Discrete tracking of parametrized curves

Heibel, Tim Hauke; Glocker, Ben; Groher, M.; Paragios, Nikos; Komodakis, N.; Navab, N.

doi:10.1109/cvprw.2009.5206714

Cited by 9 publications

(9 citation statements)

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“…The presented algorithm performs near real-time and proves to be both robust and accurate. The presented methods extend and improve the approach presented earlier in the work of Heibel et al (2009).…”

Section: Introductionmentioning

confidence: 58%

“…Those measurements are computed for overlapping segments of the curve and their sum represents the curve's likelihood term. The prior is ensuring length preservation and the optimization is carried out in a MAP-MRF framework as Heibel et al (2009) propose.…”

Section: Related Workmentioning

confidence: 99%

“…The prior term, also referred to as internal energy, constrains the motion of the curve. We use the same method as in our previous work (Heibel et al, 2009) where we penalize changes of the curve length compared to the initial curve. These changes are penalized via the squared relative error…”

Section: Curve Tracking In the Continuous Domainmentioning

confidence: 99%

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Interventional Tool Tracking Using Discrete Optimization

Heibel

Glocker

Groher

et al. 2013

IEEE Trans. Med. Imaging

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Abstract-This work presents a novel scheme for tracking of motion and deformation of interventional tools such as guidewires and catheters in fluoroscopic X-ray sequences. Being able to track and thus to estimate the correct positions of these tools is crucial in order to offer guidance enhancement during interventions. The task of estimating the apparent motion is particularly challenging due to the low signal to noise ratio (SNR) of fluoroscopic images and due to combined motion components originating from patient breathing and tool interactions performed by the physician.The presented approach is based on modeling interventional tools with B-splines whose optimal configuration of control points is determined through efficient discrete optimization. Each control point corresponds to a discrete random variable in a Markov random field (MRF) formulation where a set of labels represents the deformation space. In this context, the optimal curve corresponds to the maximum a posteriori (MAP) estimate of the MRF energy. The main motivation for employing a discrete approach is the possibility to incorporate a multi-directional search space which is robust to local minima. This is of particular interest for curve tracking under large deformation.This work analyzes feasibility of employing efficient first-order MRFs for tracking. In particular it shows how to achieve a good compromise between energy approximations and computational efficiency. Experimental results suggest to define both the external and internal energy in terms of pairwise potential functions. The method was successfully applied to the tracking of guide-wires in fluoroscopic X-ray sequences of several hundred frames which requires extremely robust techniques. Comparisons with state-ofthe-art guide-wire tracking algorithms confirm the effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 58%

Section: Related Workmentioning

confidence: 99%

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Interventional Tool Tracking Using Discrete Optimization

Heibel

Glocker

Groher

et al. 2013

IEEE Trans. Med. Imaging

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“…By splitting the suture into piece-wise linear segments, our poseconfigurable system can follow it very precisely. Despite no constraints at the ends of the suture, the tracker stabilized both ends correctly, which is a challenging task [15]. We posit this satisfactory behavior owes to the fact that, while some segments rotate, others only shift, and thus our hierarchical, spatio-temporal model renders the tracker stable.…”

Section: Resultsmentioning

confidence: 95%

“…Chain structures: Our proposal allows us to draw analogies to very influential snakes models of image contours [21], which can represent image structures with a chain graph [2,15]. Snakes actively adapt to previously unseen contours to delineate object segments for shape registration.…”

Section: Related Workmentioning

confidence: 99%

Pose-Configurable Generic Tracking of Elongated Objects

Węsierski

Horain

2013

2013 IEEE International Conference on Computer Vision

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Elongated objects have various shapes and can shift, rotate, change scale, and be rigid or deform by flexing, articulating, and vibrating, with examples as varied as a glass bottle, a robotic arm, a surgical suture, a finger pair, a tram, and a guitar string. This generally makes tracking of poses of elongated objects very challenging.We describe a unified, configurable framework for tracking the pose of elongated objects, which move in the image plane and extend over the image region. Our method strives for simplicity, versatility, and efficiency. The object is decomposed into a chained assembly of segments of multiple parts that are arranged under a hierarchy of tailored spatio-temporal constraints. In this hierarchy, segments can rescale independently while their elasticity is controlled with global orientations and local distances.While the trend in tracking is to design complex, structure-free algorithms that update object appearance online, we show that our tracker, with the novel but remarkably simple, structured organization of parts with constant appearance, reaches or improves state-of-the-art performance. Most importantly, our model can be easily configured to track exact pose of arbitrary, elongated objects in the image plane. The tracker can run up to 100 fps on a desktop PC, yet the computation time scales linearly with the number of object parts. To our knowledge, this is the first approach to generic tracking of elongated objects.

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3D Coronary Vessel Tree Tracking in X-Ray Projections

Poulain

Malandain

Vaillant³

2019

Functional Imaging and Modeling of the Heart

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CTA angiography brings potentially useful information for guidance in an interventional procedure. It comes with the challenge of registering this 3D modality to the projection of the coronary arteries which are deforming with the cardiac motion. A tree-spline i.e. a tree with a spline attached to each edge and shared control points between these points describes a 3D coronary tree and is able to represent its deformation along the time. We combine this description with a registration algorithm operating between the tree-spline and the angiographic projection of the coronary tree. It starts by the estimation of a rigid transformation for the iso cardiac phase time followed by a non-rigid deformation of the tree driven by the pairings formed between the projection of the edges of the tree-spline and the observed x-ray projection of the coronary arteries. The pairings are built taking into account the tree topology consistency. Anatomical constraints of length preservation is enforced when deforming the arteries. The proposed approach has been evaluated with clinical data issued from ten different clinical cases which enabling to form twenty three different experimental conditions. Encouraging results have been obtained.

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Discrete tracking of parametrized curves

Cited by 9 publications

References 0 publications

Interventional Tool Tracking Using Discrete Optimization

Interventional Tool Tracking Using Discrete Optimization

Pose-Configurable Generic Tracking of Elongated Objects

3D Coronary Vessel Tree Tracking in X-Ray Projections

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