Centerline Tree Research Articles

COACT: Coronary artery centerline tracker.

The curved planar reformation (CPR) technique is one of the most commonly used methods in clinical practice to locate coronary arteries in medical images. The artery centerline is the cornerstone for the generation of the CPR image. Here, we describe the development of a new fully automatic artery centerline tracker with the aim of increasing the efficiency and accuracy of the process. We propose a COronary artery Centerline Tracker (COACT) framework which consists of an ostium point finder (OPFinder) model, an intersection point detector (IPDetector) model and a set of centerline tracking strategies. The output of OPFinder is the ostium points. The function of the IPDetector is to predict the intersections of a sample sphere and the centerlines. The centerline tracking process starts from two ostium points detected by the OPFinder, and combines the results of the IPDetector with a series of strategies to gradually reconstruct the coronary artery centerline tree. Two coronary CT angiography (CCTA) datasets were used to validate the models. Dataset1 contains 160 cases (32 for test and 128 for training) and dataset2 contains 70 cases (20 for test and 50 for training). The results show that the average distance between the ostium points predicted by the OPFinder and the manually annotated ostium points was 0.88mm, which is similar to the differences between the results obtained by two observers (0.85mm). For the IPDetector, the average overlap of the predicted and ground truth intersection points was 97.82% and this is also close to the inter-observer agreement of 98.50%. For the entire coronary centerline tree, the overlap between the results obtained by COACT and the gold standard was 94.33%, which is slightly lower than the inter-observer agreement, 98.39%. We have developed a fully automatic centerline tracking method for CCTA scans and achieved a satisfactory result. The proposed algorithms are also incorporated in the medical image analysis platform TIMESlice (https://slice-doc.netlify.app) for further studies.

A novel method to model hepatic vascular network using vessel segmentation, thinning, and completion.

The accurate modeling of the liver vessel network structure is an important prerequisite for developing a preoperative plan for the liver. Considering that extracting liver blood vessels from patient's abdominal computed tomography(CT) images requires several manual operations, this study proposed an automatic segmentation method of liver vessels based on graph cut, thinning, and vascular combination, which can obtain a complete liver vascular network. First, the CT image was preprocessed by grayscale mapping based on sigmoid function, vessel enhancement based on Hessian filter, and denoising based on anisotropic filter to enhance the grayscale contrast between the vascular and non-vascular parts of the liver. Then, the liver vessels were initially segmented based on the improved three-dimensional graph cut algorithm. Based on the obtained liver vascular structure, the vessel centerline of the liver was then extracted by the proposed thinning algorithm that continuously traversed the foreground voxel points and iteratively deleted the simple points. Finally, the combination of vascular centerline optimization was used to predict and link the vascular centerline fractured portion. The under-segmented liver vessels were complemented based on the complete vascular centerline tree. To verify the proposed hepatic vascular segmentation and complementation algorithm, the open 3D Image Reconstruction for Comparison of Algorithm Database (3Dircadb) was applied to test and quantify the results. The results showed that the proposed algorithm can accurately and effectively segment the vascular network structure from abdominal CT images, and the proposed vascular complementation method can restore the true information of under-segmented liver vessels. Graphical abstract A novel hepatic vessel segmentation method from abdominal CT images was proposed, including graph cut algorithm, centerline extraction, and broken vessel completion. First, the graph cut algorithm was used to obtain the initial segmentation result. Then, the centerline of the initial segmentation result was extracted. Finally, the initial segmentation result was optimized through centerline analysis.

