Coronary Tree Extraction Using Motion Layer Separation

Zhang, Wei; Ling, Haibin; Prummer, Simone; Zhou, Kevin; Ostermeier, Martin; Comanicìu, Dorin

doi:10.1007/978-3-642-04268-3_15

Cited by 12 publications

(4 citation statements)

References 16 publications

(18 reference statements)

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“…With logarithmic postprocessing, the intensity can be described as an additive superposition of multiple tissue layers undergoing different movements. Previous work focused on separating transparent layers from one or multiple images [13,15,16], based on the assumption that the layers remain static, or their motion is either known beforehand or irrelevant to layer separation. Jointly recovering layers and their motion from fluoroscopic images remains an open problem.…”

Section: Methodsmentioning

confidence: 99%

Image-Based Respiratory Motion Compensation for Fluoroscopic Coronary Roadmapping

Zhu

Tsin

Sundar

et al. 2010

Lecture Notes in Computer Science

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We present a new image-based respiratory motion compensation method for coronary roadmapping in fluoroscopic images. A temporal analysis scheme is proposed to identify static structures in the image gradient domain. An extended Lucas-Kanade algorithm involving a weighted sum-of-squared-difference (WSSD) measure is proposed to estimate the soft tissue motion in the presence of static structures. A temporally compositional motion model is used to deal with large image motion incurred by deep breathing. Promising results have been shown in the experiments conducted on clinical data.

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Section: Methodsmentioning

confidence: 99%

Image-Based Respiratory Motion Compensation for Fluoroscopic Coronary Roadmapping

Zhu

Tsin

Sundar

et al. 2010

Lecture Notes in Computer Science

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“…Therefore, multi-layer methods can exploit the discriminative motion characteristics inherent in different anatomical structures to achieve better layer separation. Zhang et al (2009) employed a multi-scale framework to optimize the multi-layer separation problem by minimizing a reconstruction error. It utilized thin plate spline interpolation to refine the complex motion field of vessels, which relied on manually selected control points at the finest scale.…”

Section: Related Workmentioning

confidence: 99%

Vesselness-constrained robust PCA for vessel enhancement in x-ray coronary angiograms

et al. 2018

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Effective vessel enhancement in x-ray coronary angiograms (XCA) is essential for the diagnosis of coronary artery disease, yet challenged by complex background structures of varying intensities as well as motion patterns. As a typical layer-separation method, robust principal component analysis (RPCA) has been proposed to automatically improve vessel visibility via sparse and low-rank decomposition. However, the attenuated motion of vessels in x-ray angiograms leads to the unsatisfactory vessel enhancement performance of the decomposition framework. To address this problem, we propose a vesselness-constrained RPCA method (VC-RPCA), where a vessel-like appearance prior is incorporated into the layer separation framework for accurate vessel enhancement. We first pre-compute the vessel-like appearance prior based on a Frangi filter to highlight the curvilinear structures. After removing large-scale background structures via a morphological closing operation, we then integrate the pre-computed vessel-like appearance prior into a low-rank decomposition framework to separate the fine vessel structures. In addition, we develop an adaptive regularization strategy that imposes structured-sparse constraints to solve the scale issue and capture vessels without salient motion. The proposed method was validated on 13 clinical XCA sequences containing 777 images in total. The contrast-to-noise ratio, Dice coefficient and area under the ROC curve were employed for quantitative evaluation of the vessel enhancement performance. Experiments show that (1) the adaptive regularization strategy helps to obtain a complete coronary tree in the separated vessel layer; (2) our low-rank decomposition framework is robust against false positive/negative responses of the Frangi filter; and (3) the proposed VC-RPCA is computationally fast and outperforms other state-of-the-art RPCA methods for vessel enhancement in the full-contrast and low-contrast scenarios. The results demonstrate that the proposed VC-RPCA can accurately separate coronary arteries and prominently improve vessel visibility in x-ray angiograms.

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“…Zhu et al [15] divided the sequence into the vascular and non-vascular layers and applied optical flow to the non-vascular layer to compute the deformation filed. Zhang et al [16] separated the sequence into three layers, including static, lung (slow motion) and vessel (rapid motion) layer and constructed a motion transformation model for each layer. Nevertheless, the structures in the XRA sequence participate in different motion patterns.…”

Section: Introductionmentioning

confidence: 99%

Inter/intra-frame constrained vascular segmentation in X-ray angiographic image sequence

Song

Chen

et al. 2019

BMC Med Inform Decis Mak

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BackgroundAutomatic vascular segmentation in X-ray angiographic image sequence is of crucial interest, for instance, for better quantifying coronary arteries in diagnostic and interventional procedures.MethodsA novel inter/intra-frame constrained vascular segmentation method is proposed to automatically segment vessels in coronary X-ray angiographic image sequence. First, a morphological filter operator is applied to remove structures undergoing the respiratory motion from the original image sequence. Second, an inter-frame constrained robust principal component analysis (RPCA) is utilized to remove the quasi-static structures from the image sequence. Third, an intra-frame constrained RPCA is employed to smooth the final extracted vascular sequence. Fourth, a multi-feature fusion is designed to improve the vascular contrast and the final vascular segmentation is realized by thresholding-based method.ResultsExperiments are conducted on 22 clinical X-ray angiographic image sequences. The global and local contrast-to-noise ratio of the proposed method are 6.6344 and 4.2882, respectively. And the precision, sensitivity and F1 value are 0.7378, 0.7960 and 0.7658, respectively. It demonstrates that our method is effective and robust for vascular segmentation from image sequence.ConclusionsThe proposed method is effective to remove non-vascular structures, reduce motion artefacts and other non-uniform illumination caused noises. Also, the proposed method is online which can just process one image per time without re-optimizing the model.

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Coronary Tree Extraction Using Motion Layer Separation

Cited by 12 publications

References 16 publications

Image-Based Respiratory Motion Compensation for Fluoroscopic Coronary Roadmapping

Image-Based Respiratory Motion Compensation for Fluoroscopic Coronary Roadmapping

Vesselness-constrained robust PCA for vessel enhancement in x-ray coronary angiograms

Inter/intra-frame constrained vascular segmentation in X-ray angiographic image sequence

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