Level-set-based artery-vein separation in blood pool agent CE-MR angiograms

MSO-GP: 3-D segmentation of large and complex conjoined tree structures

Robust segmentation of large and complex conjoined tree structures in 3-D is a major challenge in computer vision. This is particularly true in computational biology, where we often encounter large data structures in size, but few in number, which poses a hard problem for learning algorithms. We show that merging multiscale opening with geodesic path propagation, can shed new light on this classic machine vision challenge, while circumventing the learning issue by developing an unsupervised visual geometry approach (digital topology/morphometry). The novelty of the proposed MSO-GP method comes from the geodesic path propagation being guided by a skeletonization of the conjoined structure that helps to achieve robust segmentation results in a particularly challenging task in this area, that of artery-vein separation from non-contrast pulmonary computed tomography angiograms. This is an important first step in measuring vascular geometry to then diagnose pulmonary diseases and to develop image-based phenotypes. We first present proof-of-concept results on synthetic data, and then verify the performance on pig lung and human lung data with less segmentation time and user intervention needs than those of the competing methods.

MRI-Based Medial Axis Extraction and Boundary Segmentation of Cranial Nerves Through Discrete Deformable 3D Contour and Surface Models

This paper presents a segmentation technique to identify the medial axis and the boundary of cranial nerves. We utilize a 3-D deformable one-simplex discrete contour model to extract the medial axis of each cranial nerve. This contour model represents a collection of two-connected vertices linked by edges, where vertex position is determined by a Newtonian expression for vertex kinematics featuring internal and external forces, the latter of which include attractive forces toward the nerve medial axis. We exploit multiscale vesselness filtering and minimal path techniques in the medial axis extraction method, which also computes a radius estimate along the path. Once we have the medial axis and the radius function of a nerve, we identify the nerve surface using a two-simplex deformable model, which expands radially and can accommodate any nerve shape. As a result, the method proposed here combines the benefits of explicit contour and surface models, while also achieving a cornerstone for future work that will emphasize shape statistics, static collision with other critical structures, and tree-shape analysis.

Automated integer programming based separation of arteries and veins from thoracic CT images

Automated computer-aided analysis of lung vessels has shown to yield promising results for non-invasive diagnosis of lung diseases. To detect vascular changes which affect pulmonary arteries and veins differently, both compartments need to be identified. We present a novel, fully automatic method that separates arteries and veins in thoracic computed tomography images, by combining local as well as global properties of pulmonary vessels. We split the problem into two parts: the extraction of multiple distinct vessel subtrees, and their subsequent labeling into arteries and veins. Subtree extraction is performed with an integer program (IP), based on local vessel geometry. As naively solving this IP is time-consuming, we show how to drastically reduce computational effort by reformulating it as a Markov Random Field. Afterwards, each subtree is labeled as either arterial or venous by a second IP, using two anatomical properties of pulmonary vessels: the uniform distribution of arteries and veins, and the parallel configuration and close proximity of arteries and bronchi. We evaluate algorithm performance by comparing the results with 25 voxel-based manual reference segmentations. On this dataset, we show good performance of the subtree extraction, consisting of very few non-vascular structures (median value: 0.9%) and merged subtrees (median value: 0.6%). The resulting separation of arteries and veins achieves a median voxel-based overlap of 96.3% with the manual reference segmentations, outperforming a state-of-the-art interactive method. In conclusion, our novel approach provides an opportunity to become an integral part of computer aided pulmonary diagnosis, where artery/vein separation is important.