A case study on 3D characterisation of pore structure in a tight sandstone gas reservoir: The Collyhurst Sandstone, East Irish Sea Basin, northern England

Pore systems of tight-gas reservoirs control important parameters such as flow capacity, producible pore volumes, hydrocarbon flow rates and storage which are complex to characterise and to quantify. Detailed understanding of 3D pore structure is vital in predicting tight-gas reservoir productivity. This study investigates the pore architecture of a sandstone sample from Collyhurst Sandstone Formation, northern England, a tight gas reservoir. X-ray Computed Tomography (XCT) was employed to quantify the pore system (pore size, shape, geometry, and connectivity). Three types of pores were identified: inter-grain pores which are at the boundary of detrital grains and/or authigenic minerals, intra-grains pores which are pores within the detrital mineral grains, and intra-cement pores which are pores within the cement phase (authigenic minerals). The first pore type is primary in origin, whereas the latter two are secondary pores. A centerline tree and a pore network model (PNM) algorithm were applied for pore network analysis and a porosity value was estimated. The image-based porosity value was validated with laboratory experimental measurement. 3D image estimates 3.1% volume porosity, while 4.6% was recorded from the laboratory experiment. Spatial quantification of pore types and analysis of pore connectivity of the studied sandstone improves understanding on gas flow behaviour and gas storage. Furthermore, the results are useful in predicting permeability and multi-flow phase properties in a tight gas reservoir.

Improved centerline tree detection of diseased peripheral arteries with a cascading algorithm for vascular segmentation

Vascular segmentation plays an important role in the assessment of peripheral arterial disease. The segmentation is very challenging especially for arteries with severe stenosis or complete occlusion. We present a cascading algorithm for vascular centerline tree detection specializing in detecting centerlines in diseased peripheral arteries. It takes a three-dimensional computed tomography angiography (CTA) volume and returns a vascular centerline tree, which can be used for accelerating and facilitating the vascular segmentation. The algorithm consists of four levels, two of which detect healthy arteries of varying sizes and two that specialize in different types of vascular pathology: severe calcification and occlusion. We perform four main steps at each level: appropriate parameters for each level are selected automatically, a set of centrally located voxels is detected, these voxels are connected together based on the connection criteria, and the resulting centerline tree is corrected from spurious branches. The proposed method was tested on 25 CTA scans of the lower limbs, achieving an average overlap rate of 89% and an average detection rate of 82%. The average execution time using four CPU cores was 70s, and the technique was successful also in detecting very distal artery branches, e.g., in the foot.

The human placenta: Function and morphological assessments using ultrasound and magnetic resonance elastography

s / Placenta 45 (2016) 63e133 74 P1.6 THE HUMAN PLACENTA: FUNCTION AND MORPHOLOGICAL ASSESSMENTS USING ULTRASOUND AND MAGNETIC RESONANCE ELASTOGRAPHY Richard K. Miller , Stephan McAleavey , Juvenal Ormachea , Eva Pressman, Loralei Thornburg , David Dombroski , Jianhui Zhong, Marvin Doyley , Ronald W. Wood, Jonathan Carrol-Nellenback , Ollivier Hyrien , Philip Katzman , Christopher Stodgell , Kam Szlachetka, Henry Wang, Kevin Parker . University of Rochester School of Medicine and Dentistry, Rochester, New York, USA; University of Rochester Haijm School of Engineering, Rochester, New York, USA Objective: In utero evaluations of placental anatomy and function offer the promise of associating placental pathophysiology with neonatal outcomes, and thus potential for enhanced management of perinatal complications and improved long-term health of the mother and child. Ultrasound Elastography (USE) and Magnetic Resonance Elastography (MRE) provide means to evaluate tissue stiffness in utero. In conjunction with measures of vascular anatomy and perfusion, we can test mechanistic hypotheses about the relationship with anatomical function and deviations associated with disease states. For example, The Parker Hypothesis (Parker, Phys. Med. Biol. 59: 4443,14) proposes that viscoelastic measures of soft tissue stiffness are related to the fractal branching properties of the vascular anatomy. Methods: Term human placentae were obtained immediately upon delivery and evaluated directly either in a 3T GE MRI with Elastography or using a Siemens Antares US scanner and VF10-5 Probe with custom single track location shear wave elasticity imaging pulse sequences and accelerated processing. Other placentae were perfused through the umbilical cord and imaging, Doppler measures and elastography were conducted. At the end of the experiments, a barium gel was infused to enhance subsequent CT imaging of the fetal vasculature. Results: The utility of elastography in functional evaluation of the placenta are readily demonstrated in the following examples: 1) Placental tissue anomalies (e.g., infarcted areas) are stiff, and provided dramatic examples of the sensitivity USE and MRE methodologies for the detection of placental pathology; 2) Ex vivo placental perfusions have been used to relate placental stiffness to fetal vasculature when measured by USE, as seen when fetal vascular dynamics were pharmacologically manipulated. Clinical and high-resolution microCT systems can and have been used on placentae to provide additional post-delivery anatomical characterization and quantification of the vascular beds. These CT observations provide confirmation of in vivo Doppler US and the potential use of MR angiography for vascular characterization. We demonstrated that these datasets are amenable to automated identification and characterization of branching morphology, including centerline tree and Sholl analyses. Conclusion: These USE and MRE technologies enable promising new approaches for the prediction of risks during and after pregnancy. (Support: Goode Foundation) MRI localization of a placental infarct with associated elastographic ortho slice A: gross examination of placental lesion. B, MR segmented volume. C. Superimposed MRE. Yellow arrow directs attention to lesion.

