85,229 research outputs found
Fast catheter segmentation and tracking based on x-ray fluoroscopic and echocardiographic modalities for catheter-based cardiac minimally invasive interventions
X-ray fluoroscopy and echocardiography imaging (ultrasound, US) are two imaging modalities that are widely used in cardiac catheterization. For these modalities, a fast, accurate and stable algorithm for the detection and tracking of catheters is required to allow clinicians to observe the catheter location in real-time. Currently X-ray fluoroscopy is routinely used as the standard modality in catheter ablation interventions. However, it lacks the ability to visualize soft tissue and uses harmful radiation. US does not have these limitations but often contains acoustic artifacts and has a small field of view. These make the detection and tracking of the catheter in US very challenging.
The first contribution in this thesis is a framework which combines Kalman filter and discrete optimization for multiple catheter segmentation and tracking in X-ray images. Kalman filter is used to identify the whole catheter from a single point detected on the catheter in the first frame of a sequence of x-ray images. An energy-based formulation is developed that can be used to track the catheters in the following frames. We also propose a discrete optimization for minimizing the energy function in each frame of the X-ray image sequence. Our approach is robust to tangential motion of the catheter and combines the tubular and salient feature measurements into a single robust and efficient framework.
The second contribution is an algorithm for catheter extraction in 3D ultrasound images based on (a) the registration between the X-ray and ultrasound images and (b) the segmentation of the catheter in X-ray images. The search space for the catheter extraction in the ultrasound images is constrained to lie on or close to a curved surface in the ultrasound volume. The curved surface corresponds to the back-projection of the extracted catheter from the X-ray image to the ultrasound volume. Blob-like features are detected in the US images and organized in a graphical model. The extracted catheter is modelled as the optimal path in this graphical model.
Both contributions allow the use of ultrasound imaging for the improved visualization of soft tissue. However, X-ray imaging is still required for each ultrasound frame and the amount of X-ray exposure has not been reduced. The final contribution in this thesis is a system that can track the catheter in ultrasound volumes automatically without the need for X-ray imaging during the tracking. Instead X-ray imaging is only required for the system initialization and for recovery from tracking failures. This allows a significant reduction in the amount of X-ray exposure for patient and clinicians.Open Acces
Automatic segmentation of coronary angiograms based on fuzzy inferring and probabilistic tracking
<p>Abstract</p> <p>Background</p> <p>Segmentation of the coronary angiogram is important in computer-assisted artery motion analysis or reconstruction of 3D vascular structures from a single-plan or biplane angiographic system. Developing fully automated and accurate vessel segmentation algorithms is highly challenging, especially when extracting vascular structures with large variations in image intensities and noise, as well as with variable cross-sections or vascular lesions.</p> <p>Methods</p> <p>This paper presents a novel tracking method for automatic segmentation of the coronary artery tree in X-ray angiographic images, based on probabilistic vessel tracking and fuzzy structure pattern inferring. The method is composed of two main steps: preprocessing and tracking. In preprocessing, multiscale Gabor filtering and Hessian matrix analysis were used to enhance and extract vessel features from the original angiographic image, leading to a vessel feature map as well as a vessel direction map. In tracking, a seed point was first automatically detected by analyzing the vessel feature map. Subsequently, two operators [e.g., a probabilistic tracking operator (PTO) and a vessel structure pattern detector (SPD)] worked together based on the detected seed point to extract vessel segments or branches one at a time. The local structure pattern was inferred by a multi-feature based fuzzy inferring function employed in the SPD. The identified structure pattern, such as crossing or bifurcation, was used to control the tracking process, for example, to keep tracking the current segment or start tracking a new one, depending on the detected pattern.</p> <p>Results</p> <p>By appropriate integration of these advanced preprocessing and tracking steps, our tracking algorithm is able to extract both vessel axis lines and edge points, as well as measure the arterial diameters in various complicated cases. For example, it can walk across gaps along the longitudinal vessel direction, manage varying vessel curvatures, and adapt to varying vessel widths in situations with arterial stenoses and aneurysms.</p> <p>Conclusions</p> <p>Our algorithm performs well in terms of robustness, automation, adaptability, and applicability. In particular, the successful development of two novel operators, namely, PTO and SPD, ensures the performance of our algorithm in vessel tracking.</p
Augmented Reality-based Feedback for Technician-in-the-loop C-arm Repositioning
Interventional C-arm imaging is crucial to percutaneous orthopedic procedures
as it enables the surgeon to monitor the progress of surgery on the anatomy
level. Minimally invasive interventions require repeated acquisition of X-ray
images from different anatomical views to verify tool placement. Achieving and
reproducing these views often comes at the cost of increased surgical time and
radiation dose to both patient and staff. This work proposes a marker-free
"technician-in-the-loop" Augmented Reality (AR) solution for C-arm
repositioning. The X-ray technician operating the C-arm interventionally is
equipped with a head-mounted display capable of recording desired C-arm poses
in 3D via an integrated infrared sensor. For C-arm repositioning to a
particular target view, the recorded C-arm pose is restored as a virtual object
and visualized in an AR environment, serving as a perceptual reference for the
technician. We conduct experiments in a setting simulating orthopedic trauma
surgery. Our proof-of-principle findings indicate that the proposed system can
decrease the 2.76 X-ray images required per desired view down to zero,
suggesting substantial reductions of radiation dose during C-arm repositioning.
