Robust and accurate coronary artery centerline extraction in CTA by combining model-driven and data-driven approaches.

Various methods have been proposed to extract coronary artery centerlines from computed tomography angiography (CTA) data. Almost all previous approaches are data-driven, which try to trace a centerline from an automatically detected or manually specified coronary ostium. No or little high level prior information is used; therefore, the centerline tracing procedure may terminate early at a severe occlusion or an anatomically inconsistent centerline course may be generated. Though the connectivity of coronary arteries exhibits large variations, the position of major coronary arteries relative to the heart chambers is quite stable. In this work, we propose to exploit the automatically segmented chambers to 1) predict the initial position of the major coronary centerlines and 2) define a vessel-specific region-of-interest (ROI) to constrain the following centerline refinement. The proposed prior constraints have been integrated into a model-driven algorithm for the extraction of three major coronary centerlines, namely the left anterior descending artery (LAD), left circumflex artery (LCX), and right coronary artery (RCA). After extracting the major coronary arteries, the side branches are traced using a data-driven approach to handle large anatomical variations in side branches. Experiments on the public Rotterdam coronary CTA database demonstrate the robustness and accuracy of the proposed method. We achieve the best average ranking on overlap metrics among automatic methods and our accuracy metric outperforms all other 22 methods (including both automatic and semi-automatic methods).

Efficient detection of native and bypass coronary ostia in cardiac CT volumes: anatomical vs. pathological structures.

Cardiac computed tomography (CT) is the primary noninvasive imaging modality to diagnose coronary artery disease. Though various methods have been proposed for coronary artery segmentation, most rely on at least one user click to provide a seed point for initialization. Automatic detection of the coronary ostia (where coronaries originate from the aorta), including both the native coronary ostia and graft ostia of the bypass coronaries, can make the whole coronary exam workflow fully automatic, therefore increasing a physician's throughput. Anatomical structures (native coronary ostia) and pathological structures (graft ostia) often require significantly different detection methods. The native coronary ostia are well constrained by the surrounding structures, therefore are detected as a global object. Detecting the graft ostia is far more difficult due to the large variation in graft position. A new searching strategy is proposed to efficiently guide the focus of analysis and, at the same time, reduce the false positive detections. Since the bypass coronaries are grafted on the ascending aorta surface, the ascending aorta is first segmented to constrain the search. The quantitative prior distribution of the graft ostia on the aorta surface is learned from a training set to significantly reduce the searching space further. Efficient local image features are extracted around each candidate point on the aorta surface to train a detector. The proposed method is computationally efficient, taking about 0.40 seconds to detect both native and graft ostia in a volume with around 512 x 512 x 200 voxels.

Standardized evaluation methodology and reference database for evaluating coronary artery centerline extraction algorithms.

Efficiently obtaining a reliable coronary artery centerline from computed tomography angiography data is relevant in clinical practice. Whereas numerous methods have been presented for this purpose, up to now no standardized evaluation methodology has been published to reliably evaluate and compare the performance of the existing or newly developed coronary artery centerline extraction algorithms. This paper describes a standardized evaluation methodology and reference database for the quantitative evaluation of coronary artery centerline extraction algorithms. The contribution of this work is fourfold: (1) a method is described to create a consensus centerline with multiple observers, (2) well-defined measures are presented for the evaluation of coronary artery centerline extraction algorithms, (3) a database containing 32 cardiac CTA datasets with corresponding reference standard is described and made available, and (4) 13 coronary artery centerline extraction algorithms, implemented by different research groups, are quantitatively evaluated and compared. The presented evaluation framework is made available to the medical imaging community for benchmarking existing or newly developed coronary centerline extraction algorithms.

Robust vessel tree modeling.

In this paper, we present a novel method for extracting center axis representations (centerlines) of blood vessels in contrast enhanced (CE)-CTA/MRA, robustly and accurately. This graph-based optimization algorithm which employs multi-scale medialness filters extracts vessel centerlines by computing the minimum-cost paths. Specifically, first, new medialness filters are designed from the assumption of circular/elliptic vessel cross-sections. These filters produce contrast and scale independent responses even the presence of nearby structures. Second, they are incorporated to the minimum-cost path detection algorithm in a novel way for the computational efficiency and accuracy. Third, the full vessel centerline tree is constructed from this optimization technique by assigning a saliency measure for each centerline from their length and radius information. The proposed method is computationally efficient and produces results that are comparable in quality to the ones created by experts. It has been tested on more than 100 coronary artery data set where the full coronary artery trees are extracted in 21 seconds in average on a 3.2 GHz PC.

Segmentation of carpal bones from CT images using skeletally coupled deformable models.

The in vivo investigation of joint kinematics in normal and injured wrist requires the segmentation of carpal bones from 3D (CT) images, and their registration over time. The non-uniformity of bone tissue, ranging from dense cortical bone to textured spongy bone, the irregular shape of closely packed carpal bones, small inter-bone spaces compared to the resolution of CT images, along with the presence of blood vessels, and the inherent blurring of CT imaging render the segmentation of carpal bones a challenging task. We review the performance of statistical classification, deformable models (active contours), region growing, region competition, and morphological operations for this application. We then propose a model which combines several of these approaches in a unified framework. Specifically, our approach is to use a curve evolution implementation of region growing from initialized seeds, where growth is modulated by a skeletally-mediated competition between neighboring regions. The inter-seed skeleton, which we interpret as the predicted boundary of collision between two regions, is used to couple the growth of seeds and to mediate long-range competition between them. The implementation requires subpixel representations of each growing region as well as the inter-region skeleton. This method combines the advantages of active contour models, region growing, and both local and global region competition methods. We demonstrate the effectiveness of this approach for our application where many of the difficulties presented above are overcome as illustrated by synthetic and real examples. Since this segmentation method does not rely on domain-specific knowledge, it should be applicable to a range of other medical imaging segmentation tasks.