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Shi P, Xin J, Du S, Wu J, Deng Y, Cai Z, Zheng N. Automatic lumen and anatomical layers segmentation in IVOCT images using meta learning. JOURNAL OF BIOPHOTONICS 2023; 16:e202300059. [PMID: 37289201 DOI: 10.1002/jbio.202300059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 04/12/2023] [Accepted: 06/01/2023] [Indexed: 06/09/2023]
Abstract
Automated analysis of the vessel structure in intravascular optical coherence tomography (IVOCT) images is critical to assess the health status of vessels and monitor coronary artery disease progression. However, deep learning-based methods usually require well-annotated large datasets, which are difficult to obtain in the field of medical image analysis. Hence, an automatic layers segmentation method based on meta-learning was proposed, which can simultaneously extract the surfaces of the lumen, intima, media, and adventitia using a handful of annotated samples. Specifically, we leverage a bi-level gradient strategy to train a meta-learner for capturing the shared meta-knowledge among different anatomical layers and quickly adapting to unknown anatomical layers. Then, a Claw-type network and a contrast consistency loss were designed to better learn the meta-knowledge according to the characteristic of annotation of the lumen and anatomical layers. Experimental results on the two cardiovascular IVOCT datasets show that the proposed method achieved state-of-art performance.
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Affiliation(s)
- Peiwen Shi
- Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, China
| | - Jingmin Xin
- Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, China
| | - Shaoyi Du
- Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, China
| | - Jiayi Wu
- Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, China
| | - Yangyang Deng
- Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, China
- Cardiovascular Department, First Affiliated Hospital of Xi'an Jiaotong University, China
| | - Zhuotong Cai
- Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, China
| | - Nanning Zheng
- Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, China
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Gui L, Ma J, Yang X. Shape prior generation and geodesic active contour interactive iterating algorithm (SPACIAL): fully automatic segmentation for 3D lumen in intravascular optical coherence tomography images. Med Phys 2021; 48:7099-7111. [PMID: 34469593 DOI: 10.1002/mp.15201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Revised: 07/24/2021] [Accepted: 08/21/2021] [Indexed: 11/07/2022] Open
Abstract
PURPOSE Fully automatic lumen segmentation in intravascular optical coherence tomography (OCT) images can assist physicians in quickly estimating the health status of vessels. However, OCT images are usually degraded by residual blood, catheter walls, guide wire artifacts, etc., which significantly reduce the quality of segmentation. To achieve accurate lumen segmentation in low-quality images, we propose a novel segmentation algorithm named SPACIAL: Shape Prior generation and geodesic Active Contour Interactive iterAting aLgorithm, which is guided by an adaptively generated shape prior. METHODS In this framework, the active contour evolves under the guidance of shape prior, while the shape prior is automatically and adaptively generated based on the active contour. The active contour and the shape prior interactively iterate each other, which can generate the adaptive shape prior and consequently lead to accurate segmentation results. In addition, a fast algorithm is introduced to accelerate the segmentation in 3D images. RESULTS The validity of the model is verified in 3240 images from 12 OCT pullbacks. The experimental results show satisfactory segmentation accuracy and time efficiency: the average Dice coefficient of SPACIAL is 93.6(2.4)%, and 5.7 times faster than that of the classical level set method. CONCLUSION The proposed SPACIAL can quickly and efficiently perform accurate lumen segmentation on low quality OCT images, which is of great importance to cardiovascular disease diagnosis . The SPACIAL method shows great potential in clinical applications.
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Affiliation(s)
- Luying Gui
- School of Science, Nanjing University of Science and Technology, Nanjing, Jiangsu, China
| | - Jun Ma
- School of Science, Nanjing University of Science and Technology, Nanjing, Jiangsu, China
| | - Xiaoping Yang
- Department of Mathematics, Nanjing University, Nanjing, Jiangsu, China
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Chiastra C, Migliori S, Burzotta F, Dubini G, Migliavacca F. Patient-Specific Modeling of Stented Coronary Arteries Reconstructed from Optical Coherence Tomography: Towards a Widespread Clinical Use of Fluid Dynamics Analyses. J Cardiovasc Transl Res 2017; 11:156-172. [PMID: 29282628 PMCID: PMC5908818 DOI: 10.1007/s12265-017-9777-6] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2017] [Accepted: 12/18/2017] [Indexed: 11/30/2022]
Abstract
The recent widespread application of optical coherence tomography (OCT) in interventional cardiology has improved patient-specific modeling of stented coronary arteries for the investigation of local hemodynamics. In this review, the workflow for the creation of fluid dynamics models of stented coronary arteries from OCT images is presented. The algorithms for lumen contours and stent strut detection from OCT as well as the reconstruction methods of stented geometries are discussed. Furthermore, the state of the art of studies that investigate the hemodynamics of OCT-based stented coronary artery geometries is reported. Although those studies analyzed few patient-specific cases, the application of the current reconstruction methods of stented geometries to large populations is possible. However, the improvement of these methods and the reduction of the time needed for the entire modeling process are crucial for a widespread clinical use of the OCT-based models and future in silico clinical trials.