Targeted vessel reconstruction in non-contrast-enhanced steady-state free precession angiography

Image quality in non-contrast-enhanced (NCE) angiograms is often limited by scan time constraints. An effective solution is to undersample angiographic acquisitions and to recover vessel images with penalized reconstructions. However, conventional methods leverage penalty terms with uniform spatial weighting, which typically yield insufficient suppression of aliasing interference and suboptimal blood/background contrast. Here we propose a two-stage strategy where a tractographic segmentation is employed to auto-extract vasculature maps from undersampled data. These maps are then used to incur spatially adaptive sparsity penalties on vascular and background regions. In vivo steady-state free precession angiograms were acquired in the hand, lower leg and foot. Compared with regular non-adaptive compressed sensing (CS) reconstructions (CSlow ), the proposed strategy improves blood/background contrast by 71.3 ± 28.9% in the hand (mean ± s.d. across acceleration factors 1-8), 30.6 ± 11.3% in the lower leg and 28.1 ± 7.0% in the foot (signed-rank test, P < 0.05 at each acceleration). The proposed targeted reconstruction can relax trade-offs between image contrast, resolution and scan efficiency without compromising vessel depiction.

Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: the VESSEL12 study

The VESSEL12 (VESsel SEgmentation in the Lung) challenge objectively compares the performance of different algorithms to identify vessels in thoracic computed tomography (CT) scans. Vessel segmentation is fundamental in computer aided processing of data generated by 3D imaging modalities. As manual vessel segmentation is prohibitively time consuming, any real world application requires some form of automation. Several approaches exist for automated vessel segmentation, but judging their relative merits is difficult due to a lack of standardized evaluation. We present an annotated reference dataset containing 20 CT scans and propose nine categories to perform a comprehensive evaluation of vessel segmentation algorithms from both academia and industry. Twenty algorithms participated in the VESSEL12 challenge, held at International Symposium on Biomedical Imaging (ISBI) 2012. All results have been published at the VESSEL12 website http://vessel12.grand-challenge.org. The challenge remains ongoing and open to new participants. Our three contributions are: (1) an annotated reference dataset available online for evaluation of new algorithms; (2) a quantitative scoring system for objective comparison of algorithms; and (3) performance analysis of the strengths and weaknesses of the various vessel segmentation methods in the presence of various lung diseases.

A vessel active contour model for vascular segmentation

This paper proposes a vessel active contour model based on local intensity weighting and a vessel vector field. Firstly, the energy function we define is evaluated along the evolving curve instead of all image points, and the function value at each point on the curve is based on the interior and exterior weighted means in a local neighborhood of the point, which is good for dealing with the intensity inhomogeneity. Secondly, a vascular vector field derived from a vesselness measure is employed to guide the contour to evolve along the vessel central skeleton into thin and weak vessels. Thirdly, an automatic initialization method that makes the model converge rapidly is developed, and it avoids repeated trails in conventional local region active contour models. Finally, a speed-up strategy is implemented by labeling the steadily evolved points, and it avoids the repeated computation of these points in the subsequent iterations. Experiments using synthetic and real vessel images validate the proposed model. Comparisons with the localized active contour model, local binary fitting model, and vascular active contour model show that the proposed model is more accurate, efficient, and suitable for extraction of the vessel tree from different medical images.

3-D curvilinear structure detection filter via structure-ball analysis

Curvilinear structure detection filters are crucial building blocks in many medical image processing applications, where they are used to detect important structures, such as blood vessels, airways, and other similar fibrous tissues. Unfortunately, most of these filters are plagued by an implicit single structure direction assumption, which results in a loss of signal around bifurcations. This peculiarity limits the performance of all subsequent processes, such as understanding angiography acquisitions, computing an accurate segmentation or tractography, or automatically classifying image voxels. This paper presents a new 3-D curvilinear structure detection filter based on the analysis of the structure ball, a geometric construction representing second order differences sampled in many directions. The structure ball is defined formally, and its computation on a discreet image is discussed. A contrast invariant diffusion index easing voxel analysis and visualization is also introduced, and different structure ball shape descriptors are proposed. A new curvilinear structure detection filter is defined based on the shape descriptors that best characterize curvilinear structures. The new filter produces a vesselness measure that is robust to the presence of X- and Y-junctions along the structure by going beyond the single direction assumption. At the same time, it stays conceptually simple and deterministic, and allows for an intuitive representation of the structure's principal directions. Sample results are provided for synthetic images and for two medical imaging modalities.

