Background / Objectives: Automatic algorithms for stent struts segmentation in optical coherence tomography (OCT) images of coronary arteries have been developed over the years, particularly with application on metallic stents. The aim of this study is three-fold: (1) to develop and to validate a segmentation algorithm for the detection of both lumen contours and polymeric bioresorbable scaffold struts from 8-bit OCT images, (2) to develop a method for automatic OCT pullback quality assessment, and (3) to demonstrate the applicability of the segmentation algorithm for the creation of patient-specific stented coronary artery for local hemodynamics analysis.
Methods: The proposed OCT segmentation algorithm comprises four steps: (1) image pre-processing, (2) lumen segmentation, (3) stent struts segmentation, (4) strut-based lumen correction. This segmentation process is then followed by an automatic OCT pullback image quality assessment. This method classifies the OCT pullback image quality as 'good' or 'poor' based on the number of regions detected by the stent segmentation. The segmentation algorithm was validated against manual segmentation of 1150 images obtained from 23 in vivo OCT pullbacks.
Results: When considering the entire set of OCT pullbacks, lumen segmentation showed results comparable with manual segmentation and with previous studies (sensitivity ~97%, specificity ~99%), while stent segmentation showed poorer results compared to manual segmentation (sensitivity ~63%, precision ~83%). The OCT pullback quality assessment algorithm classified 7 pullbacks as 'poor' quality cases. When considering only the 'good' classified cases, the performance indexes of the scaffold segmentation were higher (sensitivity >76%, precision >86%).
Conclusions: This study proposes a segmentation algorithm for the detection of lumen contours and stent struts in low quality OCT images of patients treated with polymeric bioresorbable scaffolds. The segmentation results were successfully used for the reconstruction of one coronary artery model that included a bioresorbable scaffold geometry for computational fluid dynamics analysis.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6417773 | PMC |
| http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0213603 | PLOS |
Publication Analysis
Top Keywords
Similar Publications
Automating hock wound detection in dairy cattle.
JDS Commun
January 2025
Department of Population Medicine and Diagnostic Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY 14853.
Hock scoring in dairy cattle is a crucial welfare assessment tool used to evaluate the condition of a cow's hocks, particularly for signs of injury, swelling, or lesions. These scores provide insight into the overall well-being of the animals and are essential for ensuring proper management and housing conditions. Accurate hock scoring is vital because it can indicate issues such as poor bedding quality or inadequate space, which directly affect the health and productivity of the herd.
View Article and Find Full Text PDFDeformable image registration (DIR) is an enabling technology in many diagnostic and therapeutic tasks. Despite this, DIR algorithms have limited clinical use, largely due to a lack of benchmark datasets for quality assurance during development. To support future algorithm development, here we introduce our first-of-its-kind abdominal CT DIR benchmark dataset, comprising large numbers of highly accurate landmark pairs on matching blood vessel bifurcations.
View Article and Find Full Text PDFSapphire fiber Bragg gratings (SFBGs) are promising high-temperature sensors in many harsh environments, such as aviation, nuclear power, and furnaces. Here, we proposed and experimentally demonstrated a quasi-distributed high-temperature sensor based on an SFBG array sealed in an argon gas-infiltrated sapphire tube interrogated by using an InGaAs-based interrogator. An SFBG array including five SFBGs was inscribed using the femtosecond laser line-by-line method and sealed in an argon gas-infiltrated sapphire tube.
View Article and Find Full Text PDFThis paper addresses the thermal instability of lasers resulting from the thermal effects of the 2 µm gain medium, proposing what we believe to be a novel compensation scheme that integrates machine learning technology with multi-segment bonded Tm: YAG crystals and negative lenses, based on the thermal focal length model of a thick thermal lens. This approach significantly optimizes thermal compensation and facilitates rapid assessment of the light-emitting behavior trends of Tm: YAG lasers. Firstly, the thermal behavior of conventional and multi-segment bonded Tm: YAG crystals is analyzed.
View Article and Find Full Text PDFF-FDG PET/CT radiomic analysis and artificial intelligence to predict pathological complete response after neoadjuvant chemotherapy in breast cancer patients.
Radiol Med
January 2025
Department of Translational Medicine, University of Ferrara, Ferrara, Italy.
Purpose: Build machine learning (ML) models able to predict pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer (BC) patients based on conventional and radiomic signatures extracted from baseline [F]FDG PET/CT.
Material And Methods: Primary tumor and the most significant lymph node metastasis were manually segmented in baseline [F]FDG PET/CT of 52 newly diagnosed BC patients. Clinical parameters, NAC and conventional semiquantitative PET parameters were collected.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!