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Affiliation(s)
- Claudio Chiastra
- Laboratory of Biological Structure Mechanics (LaBS), Department of Chemistry, Materials and Chemical Engineering "Giulio Natta", Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milan, Italy.
| | - Susanna Migliori
- Laboratory of Biological Structure Mechanics (LaBS), Department of Chemistry, Materials and Chemical Engineering "Giulio Natta", Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milan, Italy
| | - Francesco Burzotta
- Institute of Cardiology, Catholic University of the Sacred Heart, Rome, Italy
| | - Gabriele Dubini
- Laboratory of Biological Structure Mechanics (LaBS), Department of Chemistry, Materials and Chemical Engineering "Giulio Natta", Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milan, Italy
| | - Francesco Migliavacca
- Laboratory of Biological Structure Mechanics (LaBS), Department of Chemistry, Materials and Chemical Engineering "Giulio Natta", Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milan, Italy
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Reconstruction of stented coronary arteries from optical coherence tomography images: Feasibility, validation, and repeatability of a segmentation method. PLoS One 2017; 12:e0177495. [PMID: 28574987 PMCID: PMC5456060 DOI: 10.1371/journal.pone.0177495] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2017] [Accepted: 04/27/2017] [Indexed: 11/19/2022] Open
Abstract
Optical coherence tomography (OCT) is an established catheter-based imaging modality for the assessment of coronary artery disease and the guidance of stent placement during percutaneous coronary intervention. Manual analysis of large OCT datasets for vessel contours or stent struts detection is time-consuming and unsuitable for real-time applications. In this study, a fully automatic method was developed for detection of both vessel contours and stent struts. The method was applied to in vitro OCT scans of eight stented silicone bifurcation phantoms for validation purposes. The proposed algorithm comprised four main steps, namely pre-processing, lumen border detection, stent strut detection, and three-dimensional point cloud creation. The algorithm was validated against manual segmentation performed by two independent image readers. Linear regression showed good agreement between automatic and manual segmentations in terms of lumen area (r>0.99). No statistically significant differences in the number of detected struts were found between the segmentations. Mean values of similarity indexes were >95% and >85% for the lumen and stent detection, respectively. Stent point clouds of two selected cases, obtained after OCT image processing, were compared to the centerline points of the corresponding stent reconstructions from micro computed tomography, used as ground-truth. Quantitative comparison between the corresponding stent points resulted in median values of ~150 μm and ~40 μm for the total and radial distances of both cases, respectively. The repeatability of the detection method was investigated by calculating the lumen volume and the mean number of detected struts per frame for seven repeated OCT scans of one selected case. Results showed low deviation of values from the median for both analyzed quantities. In conclusion, this study presents a robust automatic method for detection of lumen contours and stent struts from OCT as supported by focused validation against both manual segmentation and micro computed tomography and by good repeatability.
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Liu S, Eggermont J, Wolterbeek R, Broersen A, Busk CAGR, Precht H, Lelieveldt BPF, Dijkstra J. Analysis and compensation for the effect of the catheter position on image intensities in intravascular optical coherence tomography. JOURNAL OF BIOMEDICAL OPTICS 2016; 21:126005. [PMID: 27926746 DOI: 10.1117/1.jbo.21.12.126005] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/22/2016] [Accepted: 11/09/2016] [Indexed: 06/06/2023]
Abstract
Intravascular optical coherence tomography (IVOCT) is an imaging technique that is used to analyze the underlying cause of cardiovascular disease. Because a catheter is used during imaging, the intensities can be affected by the catheter position. This work aims to analyze the effect of the catheter position on IVOCT image intensities and to propose a compensation method to minimize this effect in order to improve the visualization and the automatic analysis of IVOCT images. The effect of catheter position is modeled with respect to the distance between the catheter and the arterial wall (distance-dependent factor) and the incident angle onto the arterial wall (angle-dependent factor). A light transmission model incorporating both factors is introduced. On the basis of this model, the interaction effect of both factors is estimated with a hierarchical multivariant linear regression model. Statistical analysis shows that IVOCT intensities are significantly affected by both factors with p<0.001, as either aspect increases the intensity decreases. This effect differs for different pullbacks. The regression results were used to compensate for this effect. Experiments show that the proposed compensation method can improve the performance of the automatic bioresorbable vascular scaffold strut detection.
