2D centerlines combined with disparity map information allow the reconstruction of the 3D vessel centerlines. The proposed method has been evaluated on patient data sets for evaluation purposes
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
Reconstruction of coronary arteries from X-ray angiography: A review.
Despite continuous progress in X-ray angiography systems, X-ray coronary angiography is fundamentally limited by its 2D representation of moving coronary arterial trees, which can negatively impact assessment of coronary artery disease and guidance of percutaneous coronary intervention. To provide clinicians with 3D/3D+time information of coronary arteries, methods computing reconstructions of coronary arteries from X-ray angiography are required. Because of several aspects (e.g. cardiac and respiratory motion, type of X-ray system), reconstruction from X-ray coronary angiography has led to vast amount of research and it still remains as a challenging and dynamic research area. In this paper, we review the state-of-the-art approaches on reconstruction of high-contrast coronary arteries from X-ray angiography. We mainly focus on the theoretical features in model-based (modelling) and tomographic reconstruction of coronary arteries, and discuss the evaluation strategies. We also discuss the potential role of reconstructions in clinical decision making and interventional guidance, and highlight areas for future research
Anatomical Modeling of Cerebral Microvascular Structures: Application to Identify Biomarkers of Microstrokes
Les rĂ©seaux microvasculaires corticaux sont responsables du transport de lâoxygĂšne et des substrats Ă©nergĂ©tiques vers les neurones. Ces rĂ©seaux rĂ©agissent dynamiquement aux demandes Ă©nergĂ©tiques lors dâune activation neuronale par le biais du couplage neurovasculaire. Afin dâĂ©lucider le rĂŽle de la composante microvasculaire dans ce processus de couplage, lâutilisation de la modĂ©lisation in-formatique pourrait se rĂ©vĂ©ler un Ă©lĂ©ment clĂ©. Cependant, la manque de mĂ©thodologies de calcul appropriĂ©es et entiĂšrement automatisĂ©es pour modĂ©liser et caractĂ©riser les rĂ©seaux microvasculaires reste lâun des principaux obstacles. Le dĂ©veloppement dâune solution entiĂšrement automatisĂ©e est donc important pour des explorations plus avancĂ©es, notamment pour quantifier lâimpact des mal-formations vasculaires associĂ©es Ă de nombreuses maladies cĂ©rĂ©brovasculaires. Une observation courante dans lâensemble des troubles neurovasculaires est la formation de micro-blocages vascu-laires cĂ©rĂ©braux (mAVC) dans les artĂ©rioles pĂ©nĂ©trantes de la surface piale. De rĂ©cents travaux ont dĂ©montrĂ© lâimpact de ces Ă©vĂ©nements microscopiques sur la fonction cĂ©rĂ©brale. Par consĂ©quent, il est dâune importance vitale de dĂ©velopper une approche non invasive et comparative pour identifier leur prĂ©sence dans un cadre clinique.