A new paradigm of interactive artery/vein separation in noncontrast pulmonary CT imaging using multiscale topomorphologic opening

Distinguishing pulmonary arterial and venous (A/V) trees via in vivo imaging is a critical first step in the quantification of vascular geometry for the purpose of diagnosing several pulmonary diseases and to develop new image-based phenotypes. A multiscale topomorphologic opening (MSTMO) algorithm has recently been developed in our laboratory for separating A/V trees via noncontrast pulmonary human CT imaging. The method starts with two sets of seeds-one for each of A/V trees and combines fuzzy distance transform and fuzzy connectivity in conjunction with several morphological operations leading to locally adaptive iterative multiscale opening of two mutually conjoined structures. In this paper, we introduce the methods for handling "local update" and "separators" into our previous theoretical formulation and incorporate the algorithm into an effective graphical user interface (GUI). Results of a comprehensive evaluative study assessing both accuracy and reproducibility of the method under the new setup are presented and also, the effectiveness of the GUI-based system toward improving A/V separation results is examined. Accuracy of the method has been evaluated using mathematical phantoms, CT images of contrast-separated pulmonary A/V casting of a pig's lung and noncontrast pulmonary human CT imaging. The method has achieved 99% true A/V labeling in the cast phantom and, almost, 92-94% true labeling in human lung data. Reproducibility of the method has been evaluated using multiuser A/V separation in human CT data along with contrast-enhanced CT images of a pig's lung at different positive end-expiratory pressures (PEEPs). The method has achieved, almost, 92-98% agreements in multiuser A/V labeling with ICC for A/V measures being over 0.96-0.99. Effectiveness of the GUI-based method has been evaluated on human data in terms of improvements of accuracy of A/V separation results and results have shown 8-22% improvements in true A/V labeling. Both qualitative and quantitative results found are very promising.

VESSEL CENTERLINE TRACKING AND BOUNDARY SEGMENTATION IN CORONARY MRA WITH MINIMAL MANUAL INTERACTION

Magnetic resonance angiography (MRA) provides a noninvasive means to detect the presence, location and severity of atherosclerosis throughout the vascular system. In such studies, and especially those in the coronary arteries, the vessel luminal area is typically measured at multiple cross-sectional locations along the course of the artery. The advent of fast volumetric imaging techniques covering proximal to mid segments of coronary arteries necessitates automatic analysis tools requiring minimal manual interactions to robustly measure cross-sectional area along the three-dimensional track of the arteries in under-sampled and non-isotropic datasets. In this work, we present a modular approach based on level set methods to track the vessel centerline, segment the vessel boundaries, and measure transversal area using two user-selected endpoints in each coronary of interest. Arterial area and vessel length are measured using our method and compared to the standard Soap-Bubble reformatting and analysis tool in in-vivo non-contrast enhanced coronary MRA images.

Cerebral artery-vein separation using 0.1-Hz oscillation in dual-wavelength optical imaging

We present a novel artery-vein separation method using 0.1-Hz oscillation at two wavelengths with optical imaging of intrinsic signals (OIS). The 0.1-Hz oscillation at a green light wavelength of 546 nm exhibits greater amplitude in arteries than in veins and is primarily caused by vasomotion, whereas the 0.1-Hz oscillation at a red light wavelength of 630 nm exhibits greater amplitude in veins than in arteries and is primarily caused by changes of deoxyhemoglobin concentration. This spectral feature enables cortical arteries and veins to be segmented independently. The arteries can be segmented on the 0.1-Hz amplitude image at 546 nm using matched filters of a modified dual Gaussian model combining with a single Gaussian model. The veins are a combination of vessels segmented on both amplitude images at the two wavelengths using multiscale matched filters of single Gaussian model. Our method can separate most of the thin arteries and veins from each other, especially the thin arteries with low contrast in raw gray images. In vivo OIS experiments demonstrate the separation ability of the 0.1-Hz based segmentation method in cerebral cortex of eight rats. Two validation studies were undertaken to evaluate the performance of the method by quantifying the arterial and venous length based on a reference standard. The results indicate that our 0.1-Hz method is very effective in separating both large and thin arteries and veins regardless of vessel crossover or overlapping to great extent in comparison with previous methods.