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Affiliation(s)
- Shengnan Liu
- Leiden University Medical Center, Department of Radiology C2-S, Division of Image Processing, P.O. Box 9600, Leiden 2300 RC, The Netherlands
| | - Jeroen Eggermont
- Leiden University Medical Center, Department of Radiology C2-S, Division of Image Processing, P.O. Box 9600, Leiden 2300 RC, The Netherlands
| | - Ron Wolterbeek
- Leiden University Medical Center, Department of Medical Statistics and Bioinformatics, P.O. Box 9600, Leiden 2300 RC, The Netherlands
| | - Alexander Broersen
- Leiden University Medical Center, Department of Radiology C2-S, Division of Image Processing, P.O. Box 9600, Leiden 2300 RC, The Netherlands
| | - Carol A G R Busk
- University of Southern Denmark, Institute of Forensic Medicine, Odense C 5000, Denmark
| | - Helle Precht
- University College Lillebaelt, Conrad Research Center, Odense SØ 5220, Denmark
| | - Boudewijn P F Lelieveldt
- Leiden University Medical Center, Department of Radiology C2-S, Division of Image Processing, P.O. Box 9600, Leiden 2300 RC, The NetherlandseDelft University of Technology, Intelligent Systems Department, P.O. Box 5031, Delft 2600 GA, The Netherlands
| | - Jouke Dijkstra
- Leiden University Medical Center, Department of Radiology C2-S, Division of Image Processing, P.O. Box 9600, Leiden 2300 RC, The Netherlands
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Menguy PY, Péry E, Ouchchane L, Guttmann A, Trésorier R, Combaret N, Motreff P, Sarry L. Preliminary results for the supervised detection of lumen and stent from OCT pullbacks. Ing Rech Biomed 2016. [DOI: 10.1016/j.irbm.2015.12.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Ciaccio EJ. Honored papers 2015. Comput Biol Med 2016. [DOI: 10.1016/j.compbiomed.2016.05.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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van der Marel K, Gounis MJ, Weaver JP, de Korte AM, King RM, Arends JM, Brooks OW, Wakhloo AK, Puri AS. Grading of Regional Apposition after Flow-Diverter Treatment (GRAFT): a comparative evaluation of VasoCT and intravascular OCT. J Neurointerv Surg 2015. [DOI: 10.1136/neurintsurg-2015-011843] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
BackgroundPoor vessel wall apposition of flow diverter (FD) stents poses risks for stroke-related complications when treating intracranial aneurysms, necessitating long-term surveillance imaging. To facilitate quantitative evaluation of deployed devices, a novel algorithm is presented that generates intuitive two-dimensional representations of wall apposition from either high-resolution contrast-enhanced cone-beam CT (VasoCT) or intravascular optical coherence tomography (OCT) images.MethodsVasoCT and OCT images were obtained after FD implant (n=8 aneurysms) in an experimental sidewall aneurysm model in canines. Surface models of the vessel wall and FD device were extracted, and the distance between them was presented on a two-dimensional flattened map. Maps and cross-sections at potential locations of malapposition detected on VasoCT-based maps were compared. The performance of OCT-based apposition detection was evaluated on manually labeled cross-sections using logistic regression against a thresholded (≥0.25 mm) apposition measure.ResultsVasoCT and OCT acquisitions yielded similar Grading of Regional Apposition after Flow-Diverter Treatment (GRAFT) apposition maps. GRAFT maps from VasoCT highlighted 16 potential locations of malapposition, of which two were found to represent malapposed device struts. Logistic regression showed that OCT could detect malapposition with a sensitivity of 98% and a specificity of 81%.ConclusionsGRAFT delivered quantitative and visually convenient representations of potential FD malapposition and occasional acute thrombus formation. A powerful combination for future neuroendovascular applications is foreseen with the superior resolution delivered by intravascular OCT.
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