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Purpose: A new segmentation technique is implemented for automatic lumen area extraction and stent strut detection in intravascular optical coherence tomography (OCT) images for the purpose of quantitative analysis of in-stent restenosis (ISR). In addition, a user-friendly graphical user interface (GUI) is developed based on the employed algorithm toward clinical use., Methods: Four clinical datasets of frequency-domain OCT scans of the human femoral artery were analyzed. First, a segmentation method based on fuzzy C means (FCM) clustering and wavelet transform (WT) was applied toward inner luminal contour extraction. Subsequently, stent strut positions were detected by utilizing metrics derived from the local maxima of the wavelet transform into the FCM membership function., Results: The inner lumen contour and the position of stent strut were extracted with high precision. Compared to manual segmentation by an expert physician, the automatic lumen contour delineation had an average overlap value of 0.917 ± 0.065 for all OCT images included in the study. The strut detection procedure achieved an overall accuracy of 93.80% and successfully identified 9.57 ± 0.5 struts for every OCT image. Processing time was confined to approximately 2.5 s per OCT frame., Conclusions: A new fast and robust automatic segmentation technique combining FCM and WT for lumen border extraction and strut detection in intravascular OCT images was designed and implemented. The proposed algorithm integrated in a GUI represents a step forward toward the employment of automated quantitative analysis of ISR in clinical practice.

Purpose: Optical coherence tomography (OCT) is a catheter-based imaging method that employs near-infrared light to produce high-resolution cross-sectional intravascular images. The authors propose a segmentation technique for automatic lumen area extraction and stent strut detection in intravascular OCT images for the purpose of quantitative analysis of neointimal hyperplasia (NIH)., Methods: A clinical dataset of frequency-domain OCT scans of the human femoral artery was analyzed. First, a segmentation method based on the Markov random field (MRF) model was employed for lumen area identification. Second, textural and edge information derived from local intensity distribution and continuous wavelet transform (CWT) analysis were integrated to extract the inner luminal contour. Finally, the stent strut positions were detected via the introduction of each strut wavelet response across scales into a feature extraction and classification scheme in order to optimize the strut position detection., Results: The inner lumen contour and the position of stent strut were extracted with very high accuracy. Compared with manual segmentation by an expert vascular physician the automatic segmentation had an average overlap value of 0.937 ± 0.045 for all OCT images included in the study. The strut detection accuracy had an area under the curve (AUC) value of 0.95, together with sensitivity and specificity average values of 0.91 and 0.96, respectively., Conclusions: A robust automatic segmentation technique integrating textural and edge information for vessel lumen border extraction and strut detection in intravascular OCT images was designed and presented. The proposed algorithm may be employed for automated quantitative morphological analysis of in-stent neointimal hyperplasia.

Purpose: To investigate distal embolism during endovascular procedures of the infra-aortic arteries by utilizing a commercial filter basket and unveil any correlation between the baseline clinical and procedural variables and the histopathological findings of the collected particles., Methods: In a prospective study, 48 patients (37 men; mean age 70.8+/-7.8 years, range 50- 83) underwent endoluminal therapy of infra-aortic lesions (stenosis >75% or occlusion; mean lesion length 52.2+/-38.0 mm) with standard endovascular procedures. A nitinol filter basket (n=50) was employed for distal protection. The collected particles were histopathologically analyzed. The harvested specimens were quantified after digital image post processing., Results: Procedural success of filter-protected revascularization was 93.8%. Three failures included 1 vasospasm, 1 distal embolus, and 1 side-branch occlusion. The total area of retrieved particles per basket was 2.76+/-6.49 mm(2) (range 0.0-40.3). Particles with a major axis >1 and >3 mm were detected in 29 (58.0%) and 6 (12.0%), respectively, of the examined filters. Collected particles consisted primarily of platelets and fibrin conglomerates, trapped erythrocytes, inflammatory cells, and extracellular matrix. Increased lesion length, increased reference vessel diameter, acute thromboses, and total occlusions were positively correlated with higher amounts of captured particles (p<0.05). Multivariate analysis incriminated declotting procedures as the only independent predictor of increased embolic burden (p<0.05)., Conclusion: The embolism phenomenon during infra-aortic interventions is frequent and underestimated. The liberated particles consisted primarily of atheromatous plaque elements and thrombus. The reported data might support the application of a protective filter basket in selected subsets of lesions with a riskier embolic profile and whenever declotting procedures are performed.