Post-processing central k-space subtraction for high-resolution arterial peripheral MR angiography

Peripheral MR angiography requires high resolution and arterial contrast. Neither can be obtained simultaneously due to the short arterial phase of the contrast agent. To improve temporal resolution, keyhole imaging was developed, which combines high resolution and arterial k-spaces at the time of image acquisition. Here, a related approach is introduced for image post-processing in the Fourier domain. It is demonstrated that simple substitution of the central k-space with low-resolution data leads to severe distortion. Hence, a dedicated calculation scheme is necessary for composite k-space post-processing. A solution is presented for high-resolution arterial peripheral MR angiography that uses subtraction of venous intensities from the central high-resolution k-space. The calculations in the Fourier domain do not require interpolations between the different resolutions. High-resolution steady-state MR angiography, which exhibits contrast-enhanced arteries and veins at an isotropic resolution of 0.65 mm, and standard resolution arterial first-pass MR angiography were combined to obtain images with the resolution of the steady-state images and arterial contrast. Numerical simulations on software phantoms are presented. The operation of the method is demonstrated in five patients.

Vascular active contour for vessel tree segmentation

In this paper, a novel active contour model is proposed for vessel tree segmentation. First, we introduce a region competition-based active contour model exploiting the gaussian mixture model, which mainly segments thick vessels. Second, we define a vascular vector field to evolve the active contour along its center line into the thin and weak vessels. The vector field is derived from the eigenanalysis of the Hessian matrix of the image intensity in a multiscale framework. Finally, a dual curvature strategy, which uses a vesselness measure-dependent function selecting between a minimal principal curvature and a mean curvature criterion, is added to smoothen the surface of the vessel without changing its shape. The developed model is used to extract the liver and lung vessel tree as well as the coronary artery from high-resolution volumetric computed tomography images. Comparisons are made with several classical active contour models and manual extraction. The experiments show that our model is more accurate and robust than these classical models and is, therefore, more suited for automatic vessel tree extraction.

Topomorphologic separation of fused isointensity objects via multiscale opening: separating arteries and veins in 3-D pulmonary CT

A novel multiscale topomorphologic approach for opening of two isointensity objects fused at different locations and scales is presented and applied to separating arterial and venous trees in 3-D pulmonary multidetector X-ray computed tomography (CT) images. Initialized with seeds, the two isointensity objects (arteries and veins) grow iteratively while maintaining their spatial exclusiveness and eventually form two mutually disjoint objects at convergence. The method is intended to solve the following two fundamental challenges: how to find local size of morphological operators and how to trace continuity of locally separated regions. These challenges are met by combining fuzzy distance transform (FDT), a morphologic feature with a topologic fuzzy connectivity, and a new morphological reconstruction step to iteratively open finer and finer details starting at large scales and progressing toward smaller scales. The method employs efficient user intervention at locations where local morphological separability assumption does not hold due to imaging ambiguities or any other reason. The approach has been validated on mathematically generated tubular objects and applied to clinical pulmonary noncontrast CT data for separating arteries and veins. The tradeoff between accuracy and the required user intervention for the method has been quantitatively examined by comparing with manual outlining. The experimental study, based on a blind seed selection strategy, has demonstrated that above 95% accuracy may be achieved using 25-40 seeds for each of arteries and veins. Our method is very promising for semiautomated separation of arteries and veins in pulmonary CT images even when there is no object-specific intensity variation at conjoining locations.

