Automatic stent and catheter marker detection in X-ray fluoroscopy using adaptive thresholding and classification

Chabi, Negar; Beuing, Oliver; Preim, Bernhard; Saalfeld, Sylvia

doi:10.1515/cdbme-2020-0006

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…Therefore, for opaque markers that are not necessarily pairwise like in our study, other techniques have to be designed. Accordingly, in our previous study [ 8 ], we proposed a general approach to detect stent and catheter markers. The main differences between our previous and our current approach, are as follows: different pre-processing techniques were used, in the previous technique, adaptive thresholding was used to detect probable marker areas and its performance highly depends on its sensitivity value, which finally negatively affected the generalizability of the method.…”

Section: Discussionmentioning

confidence: 99%

“…Due to the blob-like structure of the markers, blob analysis was used to generate features. Difference of Gaussian (DoG), Laplacian of Gaussian (LoG), and two types of the Determinant of Hessian (DoH) were computed for all the extracted patches [ 8 ]. The Hessian matrix for the first type of DoH is the directional gradient of the input, while the Hessian matrix for the second type of DoH is the second derivative of the Gaussian of the input.…”

Section: Methodsmentioning

confidence: 99%

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Self-calibration of C-arm imaging system using interventional instruments during an intracranial biplane angiography

Chabi

Iuso

Beuing³

et al. 2022

Int J CARS

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Purpose To create an accurate 3D reconstruction of the vascular trees, it is necessary to know the exact geometrical parameters of the angiographic imaging system. Many previous studies used vascular structures to estimate the system’s exact geometry. However, utilizing interventional devices and their relative features may be less challenging, as they are unique in different views. We present a semi-automatic self-calibration approach considering the markers attached to the interventional instruments to estimate the accurate geometry of a biplane X-ray angiography system for neuroradiologic use. Methods A novel approach is proposed to detect and segment the markers using machine learning classification, a combination of support vector machine and boosted tree. Then, these markers are considered as reference points to optimize the acquisition geometry iteratively. Results The method is evaluated on four clinical datasets and three pairs of phantom angiograms. The mean and standard deviation of backprojection error for the catheter or guidewire before and after self-calibration are $$7.13\pm 6.47$$ 7.13 ± 6.47 mm and $$0.10\pm 0.06$$ 0.10 ± 0.06 mm, respectively. The mean and standard deviation of the 3D root-mean-square error (RMSE) for some markers in the phantom reduced from $$0.51\pm 0.11$$ 0.51 ± 0.11 to $$0.31\pm 0.08$$ 0.31 ± 0.08 mm. Conclusion A semi-automatic approach to estimate the accurate geometry of the C-arm system was presented. Results show the reduction in the 2D backprojection error as well as the 3D RMSE after using our proposed self-calibration technique. This approach is essential for 3D reconstruction of the vascular trees or post-processing techniques of angiography systems that rely on accurate geometry parameters.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Self-calibration of C-arm imaging system using interventional instruments during an intracranial biplane angiography

Chabi

Iuso

Beuing³

et al. 2022

Int J CARS

Self Cite

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“…Lu et al [15] used probabilistic boosting trees combining joint local context of each marker pair as classifiers to detect markers. Chabi et al [6] detected potential markers based on adaptive threshold and refined detections by excluding non-mask area using various machine learning classifiers, including k-nearest neighbor, naive Bayesian classifier, support vector machine and linear discriminant analysis. Vernikouskaya et al [23] employed U-Net, a popular encoder-decoder like CNN designed specifically for medical images, to segment markers and catheter shafts during pulmonary vein isolation as binary masks.…”

Section: Balloon Marker Detectionmentioning

confidence: 99%

Robust Landmark-based Stent Tracking in X-ray Fluoroscopy

Huang¹,

Liu²,

Chen³

et al. 2022

Preprint

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In clinical procedures of angioplasty (i.e., open clogged coronary arteries), devices such as balloons and stents need to be placed and expanded in arteries under the guidance of X-ray fluoroscopy. Due to the limitation of X-ray dose, the resulting images are often noisy. To check the correct placement of these devices, typically multiple motioncompensated frames are averaged to enhance the view. Therefore, device tracking is a necessary procedure for this purpose. Even though angioplasty devices are designed to have radiopaque markers for the ease of tracking, current methods struggle to deliver satisfactory results due to the small marker size and complex scenes in angioplasty. In this paper, we propose an end-to-end deep learning framework for single stent tracking, which consists of three hierarchical modules: a U-Net for landmark detection, a ResNet for stent proposal and feature extraction, and a graph convolutional neural network for stent tracking that temporally aggregates both spatial information and appearance features. The experiments show that our method performs significantly better in detection compared with the state-of-the-art point-based tracking models. In addition, its fast inference speed satisfies clinical requirements.

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