944 research outputs found
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
Trainable COSFIRE filters for vessel delineation with application to retinal images
Retinal imaging provides a non-invasive opportunity for the diagnosis of several medical pathologies. The automatic segmentation of the vessel tree is an important pre-processing step which facilitates subsequent automatic processes that contribute to such diagnosis. We introduce a novel method for the automatic segmentation of vessel trees in retinal fundus images. We propose a filter that selectively responds to vessels and that we call B-COSFIRE with B standing for bar which is an abstraction for a vessel. It is based on the existing COSFIRE (Combination Of Shifted Filter Responses) approach. A B-COSFIRE filter achieves orientation selectivity by computing the weighted geometric mean of the output of a pool of Difference-of-Gaussians filters, whose supports are aligned in a collinear manner. It achieves rotation invariance efficiently by simple shifting operations. The proposed filter is versatile as its selectivity is determined from any given vessel-like prototype pattern in an automatic configuration process. We configure two B-COSFIRE filters, namely symmetric and asymmetric, that are selective for bars and bar-endings, respectively. We achieve vessel segmentation by summing up the responses of the two rotation-invariant B-COSFIRE filters followed by thresholding. The results that we achieve on three publicly available data sets (DRIVE: Se = 0.7655, Sp = 0.9704; STARE: Se = 0.7716, Sp = 0.9701; CHASE_DB1: Se = 0.7585, Sp = 0.9587) are higher than many of the state-of-the-art methods. The proposed segmentation approach is also very efficient with a time complexity that is significantly lower than existing methods.peer-reviewe
UV Exposed Optical Fibers with Frequency Domain Reflectometry for Device Tracking in Intra-Arterial Procedures
Shape tracking of medical devices using strain sensing properties in optical
fibers has seen increased attention in recent years. In this paper, we propose
a novel guidance system for intra-arterial procedures using a distributed
strain sensing device based on optical frequency domain reflectometry (OFDR) to
track the shape of a catheter. Tracking enhancement is provided by exposing a
fiber triplet to a focused ultraviolet beam, producing high scattering
properties. Contrary to typical quasi-distributed strain sensors, we propose a
truly distributed strain sensing approach, which allows to reconstruct a fiber
triplet in real-time. A 3D roadmap of the hepatic anatomy integrated with a 4D
MR imaging sequence allows to navigate the catheter within the
pre-interventional anatomy, and map the blood flow velocities in the arterial
tree. We employed Riemannian anisotropic heat kernels to map the sensed data to
the pre-interventional model. Experiments in synthetic phantoms and an in vivo
model are presented. Results show that the tracking accuracy is suitable for
interventional tracking applications, with a mean 3D shape reconstruction
errors of 1.6 +/- 0.3 mm. This study demonstrates the promising potential of
MR-compatible UV-exposed OFDR optical fibers for non-ionizing device guidance
in intra-arterial procedures
Extracting Tree-structures in CT data by Tracking Multiple Statistically Ranked Hypotheses
In this work, we adapt a method based on multiple hypothesis tracking (MHT)
that has been shown to give state-of-the-art vessel segmentation results in
interactive settings, for the purpose of extracting trees. Regularly spaced
tubular templates are fit to image data forming local hypotheses. These local
hypotheses are used to construct the MHT tree, which is then traversed to make
segmentation decisions. However, some critical parameters in this method are
scale-dependent and have an adverse effect when tracking structures of varying
dimensions. We propose to use statistical ranking of local hypotheses in
constructing the MHT tree, which yields a probabilistic interpretation of
scores across scales and helps alleviate the scale-dependence of MHT
parameters. This enables our method to track trees starting from a single seed
point. Our method is evaluated on chest CT data to extract airway trees and
coronary arteries. In both cases, we show that our method performs
significantly better than the original MHT method.Comment: Accepted for publication at the International Journal of Medical
Physics and Practic
Automatic segmentation of coronary angiograms based on fuzzy inferring and probabilistic tracking
