Diaphragm border detection in coronary X-ray angiographies: New method and applications

Petkov, Simeon; Carrillo, Xavier; Radeva, Petia; Gatta, Carlo

doi:10.1016/j.compmedimag.2014.01.003

Cited by 9 publications

(7 citation statements)

References 19 publications

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“…Such non-vascular structures are erroneously segmented by our method only if they intersect or are very close to a vascular branch, otherwise they are enhanced by the Frangi filter but omitted by our segmentation method since the growth process starts from correctly classified seeds. Such error occurs in just few images of our dataset and it can be resolved if these structures are beforehand detected and eliminated from the images using an a priori knowledge for the shape of the diaphragm border [55] or a learning-based method to detect the catheter [43].…”

Section: Discussionmentioning

confidence: 99%

A coronary artery segmentation method based on multiscale analysis and region growing

Kerkeni

Benabdallah

Manzanera

et al. 2016

Computerized Medical Imaging and Graphics

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Accurate coronary artery segmentation is a fundamental step in various medical imaging applications such as stenosis detection, 3D reconstruction and cardiac dynamics assessing. In this paper, a multiscale region growing (MSRG) method for coronary artery segmentation in 2D X-ray angiograms is proposed. First, a region growing rule incorporating both vesselness and direction information in a unique way is introduced. Then an iterative multiscale search based on this criterion is performed. Selected points in each step are considered as seeds for the following step. By combining vesselness and direction information in the growing rule, this method is able to avoid blockage caused by low vesselness values in vascular regions, which in turn, yields continuous vessel tree. Performing the process in a multiscale fashion helps to extract thin and peripheral vessels often missed by other segmentation methods. Quantitative evaluation performed on real angiography images shows that the proposed segmentation method identifies about 80% of the total coronary artery tree in relatively easy images and 70% in challenging cases with a mean precision of 82% and outperforms others segmentation methods in terms of sensitivity. The MSRG segmentation method was also implemented with different enhancement filters and it has been shown that the Frangi filter gives better results. The proposed segmentation method has proven to be tailored for coronary artery segmentation. It keeps an acceptable performance when dealing with challenging situations such as noise, stenosis and poor contrast.

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Section: Discussionmentioning

confidence: 99%

A coronary artery segmentation method based on multiscale analysis and region growing

Kerkeni

Benabdallah

Manzanera

et al. 2016

Computerized Medical Imaging and Graphics

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“…Next, as we are interested in respiratory motion only, we remove structures that show cardiac motion. To this end, similar to [ 6 ], a morphological closing is applied to the downsampled image with a circular structuring element in order to remove any tubular and curvilinear structures, such as coronary arteries, guiding catheters, guide wires and stitches. The size of the structuring element is chosen based on the maximal diameter of coronary arteries and guiding catheters.…”

Section: Methodsmentioning

confidence: 99%

“…These methods involve human interaction to draw an ROI and hence not entirely automatic. Automatic diaphragm detection and tracking were reported in [ 6 , 7 ]. These methods use morphological operation to preprocess XA images followed by a second-order curve fitting to the diaphragm border.…”

Section: Introductionmentioning

confidence: 99%

PCA-derived respiratory motion surrogates from X-ray angiograms for percutaneous coronary interventions

Dibildox

Schultz

et al. 2015

Int J CARS

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PurposeIntraoperative coronary motion modeling with motion surrogates enables prospective motion prediction in X-ray angiograms (XA) for percutaneous coronary interventions. The motion of coronary arteries is mainly affected by patients breathing and heartbeat. Purpose of our work is therefore to extract coronary motion surrogates that are related to respiratory and cardiac motion. In particular, we focus on respiratory motion surrogates extraction in this paper.MethodsWe propose a fast automatic method for extracting patient-specific respiratory motion surrogate from cardiac XA. The method starts with an image preprocessing step to remove all tubular and curvilinear structures from XA images, such as vessels and guiding catheters, followed by principal component analysis on pixel intensities. The respiratory motion surrogate of an XA image is then obtained by projecting its vessel-removed image onto the first principal component.ResultsThis breathing motion surrogate was demonstrated to get high correlation with ground truth diaphragm motion (correlation coefficient over 0.9 on average). In comparison with other related methods, the method we developed did not show significant difference (), but did improve robustness and run faster on monoplane and biplane data in retrospective and prospective scenarios.Conclusionswe developed and evaluated a method in extraction of respiratory motion surrogate from interventional X-ray images that is easy to implement and runs in real time and thus allows extracting respiratory motion surrogates during interventions.

