Vessel tractography using an intensity based tensor model

In this paper, we propose a novel tubular structure segmen- tation method, which is based on an intensity-based tensor that fits to a vessel. Our model is initialized with a single seed point and it is ca- pable of capturing whole vessel tree by an automatic branch detection algorithm. The centerline of the vessel as well as its thickness is extracted. We demonstrated the performance of our algorithm on 3 complex contrast varying tubular structured synthetic datasets for quantitative validation. Additionally, extracted arteries from 10 CTA (Computed Tomography An- giography) volumes are qualitatively evaluated by a cardiologist expert’s visual scores

Automatic centerline extraction of coronary arteries in coronary computed tomographic angiography

Coronary computed tomographic angiography (CCTA) is a non-invasive imaging modality for the visualization of the heart and coronary arteries. To fully exploit the potential of the CCTA datasets and apply it in clinical practice, an automated coronary artery extraction approach is needed. The purpose of this paper is to present and validate a fully automatic centerline extraction algorithm for coronary arteries in CCTA images. The algorithm is based on an improved version of Frangi’s vesselness filter which removes unwanted step-edge responses at the boundaries of the cardiac chambers. Building upon this new vesselness filter, the coronary artery extraction pipeline extracts the centerlines of main branches as well as side-branches automatically. This algorithm was first evaluated with a standardized evaluation framework named Rotterdam Coronary Artery Algorithm Evaluation Framework used in the MICCAI Coronary Artery Tracking challenge 2008 (CAT08). It includes 128 reference centerlines which were manually delineated. The average overlap and accuracy measures of our method were 93.7% and 0.30 mm, respectively, which ranked at the 1st and 3rd place compared to five other automatic methods presented in the CAT08. Secondly, in 50 clinical datasets, a total of 100 reference centerlines were generated from lumen contours in the transversal planes which were manually corrected by an expert from the cardiology department. In this evaluation, the average overlap and accuracy were 96.1% and 0.33 mm, respectively. The entire processing time for one dataset is less than 2 min on a standard desktop computer. In conclusion, our newly developed automatic approach can extract coronary arteries in CCTA images with excellent performances in extraction ability and accuracy

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

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