<p>Abstract</p> <p>Background</p> <p>Segmentation of the coronary angiogram is important in computer-assisted artery motion analysis or reconstruction of 3D vascular structures from a single-plan or biplane angiographic system. Developing fully automated and accurate vessel segmentation algorithms is highly challenging, especially when extracting vascular structures with large variations in image intensities and noise, as well as with variable cross-sections or vascular lesions.</p> <p>Methods</p> <p>This paper presents a novel tracking method for automatic segmentation of the coronary artery tree in X-ray angiographic images, based on probabilistic vessel tracking and fuzzy structure pattern inferring. The method is composed of two main steps: preprocessing and tracking. In preprocessing, multiscale Gabor filtering and Hessian matrix analysis were used to enhance and extract vessel features from the original angiographic image, leading to a vessel feature map as well as a vessel direction map. In tracking, a seed point was first automatically detected by analyzing the vessel feature map. Subsequently, two operators [e.g., a probabilistic tracking operator (PTO) and a vessel structure pattern detector (SPD)] worked together based on the detected seed point to extract vessel segments or branches one at a time. The local structure pattern was inferred by a multi-feature based fuzzy inferring function employed in the SPD. The identified structure pattern, such as crossing or bifurcation, was used to control the tracking process, for example, to keep tracking the current segment or start tracking a new one, depending on the detected pattern.</p> <p>Results</p> <p>By appropriate integration of these advanced preprocessing and tracking steps, our tracking algorithm is able to extract both vessel axis lines and edge points, as well as measure the arterial diameters in various complicated cases. For example, it can walk across gaps along the longitudinal vessel direction, manage varying vessel curvatures, and adapt to varying vessel widths in situations with arterial stenoses and aneurysms.</p> <p>Conclusions</p> <p>Our algorithm performs well in terms of robustness, automation, adaptability, and applicability. In particular, the successful development of two novel operators, namely, PTO and SPD, ensures the performance of our algorithm in vessel tracking.</p
Tracking and diameter estimation of retinal vessels using Gaussian process and Radon transform
Extraction of blood vessels in retinal images is an important step for computer-aided diagnosis of
ophthalmic pathologies. We propose an approach for blood vessel tracking and diameter estimation. We hypothesize
that the curvature and the diameter of blood vessels are Gaussian processes (GPs). Local Radon transform,
which is robust against noise, is subsequently used to compute the features and train the GPs. By learning
the kernelized covariance matrix from training data, vessel direction and its diameter are estimated. In order to
detect bifurcations, multiple GPs are used and the difference between their corresponding predicted directions is
quantified. The combination of Radon features and GP results in a good performance in the presence of noise.
The proposed method successfully deals with typically difficult cases such as bifurcations and central arterial
reflex, and also tracks thin vessels with high accuracy. Experiments are conducted on the publicly available
DRIVE, STARE, CHASEDB1, and high-resolution fundus databases evaluating sensitivity, specificity, and
Matthew’s correlation coefficient (MCC). Experimental results on these datasets show that the proposed method
reaches an average sensitivity of 75.67%, specificity of 97.46%, and MCC of 72.18% which is comparable to the
state-of-the-art
Fitting tree model with CNN and geodesics to track vesselsand application to Ultrasound Localization Microscopy data
Segmentation of tubular structures in vascular imaging is a well studied
task, although it is rare that we try to infuse knowledge of the tree-like
structure of the regions to be detected. Our work focuses on detecting the
important landmarks in the vascular network (via CNN performing both
localization and classification of the points of interest) and representing
vessels as the edges in some minimal distance tree graph. We leverage geodesic
methods relevant to the detection of vessels and their geometry, making use of
the space of positions and orientations so that 2D vessels can be accurately
represented as trees. We build our model to carry tracking on Ultrasound
Localization Microscopy (ULM) data, proposing to build a good cost function for
tracking on this type of data. We also test our framework on synthetic and eye
fundus data. Results show that scarcity of well annotated ULM data is an
obstacle to localization of vascular landmarks but the Orientation Score built
from ULM data yields good geodesics for tracking blood vessels.Comment: This work has been submitted to the IEEE for possible publication.
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