Dans cette thĂšse,un pipeline de traitement entiĂšrement automatisĂ© est proposĂ© pour aborder le prob-lĂšme de la modĂ©lisation anatomique microvasculaire. La mĂ©thode de modĂ©lisation consiste en un rĂ©seau de neurones entiĂšrement convolutif pour segmenter les capillaires sanguins, un gĂ©nĂ©rateur de modĂšle de surface 3D et un algorithme de contraction de la gĂ©omĂ©trie pour produire des mod-Ăšles graphiques vasculaires ne comportant pas de connections multiples. Une amĂ©lioration de ce pipeline est dĂ©veloppĂ©e plus tard pour allĂ©ger lâexigence de maillage lors de la phase de reprĂ©sen-tation graphique. Un nouveau schĂ©ma permettant de gĂ©nĂ©rer un modĂšle de graphe est dĂ©veloppĂ© avec des exigences dâentrĂ©e assouplies et permettant de retenir les informations sur les rayons des vaisseaux. Il est inspirĂ© de graphes gĂ©omĂ©triques dĂ©formants construits en respectant les morpholo-gies vasculaires au lieu de maillages de surface. Un mĂ©canisme pour supprimer la structure initiale du graphe Ă chaque exĂ©cution est implĂ©mentĂ© avec un critĂšre de convergence pour arrĂȘter le pro-cessus. Une phase de raffinement est introduite pour obtenir des modĂšles vasculaires finaux. La modĂ©lisation informatique dĂ©veloppĂ©e est ensuite appliquĂ©e pour simuler les signatures IRM po-tentielles de mAVC, combinant le marquage de spin artĂ©riel (ASL) et lâimagerie multidirectionnelle pondĂ©rĂ©e en diffusion (DWI). LâhypothĂšse est basĂ©e sur des observations rĂ©centes dĂ©montrant une rĂ©orientation radiale de la microvascularisation dans la pĂ©riphĂ©rie du mAVC lors de la rĂ©cupĂ©ra-tion chez la souris. Des lits capillaires synthĂ©tiques, orientĂ©s alĂ©atoirement et radialement, et des angiogrammes de tomographie par cohĂ©rence optique (OCT), acquis dans le cortex de souris (n = 5) avant et aprĂšs lâinduction dâune photothrombose ciblĂ©e, sont analysĂ©s. Les graphes vasculaires informatiques sont exploitĂ©s dans un simulateur 3D Monte-Carlo pour caractĂ©riser la rĂ©ponse par rĂ©sonance magnĂ©tique (MR), tout en considĂ©rant les effets des perturbations du champ magnĂ©tique causĂ©es par la dĂ©soxyhĂ©moglobine, et lâadvection et la diffusion des spins nuclĂ©aires.
Le pipeline graphique proposĂ© est validĂ© sur des angiographies synthĂ©tiques et rĂ©elles acquises avec diffĂ©rentes modalitĂ©s dâimagerie. ComparĂ© Ă dâautres mĂ©thodes effectuĂ©es dans le milieu de la recherche, les expĂ©riences indiquent que le schĂ©ma proposĂ© produit des taux dâerreur gĂ©omĂ©triques et topologiques amoindris sur divers angiogrammes. LâĂ©valuation confirme Ă©galement lâefficacitĂ© de la mĂ©thode proposĂ©e en fournissant des modĂšles reprĂ©sentatifs qui capturent tous les aspects anatomiques des structures vasculaires. Ensuite, afin de trouver des signatures de mAVC basĂ©es sur le signal IRM, la modĂ©lisation vasculaire proposĂ©e est exploitĂ©e pour quantifier le rapport de perte de signal intravoxel minimal lors de lâapplication de plusieurs directions de gradient, Ă des paramĂštres de sĂ©quence variables avec et sans ASL. Avec lâASL, les rĂ©sultats dĂ©montrent une dif-fĂ©rence significative (p <0,05) entre le signal calculĂ© avant et 3 semaines aprĂšs la photothrombose. La puissance statistique a encore augmentĂ© (p <0,005) en utilisant des angiogrammes capturĂ©s Ă la semaine suivante. Sans ASL, aucun changement de signal significatif nâest trouvĂ©. Des rapports plus Ă©levĂ©s sont obtenus Ă des intensitĂ©s de champ magnĂ©tique plus faibles (par exemple, B0 = 3) et une lecture TE plus courte (<16 ms). Cette Ă©tude suggĂšre que les mAVC pourraient ĂȘtre carac-tĂ©risĂ©s par des sĂ©quences ASL-DWI, et fournirait les informations nĂ©cessaires pour les validations expĂ©rimentales postĂ©rieures et les futurs essais comparatifs.----------ABSTRACT
Cortical microvascular networks are responsible for carrying the necessary oxygen and energy substrates to our neurons. These networks react to the dynamic energy demands during neuronal activation through the process of neurovascular coupling. A key element in elucidating the role of the microvascular component in the brain is through computational modeling. However, the lack of fully-automated computational frameworks to model and characterize these microvascular net-works remains one of the main obstacles. Developing a fully-automated solution is thus substantial for further explorations, especially to quantify the impact of cerebrovascular malformations associ-ated with many cerebrovascular diseases. A common pathogenic outcome in a set of neurovascular disorders is the formation of microstrokes, i.e., micro occlusions in penetrating arterioles descend-ing from the pial surface. Recent experiments have demonstrated the impact of these microscopic events on brain function. Hence, it is of vital importance to develop a non-invasive and translatable approach to identify their presence in a clinical setting.
