Automatic Segmentation of Coronary Arteries in CT Imaging in the Presence of Kissing Vessel Artifacts

In this paper, we present a novel two-step algorithm for segmentation of coronary arteries in computed tomography images based on the framework of active contours. In the proposed method, both global and local intensity information is utilized in the energy calculation. The global term is defined as a normalized cumulative distribution function, which contributes to the overall active contour energy in an adaptive fashion based on image histograms, to deform the active contour away from local stationary points. Possible outliers, such as kissing vessel artifacts, are removed in the postprocessing stage by a slice-by-slice correction scheme based on multiregion competition, where both arteries and kissing vessels are identified and tracked through the slices. The efficiency and the accuracy of the proposed technique are demonstrated on both synthetic and real datasets. The results on clinical datasets show that the method is able to extract the major branches of arteries with an average distance of 0.73 voxels to the manually delineated ground truth data. In the presence of kissing vessel artifacts, the outer surface of the entire coronary tree, extracted by the proposed algorithm, is smooth and contains fewer erroneous regions, originating in kissing vessel artifacts, as compared to the initial segmentation

Contrastive Registration for Unsupervised Medical Image Segmentation

Medical image segmentation is a relevant task as it serves as the first step for several diagnosis processes, thus it is indispensable in clinical usage. Whilst major success has been reported using supervised techniques, they assume a large and well-representative labelled set. This is a strong assumption in the medical domain where annotations are expensive, time-consuming, and inherent to human bias. To address this problem, unsupervised techniques have been proposed in the literature yet it is still an open problem due to the difficulty of learning any transformation pattern. In this work, we present a novel optimisation model framed into a new CNN-based contrastive registration architecture for unsupervised medical image segmentation. The core of our approach is to exploit image-level registration and feature-level from a contrastive learning mechanism, to perform registration-based segmentation. Firstly, we propose an architecture to capture the image-to-image transformation pattern via registration for unsupervised medical image segmentation. Secondly, we embed a contrastive learning mechanism into the registration architecture to enhance the discriminating capacity of the network in the feature-level. We show that our proposed technique mitigates the major drawbacks of existing unsupervised techniques. We demonstrate, through numerical and visual experiments, that our technique substantially outperforms the current state-of-the-art unsupervised segmentation methods on two major medical image datasets.Comment: 11 pages, 3 figure

Automatic Segmentation of Coronary Arteries Using Bayesian Driven Implicit Surfaces

©2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or distribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.Presented at the 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, April 12-15, 2007, Crystal Gateway Marriott, Arlington, Virginia, USA.DOI: 10.1109/ISBI.2007.356820In this paper, we propose a hybrid approach for the automatic three-dimensional segmentation of coronary arteries using multi-scale vessel filtering and a Bayesian probabilistic approach in a level set image segmentation framework. The initial surface of the coronaries is obtained from the multiscale vessel filter response, and the surface then evolves to capture the exact boundary of the coronaries according to an improved evolution model of implicit surfaces. In our model, the image force and the propagation terms are re-defined using posterior probabilities obtained via Bayes’ rule in order for the surface to approach to the boundaries faster and stop at the boundaries more accurately. The proposed method is tested on seven CT angiography (CTA) data-sets of left and right coronary arteries, and the quantitative comparison of our result against manually delineated contours on two of the data-sets yields a mean error of 0.37mm