Gadofosveset-enhanced MR angiography of carotid arteries: does steady-state imaging improve accuracy of first-pass imaging? Comparison with selective digital subtraction angiography

PURPOSE

To evaluate the diagnostic accuracy of gadofosveset-enhanced magnetic resonance (MR) angiography in the assessment of carotid artery stenosis, with digital subtraction angiography (DSA) as the reference standard, and to determine the value of reading first-pass, steady-state, and "combined" (first-pass plus steady-state) MR angiograms.

MATERIALS AND METHODS

This study was approved by the local ethics committee, and all subjects gave written informed consent. MR angiography and DSA were performed in 84 patients (56 men, 28 women; age range, 61-76 years) with carotid artery stenosis at Doppler ultrasonography. Three readers reviewed the first-pass, steady-state, and combined MR data sets, and one independent observer evaluated the DSA images to assess stenosis degree, plaque morphology and ulceration, stenosis length, and tandem lesions. Interobserver agreement regarding MR angiographic findings was analyzed by using intraclass correlation and Cohen kappa coefficients. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated by using the McNemar test to determine possible significant differences (P < .05).

RESULTS

Interobserver agreement regarding all MR angiogram readings was substantial. For grading stenosis, sensitivity, specificity, PPV, and NPV were, respectively, 90%, 92%, 91%, and 91% for first-pass imaging; 95% each for steady-state imaging; and 96%, 99%, 99%, and 97% for combined imaging. For evaluation of plaque morphology, respective values were 84%, 86%, 88%, and 82% for first-pass imaging; 98%, 97%, 98%, and 97% for steady-state imaging; and 98%, 100%, 100%, and 97% for combined imaging. Differences between the first-pass, steady-state, and combined image readings for assessment of stenosis degree and plaque morphology were significant (P < .001).

CONCLUSION

Gadofosveset-enhanced MR angiography is a promising technique for imaging carotid artery stenosis. Steady-state image reading is superior to first-pass image reading, but the combined reading protocol is more accurate.

A non-parametric vessel detection method for complex vascular structures

Modern medical imaging techniques enable the acquisition of in vivo high resolution images of the vascular system. Most common methods for the detection of vessels in these images, such as multiscale Hessian-based operators and matched filters, rely on the assumption that at each voxel there is a single cylinder. Such an assumption is clearly violated at the multitude of branching points that are easily observed in all, but the most focused vascular image studies. In this paper, we propose a novel method for detecting vessels in medical images that relaxes this single cylinder assumption. We directly exploit local neighborhood intensities and extract characteristics of the local intensity profile (in a spherical polar coordinate system) which we term as the polar neighborhood intensity profile. We present a new method to capture the common properties shared by polar neighborhood intensity profiles for all the types of vascular points belonging to the vascular system. The new method enables us to detect vessels even near complex extreme points, including branching points. Our method demonstrates improved performance over standard methods on both 2D synthetic images and 3D animal and clinical vascular images, particularly close to vessel branching regions.

A variational method for geometric regularization of vascular segmentation in medical images

In this paper, a level-set-based geometric regularization method is proposed which has the ability to estimate the local orientation of the evolving front and utilize it as shape induced information for anisotropic propagation. We show that preserving anisotropic fronts can improve elongations of the extracted structures, while minimizing the risk of leakage. To that end, for an evolving front using its shape-offset level-set representation, a novel energy functional is defined. It is shown that constrained optimization of this functional results in an anisotropic expansion flow which is usefull for vessel segmentation. We have validated our method using synthetic data sets, 2-D retinal angiogram images and magnetic resonance angiography volumetric data sets. A comparison has been made with two state-of-the-art vessel segmentation methods. Quantitative results, as well as qualitative comparisons of segmentations, indicate that our regularization method is a promising tool to improve the efficiency of both techniques.

Basics of Magnetic Resonance Imaging and Magnetic Resonance Spectroscopy

In this chapter, the basic principles of magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) (Sects. 2.2, 2.3, and 2.4), the technical components of the MRI scanner (Sect. 2.5), and the basics of contrast agents and the application thereof (Sect. 2.6) are described. Furthermore, flow phenomena and MR angiography (Sect. 2.7) as well as diffusion and tensor imaging (Sect. 2.7) are elucidated.