In this thesis, a fully automatic processing pipeline to address the problem of microvascular anatom-ical modeling is proposed. The modeling scheme consists of a fully-convolutional neural network to segment microvessels, a 3D surface model generator and a geometry contraction algorithm to produce vascular graphical models with a single connected component. An improvement on this pipeline is developed later to alleviate the requirement of water-tight surface meshes as inputs to the graphing phase. The novel graphing scheme works with relaxed input requirements and intrin-sically captures vessel radii information, based on deforming geometric graphs constructed within vascular boundaries instead of surface meshes. A mechanism to decimate the initial graph struc-ture at each run is formulated with a convergence criterion to stop the process. A refinement phase is introduced to obtain final vascular models. The developed computational modeling is then ap-plied to simulate potential MRI signatures of microstrokes, combining arterial spin labeling (ASL) and multi-directional diffusion-weighted imaging (DWI). The hypothesis is driven based on recent observations demonstrating a radial reorientation of microvasculature around the micro-infarction locus during recovery in mice. Synthetic capillary beds, randomly- and radially oriented, and op-tical coherence tomography (OCT) angiograms, acquired in the barrel cortex of mice (n=5) before and after inducing targeted photothrombosis, are analyzed. The computational vascular graphs are exploited within a 3D Monte-Carlo simulator to characterize the magnetic resonance (MR) re-sponse, encompassing the effects of magnetic field perturbations caused by deoxyhemoglobin, and the advection and diffusion of the nuclear spins.
The proposed graphing pipeline is validated on both synthetic and real angiograms acquired with different imaging modalities. Compared to other efficient and state-of-the-art graphing schemes, the experiments indicate that the proposed scheme produces the lowest geometric and topological error rates on various angiograms. The evaluation also confirms the efficiency of the proposed scheme in providing representative models that capture all anatomical aspects of vascular struc-tures. Next, searching for MRI-based signatures of microstokes, the proposed vascular modeling is exploited to quantify the minimal intravoxel signal loss ratio when applying multiple gradient di-rections, at varying sequence parameters with and without ASL. With ASL, the results demonstrate a significant difference (p<0.05) between the signal-ratios computed at baseline and 3 weeks after photothrombosis. The statistical power further increased (p<0.005) using angiograms captured at week 4. Without ASL, no reliable signal change is found. Higher ratios with improved significance are achieved at low magnetic field strengths (e.g., at 3 Tesla) and shorter readout TE (<16 ms). This study suggests that microstrokes might be characterized through ASL-DWI sequences, and provides necessary insights for posterior experimental validations, and ultimately, future transla-tional trials
Vessel tractography using an intensity based tensor model with branch detection
In this paper, we present a tubular structure seg- mentation method that utilizes a second order tensor constructed from directional intensity measurements, which is inspired from diffusion tensor image (DTI) modeling. The constructed anisotropic tensor which is fit inside a vessel drives the segmen- tation analogously to a tractography approach in DTI. Our model is initialized at a single seed point and is capable of capturing whole vessel trees by an automatic branch detection algorithm developed in the same framework. The centerline of the vessel as well as its thickness is extracted. Performance results within the Rotterdam Coronary Artery Algorithm Evaluation framework are provided for comparison with existing techniques. 96.4% average overlap with ground truth delineated by experts is obtained in addition to other measures reported in the paper. Moreover, we demonstrate further quantitative results over synthetic vascular datasets, and we provide quantitative experiments for branch detection on patient Computed Tomography Angiography (CTA) volumes, as well as qualitative evaluations on the same CTA datasets, from visual scores by a cardiologist expert
Generalizable automated pixel-level structural segmentation of medical and biological data
Over the years, the rapid expansion in imaging techniques and equipments has driven the demand for more automation in handling large medical and biological data sets. A wealth of approaches have been suggested as optimal solutions for their respective imaging types. These
solutions span various image resolutions, modalities and contrast (staining) mechanisms. Few approaches generalise well across multiple image types, contrasts or resolution.