The proposed AR solution is a first step towards facilitating communication
between the surgeon and the surgical staff, improving the quality of surgical
image acquisition, and enabling context-aware guidance for surgery rooms of the
future. The concept of technician-in-the-loop design will become relevant to
various interventions considering the expected advancements of sensing and
wearable computing in the near future
Multi-camera Realtime 3D Tracking of Multiple Flying Animals
Automated tracking of animal movement allows analyses that would not
otherwise be possible by providing great quantities of data. The additional
capability of tracking in realtime - with minimal latency - opens up the
experimental possibility of manipulating sensory feedback, thus allowing
detailed explorations of the neural basis for control of behavior. Here we
describe a new system capable of tracking the position and body orientation of
animals such as flies and birds. The system operates with less than 40 msec
latency and can track multiple animals simultaneously. To achieve these
results, a multi target tracking algorithm was developed based on the Extended
Kalman Filter and the Nearest Neighbor Standard Filter data association
algorithm. In one implementation, an eleven camera system is capable of
tracking three flies simultaneously at 60 frames per second using a gigabit
network of nine standard Intel Pentium 4 and Core 2 Duo computers. This
manuscript presents the rationale and details of the algorithms employed and
shows three implementations of the system. An experiment was performed using
the tracking system to measure the effect of visual contrast on the flight
speed of Drosophila melanogaster. At low contrasts, speed is more variable and
faster on average than at high contrasts. Thus, the system is already a useful
tool to study the neurobiology and behavior of freely flying animals. If
combined with other techniques, such as `virtual reality'-type computer
graphics or genetic manipulation, the tracking system would offer a powerful
new way to investigate the biology of flying animals.Comment: pdfTeX using libpoppler 3.141592-1.40.3-2.2 (Web2C 7.5.6), 18 pages
with 9 figure
Learning to Personalize in Appearance-Based Gaze Tracking
Personal variations severely limit the performance of appearance-based gaze
tracking. Adapting to these variations using standard neural network model
adaptation methods is difficult. The problems range from overfitting, due to
small amounts of training data, to underfitting, due to restrictive model
architectures. We tackle these problems by introducing the SPatial Adaptive
GaZe Estimator (SPAZE). By modeling personal variations as a low-dimensional
latent parameter space, SPAZE provides just enough adaptability to capture the
range of personal variations without being prone to overfitting. Calibrating
SPAZE for a new person reduces to solving a small optimization problem. SPAZE
achieves an error of 2.70 degrees with 9 calibration samples on MPIIGaze,
improving on the state-of-the-art by 14 %. We contribute to gaze tracking
research by empirically showing that personal variations are well-modeled as a
3-dimensional latent parameter space for each eye. We show that this
low-dimensionality is expected by examining model-based approaches to gaze
tracking. We also show that accurate head pose-free gaze tracking is possible
Towards an in-plane methodology to track breast lesions using mammograms and patient-specific finite-element simulations
In breast cancer screening or diagnosis, it is usual to combine different images in order to locate a lesion as accurately as possible. These images are generated using a single or several imaging techniques. As x-ray-based mammography is widely used, a breast lesion is located in the same plane of the image (mammogram), but tracking it across mammograms corresponding to different views is a challenging task for medical physicians. Accordingly, simulation tools and methodologies that use patient-specific numerical models can facilitate the task of fusing information from different images. Additionally, these tools need to be as straightforward as possible to facilitate their translation to the clinical area. This paper presents a patient-specific, finite-element-based and semi-automated simulation methodology to track breast lesions across mammograms. A realistic three-dimensional computer model of a patient''s breast was generated from magnetic resonance imaging to simulate mammographic compressions in cranio-caudal (CC, head-to-toe) and medio-lateral oblique (MLO, shoulder-to-opposite hip) directions. For each compression being simulated, a virtual mammogram was obtained and posteriorly superimposed to the corresponding real mammogram, by sharing the nipple as a common feature. Two-dimensional rigid-body transformations were applied, and the error distance measured between the centroids of the tumors previously located on each image was 3.84 mm and 2.41 mm for CC and MLO compression, respectively. Considering that the scope of this work is to conceive a methodology translatable to clinical practice, the results indicate that it could be helpful in supporting the tracking of breast lesions
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