Deep Bleeder Acoustic Coagulation (DBAC)-part II: in vivo testing of a research prototype system.

Background: Deep Bleeder Acoustic Coagulation (DBAC) is an ultrasound image-guided high-intensity focused ultrasound (HIFU) method proposed to automatically detect and localize (D&L) and treat deep, bleeding, combat wounds in the limbs of soldiers. A prototype DBAC system consisting of an applicator and control unit was developed for testing on animals. To enhance control, and thus safety, of the ultimate human DBAC autonomous product system, a thermal coagulation strategy that minimized cavitation, boiling, and non-linear behaviors was used.

Deep bleeder acoustic coagulation (DBAC)-Part I: development and in vitro testing of a research prototype cuff system.

Background: Bleeding from limb injuries is a leading cause of death on the battlefield, with deep wounds being least accessible. High-intensity focused ultrasound (HIFU) has been shown capable of coagulation of bleeding (cautery). This paper describes the development and refereed in vitro evaluation of an ultrasound (US) research prototype deep bleeder acoustic coagulation (DBAC) cuff system for evaluating the potential of DBAC in the battlefield.

CTA coronary labeling through efficient geodesics between trees using anatomy priors.

We present an efficient realization of recent work on unique geodesic paths between tree shapes for the application of matching coronary arteries to a standard model of coronary anatomy in order to label the coronary arteries. Automatically labeled coronary arteries would speed reporting for physicians. The efficiency of the approach and the quality of the results are enhanced using the relative position of detected cardiac structures.

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.

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.

Robust and fast contrast inflow detection for 2D X-ray fluoroscopy.

2D X-ray fluoroscopy is widely used in computer assisted and image guided interventions because of the real time visual guidance it can provide to the physicians. During cardiac interventions, acquisitions of angiography are often used to assist the physician in visualizing the blood vessel structures, guide wires, or catheters, localizing bifurcations, estimating severity of a lesion, or observing the blood flow. Computational algorithms often need to process differently to frames with or without contrast medium.

A review of 3D vessel lumen segmentation techniques: models, features and extraction schemes.

Vascular diseases are among the most important public health problems in developed countries. Given the size and complexity of modern angiographic acquisitions, segmentation is a key step toward the accurate visualization, diagnosis and quantification of vascular pathologies. Despite the tremendous amount of past and on-going dedicated research, vascular segmentation remains a challenging task.

Organ segmentation with level sets using local shape and appearance priors.

Organ segmentation is a challenging problem on which recent progress has been made by incorporation of local image statistics that model the heterogeneity of structures outside of an organ of interest. However, most of these methods rely on landmark based segmentation, which has certain drawbacks. We propose to perform organ segmentation with a novel level set algorithm that incorporates local statistics via a highly efficient point tracking mechanism.

Bayesian maximal paths for coronary artery segmentation from 3D CT angiograms.

We propose a recursive Bayesian model for the delineation of coronary arteries from 3D CT angiograms (cardiac CTA) and discuss the use of discrete minimal path techniques as an efficient optimization scheme for the propagation of model realizations on a discrete graph. Design issues such as the definition of a suitable accumulative metric are analyzed in the context of our probabilistic formulation. Our approach jointly optimizes the vascular centerline and associated radius on a 4D space+scale graph.

Soft level set coupling for LV segmentation in gated perfusion SPECT.

We present a new segmentation approach for the myocardium in gated and non-gated perfusion SPECT images. To this end, we represent the epi- and endocardium by separate signed distance functions and couple them by a soft constraint to give explicit control over the wall thickness. By an explicit modeling of the basal plane, the volume of the blood pool as well as the myocardium are determinable.

Liver segmentation using sparse 3D prior models with optimal data support.

Volume segmentation is a relatively slow process and, in certain circumstances, the enormous amount of prior knowledge available is underused. Model-based liver segmentation suffers from the large shape variability of this organ, and from structures of similar appearance that juxtapose the liver. The technique presented in this paper is devoted to combine a statistical analysis of the data with a reconstruction model from sparse information: only the most reliable information in the image is used, and the rest of the liver's shape is inferred from the model and the sparse observation.

4D shape priors for a level set segmentation of the left myocardium in SPECT sequences.

We develop a 4D (3D plus time) statistical shape model for implicit level set based shape representations. To this end, we represent hand segmented training sequences of the left ventricle by respective 4-dimensional embedding functions and approximate these by a principal component analysis. In contrast to recent 4D models on explicit shape representations, the implicit shape model developed in this work does not require the computation of point correspondences which is known to be quite challenging, especially in higher dimensions.

An energy minimization approach to the data driven editing of presegmented images/volumes.

Fully automatic, completely reliable segmentation in medical images is an unrealistic expectation with today's technology. However, many automatic segmentation algorithms may achieve a near-correct solution, incorrect only in a small region. For these situations, an interactive editing tool is required, ideally in 3D, that is usually left to a manual correction.