Fast vascular skeleton extraction algorithm

Vascular diseases are a common cause of death, particularly in developed countries. Computerized image analysis tools play a potentially important role in diagnosing and quantifying vascular pathologies. Given the size and complexity of modern angiographic data acquisition, fast, automatic and accurate vascular segmentation is a challenging task.In this paper we introduce a fully automatic high-speed vascular skeleton extraction algorithm that is intended as a first step in a complete vascular tree segmentation program. The method takes a 3D unprocessed Computed Tomography Angiography (CTA) scan as input and produces a graph in which the nodes are centrally located artery voxels and the edges represent connections between them. The algorithm works in two passes where the first pass is designed to extract the skeleton of large arteries and the second pass focuses on smaller vascular structures. Each pass consists of three main steps. The first step sets proper parameters automatically using Gaussian curve fitting. In the second step different filters are applied to detect voxels – nodes – that are part of arteries. In the last step the nodes are connected in order to obtain a continuous centerline tree for the entire vasculature. Structures found, that do not belong to the arteries, are removed in a final anatomy-based analysis. The proposed method is computationally efficient with an average execution time of 29 s and has been tested on a set of CTA scans of the lower limbs achieving an average overlap rate of 97% and an average detection rate of 71%.

Automated extraction and labelling of the arterial tree from whole-body MRA data.

In this work, we present a fully automated algorithm for extraction of the 3D arterial tree and labelling the tree segments from whole-body magnetic resonance angiography (WB-MRA) sequences. The algorithm developed consists of two core parts (i) 3D volume reconstruction from different stations with simultaneous correction of different types of intensity inhomogeneity, and (ii) Extraction of the arterial tree and subsequent labelling of the pruned extracted tree. Extraction of the arterial tree is performed using the probability map of the "contrast" class, which is obtained as one of the results of the inhomogeneity correction scheme. We demonstrate that such approach is more robust than using the difference between the pre- and post-contrast channels traditionally used for this purpose. Labelling the extracted tree is performed by using a combination of graph-based and atlas-based approaches. Validation of our method with respect to the extracted tree was performed on the arterial tree subdivided into 32 segments, 82.4% of which were completely detected, 11.7% partially detected, and 5.9% were missed on a cohort of 35 subjects. With respect to automated labelling accuracy of the 32 segments, various registration strategies were investigated on a training set consisting of 10 scans. Further analysis on the test set consisting of 25 data sets indicates that 69% of the vessel centerline tree in the head and neck region, 80% in the thorax and abdomen region, and 84% in the legs was accurately labelled to the correct vessel segment. These results indicate clinical potential of our approach in enabling fully automated and accurate analysis of the entire arterial tree. This is the first study that not only automatically extracts the WB-MRA arterial tree, but also labels the vessel tree segments.