This thesis proposes an automated pixel-level framework that addresses 2D, 2D+t and 3D
structural segmentation in a more generalizable manner, yet has enough adaptability to address
a number of specific image modalities, spanning retinal funduscopy, sequential
fluorescein angiography and two-photon microscopy.
The pixel-level segmentation scheme involves: i ) constructing a phase-invariant orientation field of the local spatial neighbourhood; ii ) combining local feature maps with intensity-based
measures in a structural patch context; iii ) using a complex supervised learning process to interpret the combination of all the elements in the patch in order to reach a classification decision. This has the advantage of transferability from retinal blood vessels in 2D to neural structures in 3D.
To process the temporal components in non-standard 2D+t retinal angiography sequences, we first introduce a co-registration procedure: at the pairwise level, we combine projective
RANSAC with a quadratic homography transformation to map the coordinate systems between any two frames. At the joint level, we construct a hierarchical approach in order for each individual frame to be registered to the global reference intra- and inter- sequence(s). We then take a non-training approach that searches in both the spatial neighbourhood of each pixel and the filter output across varying scales to locate and link microvascular centrelines to (sub-)
pixel accuracy. In essence, this \link while extract" piece-wise segmentation approach combines the local phase-invariant orientation field information with additional local phase estimates to obtain a soft classification of the centreline (sub-) pixel locations.
Unlike retinal segmentation problems where vasculature is the main focus, 3D neural segmentation requires additional
exibility, allowing a variety of structures of anatomical importance yet with different geometric properties to be differentiated both from the background and against other structures. Notably, cellular structures, such as Purkinje cells, neural dendrites and interneurons, all display certain elongation along their medial axes, yet each class has a characteristic shape captured by an orientation field that distinguishes it from other structures. To take this
into consideration, we introduce a 5D orientation mapping to capture these orientation properties.
This mapping is incorporated into the local feature map description prior to a learning
machine. Extensive performance evaluations and validation of each of the techniques presented in this thesis is carried out. For retinal fundus images, we compute Receiver Operating Characteristic (ROC) curves on existing public databases (DRIVE & STARE) to assess and compare our algorithms with other benchmark methods. For 2D+t retinal angiography sequences, we compute the error metrics ("Centreline Error") of our scheme with other benchmark methods.
For microscopic cortical data stacks, we present segmentation results on both surrogate data with known ground-truth and experimental rat cerebellar cortex two-photon microscopic tissue stacks.Open Acces
Image-to-Graph Convolutional Network for 2D/3D Deformable Model Registration of Low-Contrast Organs
Organ shape reconstruction based on a single-projection image during treatment has wide clinical scope, e.g., in image-guided radiotherapy and surgical guidance. We propose an image-to-graph convolutional network that achieves deformable registration of a three-dimensional (3D) organ mesh for a low-contrast two-dimensional (2D) projection image. This framework enables simultaneous training of two types of transformation: from the 2D projection image to a displacement map, and from the sampled per-vertex feature to a 3D displacement that satisfies the geometrical constraint of the mesh structure. Assuming application to radiation therapy, the 2D/3D deformable registration performance is verified for multiple abdominal organs that have not been targeted to date, i.e., the liver, stomach, duodenum, and kidney, and for pancreatic cancer. The experimental results show shape prediction considering relationships among multiple organs can be used to predict respiratory motion and deformation from digitally reconstructed radiographs with clinically acceptable accuracy
Computerized analysis of coronary artery disease: Performance evaluation of segmentation and tracking of coronary arteries in CT angiograms
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134780/1/mp0294.pd
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