직접 볼륨 렌더링의 전이 함수 설계에 관한 연구

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2017. 2. 신영길.Although direct volume rendering (DVR) has become a commodity, the design of transfer functions still a challenge. Transfer functions which map data values to optical properties (i.e., colors and opacities) highlight features of interests as well as hide unimportant regions, dramatically impacting on the quality of the visualization. Therefore, for the effective rendering of interesting features, the design of transfer functions is very important and challenging task. Furthermore, manipulation of these transfer functions is tedious and time-consuming task. In this paper, we propose a 3D spatial field for accurately identifying and visually distinguishing interesting features as well as a mechanism for data exploration using multi-dimensional transfer function. First, we introduce a 3D spatial field for the effective visualization of constricted tubular structures, called as a stenosis map which stores the degree of constriction at each voxel. Constrictions within tubular structures are quantified by using newly proposed measures (i.e., line similarity measure and constriction measure) based on the localized structure analysis, and classified with a proposed transfer function mapping the degree of constriction to color and opacity. We show the application results of our method to the visualization of coronary artery stenoses. We present performance evaluations using twenty-eight clinical datasets, demonstrating high accuracy and efficacy of our proposed method. Second, we propose a new multi-dimensional transfer function which incorporates texture features calculated from statistically homogeneous regions. This approach employs parallel coordinates to provide an intuitive interface for exploring a new multi-dimensional transfer function space. Three specific ways to use a new transfer function based on parallel coordinates enables the effective exploration of large and complex datasets. We present a mechanism for data exploration with a new transfer function space, demonstrating the practical efficacy of our proposed method. Through a study on transfer function design for DVR, we propose two useful approaches. First method to saliently visualize the constrictions within tubular structures and interactively adjust the visual appearance of the constrictions delivers a substantial aid in radiologic practice. Furthermore, second method to classify objects with our intuitive interface utilizing parallel coordinates proves to be a powerful tool for complex data exploration.Chapter 1 Introduction 1 1.1 Background 1 1.1.1 Volume rendering 1 1.1.2 Computer-aided diagnosis 3 1.1.3 Parallel coordinates 5 1.2 Problem statement 8 1.3 Main contribution 12 1.4 Organization of dissertation 16 Chapter 2 Related Work 17 2.1 Transfer function 17 2.1.1 Transfer functions based on spatial characteristics 17 2.1.2 Opacity modulation techniques 20 2.1.3 Multi-dimensional transfer functions 22 2.1.4 Manipulation mechanism for transfer functions 25 2.2 Coronary artery stenosis 28 2.3 Parallel coordinates 32 Chapter 3 Volume Visualization of Constricted Tubular Structures 36 3.1 Overview 36 3.2 Localized structure analysis 37 3.3 Stenosis map 39 3.3.1 Overview 39 3.3.2 Detection of tubular structures 40 3.3.3 Stenosis map computation 49 3.4 Stenosis-based classification 52 3.4.1 Overview 52 3.4.2 Constriction-encoded volume rendering 52 3.4.3 Opacity modulation based on constriction 54 3.5 GPU implementation 57 3.6 Experimental results 59 3.6.1 Clinical data preparation 59 3.6.2 Qualitative evaluation 60 3.6.3 Quantitative evaluation 63 3.6.4 Comparison with previous methods 66 3.6.5 Parameter study 69 Chapter 4 Interactive Multi-Dimensional Transfer Function Using Adaptive Block Based Feature Analysis 73 4.1 Overview 73 4.2 Extraction of statistical features 74 4.3 Extraction of texture features 78 4.4 Multi-dimensional transfer function design using parallel coordinates 81 4.5 Experimental results 86 Chapter 5 Conclusion 90 Bibliography 92 초 록 107Docto

Blood Vessel Segmentation and shape analysis for quantification of Coronary Artery Stenosis in CT Angiography

This thesis presents an automated framework for quantitative vascular shape analysis of the coronary arteries, which constitutes an important and fundamental component of an automated image-based diagnostic system. Firstly, an automated vessel segmentation algorithm is developed to extract the coronary arteries based on the framework of active contours. Both global and local intensity statistics are utilised in the energy functional calculation, which allows for dealing with non-uniform brightness conditions, while evolving the contour towards to the desired boundaries without being trapped in local minima. To suppress kissing vessel artifacts, a slice-by-slice correction scheme, based on multiple regions competition, is proposed to identify and track the kissing vessels throughout the transaxial images of the CTA data. Based on the resulting segmentation, we then present a dedicated algorithm to estimate the geometric parameters of the extracted arteries, with focus on vessel bifurcations. In particular, the centreline and associated reference surface of the coronary arteries, in the vicinity of arterial bifurcations, are determined by registering an elliptical cross sectional tube to the desired constituent branch. The registration problem is solved by a hybrid optimisation method, combining local greedy search and dynamic programming, which ensures the global optimality of the solution and permits the incorporation of any hard constraints posed to the tube model within a natural and direct framework. In contrast with conventional volume domain methods, this technique works directly on the mesh domain, thus alleviating the need for image upsampling. The performance of the proposed framework, in terms of efficiency and accuracy, is demonstrated on both synthetic and clinical image data. Experimental results have shown that our techniques are capable of extracting the major branches of the coronary arteries and estimating the related geometric parameters (i.e., the centreline and the reference surface) with a high degree of agreement to those obtained through manual delineation. Particularly, all of the major branches of coronary arteries are successfully detected by the proposed technique, with a voxel-wise error at 0.73 voxels to the manually delineated ground truth data. Through the application of the slice-by-slice correction scheme, the false positive metric, for those coronary segments affected by kissing vessel artifacts, reduces from 294% to 22.5%. In terms of the capability of the presented framework in defining the location of centrelines across vessel bifurcations, the mean square errors (MSE) of the resulting centreline, with respect to the ground truth data, is reduced by an average of 62.3%, when compared with initial estimation obtained using a topological thinning based algorithm

