Segmentation techniques of brain arteriovenous malformations for 3D visualization: a systematic review

BACKGROUND Visualization, analysis and characterization of the angioarchitecture of a brain arteriovenous malformation (bAVM) present crucial steps for understanding and management of these complex lesions. Three-dimensional (3D) segmentation and 3D visualization of bAVMs play hereby a significant role. We performed a systematic review regarding currently available 3D segmentation and visualization techniques for bAVMs. METHODS PubMed, Embase and Google Scholar were searched to identify studies reporting 3D segmentation techniques applied to bAVM characterization. Category of input scan, segmentation (automatic, semiautomatic, manual), time needed for segmentation and 3D visualization techniques were noted. RESULTS Thirty-three studies were included. Thirteen (39%) used MRI as baseline imaging modality, 9 used DSA (27%), and 7 used CT (21%). Segmentation through automatic algorithms was used in 20 (61%), semiautomatic segmentation in 6 (18%), and manual segmentation in 7 (21%) studies. Median automatic segmentation time was 10 min (IQR 33), semiautomatic 25 min (IQR 73). Manual segmentation time was reported in only one study, with the mean of 5-10 min. Thirty-two (97%) studies used screens to visualize the 3D segmentations outcomes and 1 (3%) study utilized a heads-up display (HUD). Integration with mixed reality was used in 4 studies (12%). CONCLUSIONS A golden standard for 3D visualization of bAVMs does not exist. This review describes a tendency over time to base segmentation on algorithms trained with machine learning. Unsupervised fuzzy-based algorithms thereby stand out as potential preferred strategy. Continued efforts will be necessary to improve algorithms, integrate complete hemodynamic assessment and find innovative tools for tridimensional visualization

3次元光音響顕微鏡における血管画像の再構築とフィルタリング手法

Tohoku University西條芳文課

Accurate geometry reconstruction of vascular structures using implicit splines

3-D visualization of blood vessel from standard medical datasets (e.g. CT or MRI) play an important role in many clinical situations, including the diagnosis of vessel stenosis, virtual angioscopy, vascular surgery planning and computer aided vascular surgery. However, unlike other human organs, the vasculature system is a very complex network of vessel, which makes it a very challenging task to perform its 3-D visualization. Conventional techniques of medical volume data visualization are in general not well-suited for the above-mentioned tasks. This problem can be solved by reconstructing vascular geometry. Although various methods have been proposed for reconstructing vascular structures, most of these approaches are model-based, and are usually too ideal to correctly represent the actual variation presented by the cross-sections of a vascular structure. In addition, the underlying shape is usually expressed as polygonal meshes or in parametric forms, which is very inconvenient for implementing ramification of branching. As a result, the reconstructed geometries are not suitable for computer aided diagnosis and computer guided minimally invasive vascular surgery. In this research, we develop a set of techniques associated with the geometry reconstruction of vasculatures, including segmentation, modelling, reconstruction, exploration and rendering of vascular structures. The reconstructed geometry can not only help to greatly enhance the visual quality of 3-D vascular structures, but also provide an actual geometric representation of vasculatures, which can provide various benefits. The key findings of this research are as follows: 1. A localized hybrid level-set method of segmentation has been developed to extract the vascular structures from 3-D medical datasets. 2. A skeleton-based implicit modelling technique has been proposed and applied to the reconstruction of vasculatures, which can achieve an accurate geometric reconstruction of the vascular structures as implicit surfaces in an analytical form. 3. An accelerating technique using modern GPU (Graphics Processing Unit) is devised and applied to rendering the implicitly represented vasculatures. 4. The implicitly modelled vasculature is investigated for the application of virtual angioscopy

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

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

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. (C) 2008 Elsevier B.V. All rights reserved.NIH[R01HL065662]NIHNIH[R01EB006494]NIHAmerican Surgical Association Research Fellowship AwardAmerican Surgical Association Research Fellowship Awar

Differential geometry methods for biomedical image processing : from segmentation to 2D/3D registration

This thesis establishes a biomedical image analysis framework for the advanced visualization of biological structures. It consists of two important parts: 1) the segmentation of some structures of interest in 3D medical scans, and 2) the registration of patient-specific 3D models with 2D interventional images. Segmenting biological structures results in 3D computational models that are simple to visualize and that can be analyzed quantitatively. Registering a 3D model with interventional images permits to position the 3D model within the physical world. By combining the information from a 3D model and 2D interventional images, the proposed framework can improve the guidance of surgical intervention by reducing the ambiguities inherent to the interpretation of 2D images. Two specific segmentation problems are considered: 1) the segmentation of large structures with low frequency intensity nonuniformity, and 2) the detection of fine curvilinear structures. First, we directed our attention toward the segmentation of relatively large structures with low frequency intensity nonuniformity. Such structures are important in medical imaging since they are commonly encountered in MRI. Also, the nonuniform diffusion of the contrast agent in some other modalities, such as CTA, leads to structures of nonuniform appearance. A level-set method that uses a local-linear region model is defined, and applied to the challenging problem of segmenting brain tissues in MRI. The unique characteristics of the proposed method permit to account for important image nonuniformity implicitly. To the best of our knowledge, this is the first time a region-based level-set model has been used to perform the segmentation of real world MRI brain scans with convincing results. The second segmentation problem considered is the detection of fine curvilinear structures in 3D medical images. Detecting those structures is crucial since they can represent veins, arteries, bronchi or other important tissues. Unfortunately, most currently available curvilinear structure detection filters incur significant signal lost at bifurcations of two structures. This peculiarity limits the performance of all subsequent processes, whether it be understanding an angiography acquisition, computing an accurate tractography, or automatically classifying the image voxels. This thesis presents a new curvilinear structure detection filter that is robust to the presence of X- and Y-junctions. At the same time, it is conceptually simple and deterministic, and allows for an intuitive representation of the structure’s principal directions. Once a 3D computational model is available, it can be used to enhance surgical guidance. A 2D/3D non-rigid method is proposed that brings a 3D centerline model of the coronary arteries into correspondence with bi-plane fluoroscopic angiograms. The registered model is overlaid on top of the interventional angiograms to provide surgical assistance during image-guided chronic total occlusion procedures, which reduces the uncertainty inherent in 2D interventional images. A fully non-rigid registration model is proposed and used to compensate for any local shape discrepancy. This method is based on a variational framework, and uses a simultaneous matching and reconstruction process. With a typical run time of less than 3 seconds, the algorithms are fast enough for interactive applications