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“…The edge-preserving method was developed for automatic segmentation and tracking of guidewire during intravascular cardiac catheterization. This omits the computation costs required for centerline estimation and optimization procedures present in previous works (15)(16)(17)(18)(19)(20)(21). By adopting the process illustrated in Figure 2, the proposed segmentation and tracking method started with in-vessel extraction of guidewire pixels in X-ray angiograms based on multi-scale differential geometric approaches.…”

Section: Guidewire Segmentation and Trackingmentioning

confidence: 99%

“…A vesselness measure based on Hessian-based guided filtering was adopted for detection and enhancement of vital regions within X-ray angiograms (16). A border detection method has been proposed to avoid confusion of diaphragm borders that are found in angiograms (17).…”

Section: Introductionmentioning

confidence: 99%

Automatic tool segmentation and tracking during robotic intravascular catheterization for cardiac interventions

Omisore¹,

Duan²,

Du³

et al. 2021

Quant Imaging Med Surg

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Background: Cardiovascular diseases resulting from aneurism, thrombosis, and atherosclerosis in the cardiovascular system are major causes of global mortality. Recent treatment methods have been based on catheterization of flexible endovascular tools with imaging guidance. While advances in robotic intravascular catheterization have led to modeling tool navigation approaches with data sensing and feedback, proper adaptation of image-based guidance for robotic navigation requires the development of sensitive segmentation and tracking models without specificity loss. Several methods have been developed to tackle non-uniform illumination, low contrast; however, presence of untargeted body organs commonly found in X-ray frames taken during angiography procedures still presents some major issues to be solved.Methods: In this study, a segmentation method was developed for automatic detection and tracking of guidewire pixels in X-ray angiograms. Image frames were acquired during robotic intravascular catheterization for cardiac interventions. For segmentation, multiscale enhancement filtering was applied on preprocessed X-ray angiograms, while morphological operations and filters were applied to refine the frames for pixel intensity adjustment and vesselness measurement. Minima and maxima extrema of the pixels were obtained to detect guidewire pixels in the X-ray frames. Lastly, morphological operation was applied for guidewire pixel connectivity and tracking in segmented pixels. Method validation was performed on 12 X-ray angiogram sequences which were acquired during in vivo intravascular catheterization trials in rabbits. Results:The study outcomes showed that an overall accuracy of 0.995±0.001 was achieved for segmentation. Tracking performance was characterized with displacement and orientation errors observed as 1.938±2.429 mm and 0.039±0.040°, respectively. Evaluation studies performed against 9 existing methods revealed that this proposed method provides more accurate segmentation with 0.753±0.074 area under curve. Simultaneously, high tracking accuracy of 0.995±0.001 with low displacement and orientation errors of 1.938±2.429 mm and 0.039±0.040°, respectively, were achieved. Also, the method demonstrated higher sensitivity and specificity values compared to the 9 existing methods, with a relatively faster exaction time. Conclusions:The proposed method has the capability to enhance robotic intravascular catheterization during percutaneous coronary interventions (PCIs). Thus, interventionists can be provided with better tool tracking and visualization systems while also reducing their exposure to operational hazards during intravascular catheterization for cardiac interventions.

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Diaphragm border detection in coronary X-ray angiographies: New method and applications

Cited by 9 publications

References 19 publications

A coronary artery segmentation method based on multiscale analysis and region growing

A coronary artery segmentation method based on multiscale analysis and region growing

PCA-derived respiratory motion surrogates from X-ray angiograms for percutaneous coronary interventions

Automatic tool segmentation and tracking during robotic intravascular catheterization for cardiac interventions

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