2-D registration and 3-D shape inference of the retinal fundus from fluorescein images

This study presents methods to 2-D registration of retinal image sequences and 3-D shape inference from fluorescein images. The Y-feature is a robust geometric entity that is largely invariant across modalities as well as across the temporal grey level variations induced by the propagation of the dye in the vessels. We first present a Y-feature extraction method that finds a set of Y-feature candidates using local image gradient information. A gradient-based approach is then used to align an articulated model of the Y-feature to the candidates more accurately while optimizing a cost function. Using mutual information, fitted Y-features are subsequently matched across images, including colors and fluorescein angiographic frames, for registration. To reconstruct the retinal fundus in 3-D, the extracted Y-features are used to estimate the epipolar geometry with a plane-and-parallax approach. The proposed solution provides a robust estimation of the fundamental matrix suitable for plane-like surfaces, such as the retinal fundus. The mutual information criterion is used to accurately estimate the dense disparity map. Our experimental results validate the proposed method on a set of difficult fluorescein image pairs.

Vessels as 4-D curves: global minimal 4-D paths to extract 3-D tubular surfaces and centerlines

In this paper, we propose an innovative approach to the segmentation of tubular structures. This approach combines all of the benefits of minimal path techniques such as global minimizers, fast computation, and powerful incorporation of user input, while also having the capability to represent and detect vessel surfaces directly which so far has been a feature restricted to active contour and surface techniques. The key is to represent the trajectory of a tubular structure not as a 3-D curve but to go up a dimension and represent the entire structure as a 4-D curve. Then we are able to fully exploit minimal path techniques to obtain global minimizing trajectories between two user supplied endpoints in order to reconstruct tubular structures from noisy or low contrast 3-D data without the sensitivity to local minima inherent in most active surface techniques. In contrast to standard purely spatial 3-D minimal path techniques, however, we are able to represent a full tubular surface rather than just a curve which runs through its interior. Our representation also yields a natural notion of a tube's "central curve." We demonstrate and validate the utility of this approach on magnetic resonance (MR) angiography and computed tomography (CT) images of coronary arteries.

Identification of the main arterial branches by whole-body contrast-enhanced MRA in elderly subjects using limited user interaction and fast marching

PURPOSE

To extract a graph model corresponding to a predefined set of arterial branches from whole-body contrast-enhanced magnetic resonance angiography (CE-MRA) data sets in elderly asymptomatic subjects, a high-incidence group.

MATERIALS AND METHODS

Maximum intensity projections (MIPs) were used as an interface to place landmarks in the three-dimensional (3D) data sets. These landmarks were linked together using fast marching to form a graph model of the arterial tree. Only vessels of interest were identified.

RESULTS

We tested our method on 10 subjects. We were able to build a graph model of the main arterial branches that performed well in the presence of vascular pathologies, such as stenosis and aneurysm. The results were rated by an experienced radiologist, with an overall success rate of 80%.

CONCLUSION

We were able to extract chosen arterial branches in 3D whole-body CE-MRA images with a moderate amount of interaction using a single MIP projection.

Vessel axis tracking using topology constrained surface evolution

An approach to 3-D vessel axis tracking based on surface evolution is presented. The main idea is to guide the evolution of the surface by analyzing its skeleton topology during evolution, and imposing shape constraints on the topology. For example, the intermediate topology can be processed such that it represents a single vessel segment, a bifurcation, or a more complex vascular topology. The evolving surface is then reinitialized with the newly found topology. Reinitialization is a crucial step since it creates probing behavior of the evolving front, encourages the segmentation process to extract the vascular structure of interest and reduces the risk on leaking of the curve into the background. The method was evaluated in two computed tomography angiography applications: 1) extracting the internal carotid arteries including the region in which they traverse through the skull base, which is challenging due to the proximity of bone structures and overlap in intensity values; 2) extracting the carotid bifurcations including many cases in which they are severely stenosed and contain calcifications. The vessel axis was found in 90% (18/20 internal carotids in ten patients) and 70% (14/20 carotid bifurcations in a different set of ten patients) of the cases.