Geodesic Atlas-Based Labeling of Anatomical Trees: Application and Evaluation on Airways Extracted From CT.

We present a fast and robust atlas-based algorithm for labeling airway trees, using geodesic distances in a geometric tree-space. Possible branch label configurations for an unlabeled airway tree are evaluated using distances to a training set of labeled airway trees. In tree-space, airway tree topology and geometry change continuously, giving a natural automatic handling of anatomical differences and noise. A hierarchical approach makes the algorithm efficient, assigning labels from the trachea and downwards. Only the airway centerline tree is used, which is relatively unaffected by pathology. The algorithm is evaluated on 80 segmented airway trees from 40 subjects at two time points, labeled by three medical experts each, testing accuracy, reproducibility and robustness in patients with chronic obstructive pulmonary disease (COPD). The accuracy of the algorithm is statistically similar to that of the experts and not significantly correlated with COPD severity. The reproducibility of the algorithm is significantly better than that of the experts, and negatively correlated with COPD severity. Evaluation of the algorithm on a longitudinal set of 8724 trees from a lung cancer screening trial shows that the algorithm can be used in large scale studies with high reproducibility, and that the negative correlation of reproducibility with COPD severity can be explained by missing branches, for instance due to segmentation problems in COPD patients. We conclude that the algorithm is robust to COPD severity given equally complete airway trees, and comparable in performance to that of experts in pulmonary medicine, emphasizing the suitability of the labeling algorithm for clinical use.

Comparing Performance of Centerline Algorithms for Quantitative Assessment of Brain Vascular Anatomy

Attributes like length, diameter, and tortuosity of tubular anatomical structures such as blood vessels in medical images can be measured from centerlines. This study develops methods for comparing the accuracy and stability of centerline algorithms. Sample data included numeric phantoms simulating arteries and clinical human brain artery images. Centerlines were calculated from segmented phantoms and arteries with shortest paths centerline algorithms developed with different cost functions. The cost functions were the inverse modified distance from edge (MDFE(i) ), the center of mass (COM), the binary-thinned (BT)-MDFE(i) , and the BT-COM. The accuracy of the centerline algorithms were measured by the root mean square error from known centerlines of phantoms. The stability of the centerlines was measured by starting the centerline tree from different points and measuring the differences between trees. The accuracy and stability of the centerlines were visualized by overlaying centerlines on vasculature images. The BT-COM cost function centerline was the most stable in numeric phantoms and human brain arteries. The MDFE(i) -based centerline was most accurate in the numeric phantoms. The COM-based centerline correctly handled the "kissing" artery in 16 of 16 arteries in eight subjects whereas the BT-COM was correct in 10 of 16 and MDFE(i) was correct in 6 of 16. The COM-based centerline algorithm was selected for future use based on the ability to handle arteries where the initial binary vessels segmentation exhibits closed loops. The selected COM centerline was found to measure numerical phantoms to within 2% of the known length.

Automatic Coronary Tree Modeling

In this paper, we present an automatic method for extracting center axis representations (centerlines) of coronary arteries in contrast enhanced (CE)-CT angiography scans. The algorithm first detects the aorta which is used as an initial mask for ostia detection~cite{grady2006:fast}. Second, the ostia locations are detected via a vessel centerline extraction method~cite{Gulsun:Tek:008} which tracks the center axis of the coronaries starting from the aorta surface. The full centerline tree of the coronary arteries are computed via the multi-scale medialness-based vessel tree extraction algorithm which starts a tracking process from the ostia locations until all the braches are reached. The centerline extraction algorithm is a graph-based optimization algorithm using multi-scale medialness filters.