AUTOMATIC SEGMENTATION OF CORONARY ARTERIES USING BAYESIAN DRIVEN IMPLICIT SURFACES

In this paper, we propose a hybrid approach for the automatic three-dimensional segmentation of coronary arteries using multi-scale vessel filtering and a Bayesian probabilistic approach in a level set image segmentation framework. The initial surface of the coronaries is obtained from the multiscale vessel filter response, and the surface then evolves to capture the exact boundary of the coronaries according to an improved evolution model of implicit surfaces. In our model, the image force and the propagation terms are re-defined using posterior probabilities obtained via Bayes ’ rule in order for the surface to approach to the boundaries faster and stop at the boundaries more accurately. The proposed method is tested on seven CT angiography (CTA) data-sets of left and right coronary arteries, and the quantitative comparison of our result against manually delineated contours on two of the data-sets yields a mean error of 0.37mm

Blood vessel segmentation and shape analysis for quantification of coronary artery stenosis in CT angiography

This thesis presents an automated framework for quantitative vascular shape analysis of the coronary arteries, which constitutes an important and fundamental component of an automated image-based diagnostic system. Firstly, an automated vessel segmentation algorithm is developed to extract the coronary arteries based on the framework of active contours. Both global and local intensity statistics are utilised in the energy functional calculation, which allows for dealing with non-uniform brightness conditions, while evolving the contour towards to the desired boundaries without being trapped in local minima. To suppress kissing vessel artifacts, a slice-by-slice correction scheme, based on multiple regions competition, is proposed to identify and track the kissing vessels throughout the transaxial images of the CTA data. Based on the resulting segmentation, we then present a dedicated algorithm to estimate the geometric parameters of the extracted arteries, with focus on vessel bifurcations. In particular, the centreline and associated reference surface of the coronary arteries, in the vicinity of arterial bifurcations, are determined by registering an elliptical cross sectional tube to the desired constituent branch. The registration problem is solved by a hybrid optimisation method, combining local greedy search and dynamic programming, which ensures the global optimality of the solution and permits the incorporation of any hard constraints posed to the tube model within a natural and direct framework. In contrast with conventional volume domain methods, this technique works directly on the mesh domain, thus alleviating the need for image upsampling. The performance of the proposed framework, in terms of efficiency and accuracy, is demonstrated on both synthetic and clinical image data. Experimental results have shown that our techniques are capable of extracting the major branches of the coronary arteries and estimating the related geometric parameters (i.e., the centreline and the reference surface) with a high degree of agreement to those obtained through manual delineation. Particularly, all of the major branches of coronary arteries are successfully detected by the proposed technique, with a voxel-wise error at 0.73 voxels to the manually delineated ground truth data. Through the application of the slice-by-slice correction scheme, the false positive metric, for those coronary segments affected by kissing vessel artifacts, reduces from 294% to 22.5%. In terms of the capability of the presented framework in defining the location of centrelines across vessel bifurcations, the mean square errors (MSE) of the resulting centreline, with respect to the ground truth data, is reduced by an average of 62.3%, when compared with initial estimation obtained using a topological thinning based algorithm.EThOS - Electronic Theses Online ServiceGBUnited Kingdo