Coronary extraction and stenosis quantification in X-ray angiographic imaging

This work describes a method to quantify stenosis in X-ray coronary angiography. Vascular edge extraction is first performed based on a deformable spline algorithm. It makes use of directional S-Gabor filters to build an external energy field that is then used in a snake optimisation scheme. A string matching technique is then applied to match the contour points and obtain a trace between the matched points. This trace allows then computing the vessel diameter and deriving quantitative stenosis measurements. Experimental results are presented on simulated data and real images.

Evaluation of an improved technique for lumen path definition and lumen segmentation of atherosclerotic vessels in CT angiography

Vessel image analysis is crucial when considering therapeutical options for (cardio-) vascular diseases. Our method, VAMPIRE (Vascular Analysis using Multiscale Paths Inferred from Ridges and Edges), involves two parts: a user defines a start- and endpoint upon which a lumen path is automatically defined, and which is used for initialization; the automatic segmentation of the vessel lumen on computed tomographic angiography (CTA) images. Both parts are based on the detection of vessel-like structures by analyzing intensity, edge, and ridge information. A multi-observer evaluation study was performed to compare VAMPIRE with a conventional method on the CTA data of 15 patients with carotid artery stenosis. In addition to the start- and endpoint, the two radiologists required on average 2.5 (SD: 1.9) additional points to define a lumen path when using the conventional method, and 0.1 (SD: 0.3) when using VAMPIRE. The segmentation results were quantitatively evaluated using Similarity Indices, which were slightly lower between VAMPIRE and the two radiologists (respectively 0.90 and 0.88) compared with the Similarity Index between the radiologists (0.92). The evaluation shows that the improved definition of a lumen path requires minimal user interaction, and that using this path as initialization leads to good automatic lumen segmentation results.

In vivo volumetric imaging of subcutaneous microvasculature by photoacoustic microscopy

Photoacoustic microscopy was developed to achieve volumetric imaging of the anatomy and functions of the subcutaneous microvasculature in both small animals and humans in vivo with high spatial resolution and high signal-to-background ratio. By following the skin contour in raster scanning, the ultrasonic transducer maintains focusing in the region of interest. Furthermore, off-focus lateral resolution is improved by using a synthetic-aperture focusing technique based on the virtual point detector concept. Structural images are acquired in both rats and humans, whereas functional images representing hemoglobin oxygen saturation are acquired in rats. After multiscale vesselness filtering, arterioles and venules in the image are separated based on the imaged oxygen saturation levels. Detailed structural information, such as vessel depth and spatial orientation, are revealed by volume rendering.

Model-based automated extraction of microtubules from electron tomography volume

We propose a model-based automated approach to extracting microtubules from noisy electron tomography volume. Our approach consists of volume enhancement, microtubule localization, and boundary segmentation to exploit the unique geometric and photometric properties of microtubules. The enhancement starts with an anisotropic invariant wavelet transform to enhance the microtubules globally, followed by a three-dimensional (3-D) tube-enhancing filter based on Weingarten matrix to further accentuate the tubular structures locally. The enhancement ends with a modified coherence-enhancing diffusion to complete the interruptions along the microtubules. The microtubules are then localized with a centerline extraction algorithm adapted for tubular objects. To perform segmentation, we novelly modify and extend active shape model method. We first use 3-D local surface enhancement to characterize the microtubule boundary and improve shape searching by relating the boundary strength with the weight matrix of the searching error. We then integrate the active shape model with Kalman filtering to utilize the longitudinal smoothness along the microtubules. The segmentation improved in this way is robust against missing boundaries and outliers that are often present in the tomography volume. Experimental results demonstrate that our automated method produces results close to those by manual process and uses only a fraction of the time of the latter.

