We present a method to track vessels in angiography [contrast filled vessels in two-dimensional (2-D) x-ray fluoroscopy]. Finding correspondence of a vessel tree from consecutive angiogram frames provides significant value in computer-aided clinical applications such as fast vessel tree segmentation, three-dimensional (3-D) vessel topology reconstruction from corresponding centerlines, cardiac motion understanding, etc. However, establishing an accurate vessel tree correspondence (vessel tree tracking) is a nontrivial problem due to nonlinear periodic cardiac and breathing motion in 2-D views, foreshortening, false bifurcations due to 3-D to 2-D projection, occlusion from other anatomies, etc. The vessel tree is represented by BSpline curves. The control points of the BSpline curves are landmarks that are the tracking targets. Our method maximizes the appearance similarity while preserving the vessel structure. A directed acyclic graph (DAG) is employed to represent the appearance and shape structure of the vessel tree: nodes from the DAG encode the appearance of the vessel tree landmarks, and the edges encode the relative locations between landmarks. The vessel tree tracking problem turns into finding the most similar tree from the DAG in the next frame, and it is solved using an efficient dynamic programming algorithm. We performed evaluations on 62 x-ray angiography sequences (above 1000 frames). Experiment results show our algorithm is robust to these challenges and delivers better performance, compared to four existing methods.
Both traditional and wireless capsule endoscopes can generate tens of thousands of images for each patient. It is desirable to have the majority of irrelevant images filtered out by automatic algorithms during an offline review process or to have automatic indication for highly suspicious areas during an online guidance. This also applies to the newly invented endomicroscopy, where online indication of tumor classification plays a significant role. Image classification is a standard pattern recognition problem and is well studied in the literature. However, performance on the challenging endoscopic images still has room for improvement. In this paper, we present a novel Cascaded Deep Decision Network (CDDN) to improve image classification performance over standard Deep neural network based methods. During the learning phase, CDDN automatically builds a network which discards samples that are classified with high confidence scores by a previously trained network and concentrates only on the challenging samples which would be handled by the subsequent expert shallow networks. We validate CDDN using two different types of endoscopic imaging, which includes a polyp classification dataset and a tumor classification dataset. From both datasets we show that CDDN can outperform other methods by about 10%. In addition, CDDN can also be applied to other image classification problems.
Catheter tracking in X-ray fluoroscopic images has become more important in interventional applications
for atrial fibrillation (AF) ablation procedures. It provides real-time guidance for the physicians and can
be used as reference for motion compensation applications. In this paper, we propose a novel approach to
track a virtual electrode (VE), which is a non-existing electrode on the coronary sinus (CS) catheter at a
more proximal location than any real electrodes. Successful tracking of the VE can provide more accurate
motion information than tracking of real electrodes. To achieve VE tracking, we first model the CS catheter
as a set of electrodes which are detected by our previously published learning-based approach.1 The tracked
electrodes are then used to generate the hypotheses for tracking the VE. Model-based hypotheses are fused
and evaluated by a Bayesian framework. Evaluation has been conducted on a database of clinical AF
ablation data including challenging scenarios such as low signal-to-noise ratio (SNR), occlusion and nonrigid
deformation. Our approach obtains 0.54mm median error and 90% of evaluated data have errors
less than 1.67mm. The speed of our tracking algorithm reaches 6 frames-per-second on most data. Our
study on motion compensation shows that using the VE as reference provides a good point to detect
non-physiological catheter motion during the AF ablation procedures.2
Electrophysiology (EP) procedures are conducted by cardiac specialists to help diagnose and treat abnormal heart
rhythms. Such procedures are conducted under mono-plane and bi-plane x-ray fluoroscopy guidance to allow the
specialist to target ablation points within the heart. Ablations lesions are usually set by applying radio-frequency energy
to endocardial tissue using catheters placed inside a patient's heart. Recently we have developed a system capable of
overlaying information involving the heart and targeted ablation locations from pre-operational image data for additional
assistance. Although useful, such information offers only approximate guidance due to heart beat and breathing motion.
As a solution to this problem, we propose to make use of a 2D lasso catheter tracking method. We apply it to bi-plane
fluoroscopy images to dynamically update fluoro overlays. The dynamic overlays are computed at 3.5 frames per second
to offer real-time updates matching the heart motion. During the course of our experiments, we found an average 3-D
error of 1.6 mm on average. We present the workflow and features of the motion-adjusted, augmented fluoroscopy
system and demonstrate the dramatic improvement in the overlay quality provided by this approach.
In this paper we present a learning-based guidewire localization algorithm which can be constrained by user inputs. The
proposed algorithm automatically localizes guidewires in fluoroscopic images. In cases where the results are not satisfactory,
the user can provide input to constrain the algorithm by clicking on the guidewire segment missed by the detection
algorithm. The algorithm then re-localizes the guidewire and updates the result in less than 0.3 second. In extreme cases,
more constraints can be provided until a satisfactory result is reached. The proposed algorithm can not only serve as an
efficient initialization tool for guidewire tracking, it can also serve as an efficient annotation tool, either for cardiologists
to mark the guidewire, or to build up a labeled database for evaluation. Through the improvement of the initialization of
guidewire tracking, it also helps to improve the visibility of the guidewire during interventional procedures. Our study
shows that even highly complicated guidewires can mostly be localized within 5 seconds by less than 6 clicks.
Digital subtraction angiography (DSA) is a well-known technique for improving the visibility and perceptibility of
blood vessels in the human body. Coronary DSA extends conventional DSA to dynamic 2D fluoroscopic sequences
of coronary arteries which are subject to respiratory and cardiac motion. Effective motion compensation is the
main challenge for coronary DSA. Without a proper treatment, both breathing and heart motion can cause
unpleasant artifacts in coronary subtraction images, jeopardizing the clinical value of coronary DSA. In this
paper, we present an effective method to separate the dynamic layer of background structures from a fluoroscopic
sequence of the heart, leaving a clean layer of moving coronary arteries. Our method combines the techniques
of learning-based vessel detection and robust motion estimation to achieve reliable motion compensation for
coronary sequences. Encouraging results have been achieved on clinically acquired coronary sequences, where
the proposed method considerably improves the visibility and perceptibility of coronary arteries undergoing
breathing and cardiac movement. Perceptibility improvement is significant especially for very thin vessels. The
potential clinical benefit is expected in the context of obese patients and deep angulation, as well as in the
reduction of contrast dose in normal size patients.
In this paper, we present a novel hierarchical framework of guidewire tracking for image-guided interventions. Our method
can automatically and robustly track a guidewire in fluoroscopy sequences during interventional procedures. The method
consists of three main components: learning based guidewire segment detection, robust and fast rigid tracking, and nonrigid
guidewire tracking. Each component aims to handle guidewire motion at a specific level. The learning based segment
detection identifies small segments of a guidewire at the level of individual frames, and provides unique primitive features
for subsequent tracking. Based on identified guidewire segments, the rigid tracking method robustly tracks the guidewire
across successive frames, assuming that a major motion of guidewire is rigid, mainly caused by the breathing motion and
table movement. Finally, a non-rigid tracking algorithm is applied to finely deform the guidewire to provide accurate shape.
The presented guidewire tracking method has been evaluated on a test set of 47 sequences with more than 1000 frames.
Quantitative evaluation demonstrates that the mean tracking error on the guidewire body is less than 2 pixels. Therefore
the presented guidewire tracking method has a great potential for applications in image guided interventions.
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