Automated extraction of fine features of kinetochore microtubules and plus-ends from electron tomography volume

Kinetochore microtubules (KMTs) and the associated plus-ends have been areas of intense investigation in both cell biology and molecular medicine. Though electron tomography opens up new possibilities in understanding their function by imaging their high-resolution structures, the interpretation of the acquired data remains an obstacle because of the complex and cluttered cellular environment. As a result, practical segmentation of the electron tomography data has been dominated by manual operation, which is time consuming and subjective. In this paper, we propose a model-based automated approach to extracting KMTs and the associated plus-ends with a coarse-to-fine scale scheme consisting of volume preprocessing, microtubule segmentation and plus-end tracing. In volume preprocessing, we first apply an anisotropic invariant wavelet transform and a tube-enhancing filter to enhance the microtubules at coarse level for localization. This is followed with a surface-enhancing filter to accentuate the fine microtubule boundary features. The microtubule body is then segmented using a modified active shape model method. Starting from the segmented microtubule body, the plus-ends are extracted with a probabilistic tracing method improved with rectangular window based feature detection and the integration of multiple cues. Experimental results demonstrate that our automated method produces results comparable to manual segmentation but using only a fraction of the manual segmentation time.

Level set based cerebral vasculature segmentation and diameter quantification in CT angiography

A level set based method is presented for cerebral vascular tree segmentation from computed tomography angiography (CTA) data. The method starts with bone masking by registering a contrast enhanced scan with a low-dose mask scan in which the bone has been segmented. Then an estimate of the background and vessel intensity distributions is made based on the intensity histogram which is used to steer the level set to capture the vessel boundaries. The relevant parameters of the level set evolution are optimized using a training set. The method is validated by a diameter quantification study which is carried out on phantom data, representing ground truth, and 10 patient data sets. The results are compared to manually obtained measurements by two expert observers. In the phantom study, the method achieves similar accuracy as the observers, but is unbiased whereas the observers are biased, i.e., the results are 0.00+/-0.23 vs. -0.32+/-0.23 mm. Also, the method's reproducibility is slightly better than the inter-and intra-observer variability. In the patient study, the method is in agreement with the observers and also, the method's reproducibility -0.04+/-0.17 mm is similar to the inter-observer variability 0.06+/-0.17 mm. Since the method achieves comparable accuracy and reproducibility as the observers, and since the method achieves better performance than the observers with respect to ground truth, we conclude that the level set based vessel segmentation is a promising method for automated and accurate CTA diameter quantification.

The utility of superparamagnetic contrast agents in MRI: theoretical consideration and applications in the cardiovascular system

This review will discuss the in vivo physical chemical relaxation properties of superparamagnetic iron oxide particles. Various parameters such as size, magnetization, compartmentalization and water exchange effects and how these alter the behavior of the iron oxide particles in an in vitro vs an in vivo situation with special reference to the cardiovascular system will be exemplified. Furthermore, applications using iron oxide particles for vascular, perfusion and viability imaging as well as assessment of the inflammatory status of a given tissue will be discussed.

Quantifying 3-D vascular structures in MRA images using hybrid PDE and geometric deformable models

The aim of this paper is to present a hybrid approach to accurate quantification of vascular structures from magnetic resonance angiography (MRA) images using level set methods and deformable geometric models constructed with 3-D Delaunay triangulation. Multiple scale filtering based on the analysis of local intensity structure using the Hessian matrix is used to effectively enhance vessel structures with various diameters. The level set method is then applied to automatically segment vessels enhanced by the filtering with a speed function derived from enhanced MRA images. Since the goal of this paper is to obtain highly accurate vessel borders, suitable for use in fluid flow simulations, in a subsequent step, the vessel surface determined by the level set method is triangulated using 3-D Delaunay triangulation and the resulting surface is used as a parametric deformable model. Energy minimization is then performed within a variational setting with a first-order internal energy; the external energy is derived from 3-D image gradients. Using the proposed method, vessels are accurately segmented from MRA data.