Automatic lumen segmentation using uniqueness of vascular connected region for intravascular optical coherence tomography

Zhu, Fengyu; Ding, Zhenyang; Tao, Kuiyuan; Li, Qingrui; Kuang, Hao; Tian, Feng; Zhou, Shanshan; Hua, Peidong; Hu, Jingqi; Shang, Mingjian; Yin, Yaozu; Liu, Tiegen

doi:10.1002/jbio.202100124

Cited by 6 publications

(2 citation statements)

References 38 publications

(70 reference statements)

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“…Segmentation of the coronary artery lumen contour is perhaps the simplest task for automated techniques when there is no atherosclerotic disease and there has been appropriate clearance of blood from the OCT images. Here, globally used binarization methods [ 59 ], such as Otsu filtering [ 60 , 61 , 62 , 63 ], morphological operations, edge detection [ 64 , 65 , 66 ] and curve fitting [ 67 ] were often sufficient to automatically delineate the lumen. However, these methods are challenged when facing bifurcation regions and catheter artefacts, as well as improper blood clearance, which are not uncommon occurrences in clinical scenarios.…”

Section: Coronary Lumenmentioning

confidence: 99%

Automated Coronary Optical Coherence Tomography Feature Extraction with Application to Three-Dimensional Reconstruction

et al. 2022

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Coronary optical coherence tomography (OCT) is an intravascular, near-infrared light-based imaging modality capable of reaching axial resolutions of 10–20 µm. This resolution allows for accurate determination of high-risk plaque features, such as thin cap fibroatheroma; however, visualization of morphological features alone still provides unreliable positive predictive capability for plaque progression or future major adverse cardiovascular events (MACE). Biomechanical simulation could assist in this prediction, but this requires extracting morphological features from intravascular imaging to construct accurate three-dimensional (3D) simulations of patients’ arteries. Extracting these features is a laborious process, often carried out manually by trained experts. To address this challenge, numerous techniques have emerged to automate these processes while simultaneously overcoming difficulties associated with OCT imaging, such as its limited penetration depth. This systematic review summarizes advances in automated segmentation techniques from the past five years (2016–2021) with a focus on their application to the 3D reconstruction of vessels and their subsequent simulation. We discuss four categories based on the feature being processed, namely: coronary lumen; artery layers; plaque characteristics and subtypes; and stents. Areas for future innovation are also discussed as well as their potential for future translation.

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Section: Coronary Lumenmentioning

confidence: 99%

Automated Coronary Optical Coherence Tomography Feature Extraction with Application to Three-Dimensional Reconstruction

et al. 2022

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“…Segmentation of the coronary artery lumen contour is perhaps the simplest task for automated techniques when there is no atherosclerotic disease and there has been appropriate clearance of blood from the OCT images. Here, binarisation methods [56], such as Otsu filtering [57][58][59][60], morphological operations, edge detection [61][62][63] and curve fitting [64] were often sufficient to automatically delineate the lumen. However, these methods are challenged when facing bifurcation regions and catheter artefacts, as well as improper blood clearance, which are not uncommon occurrences in clinical scenarios.…”

Section: Coronary Lumen (Table A1)mentioning

confidence: 99%

Automated Coronary Optical Coherence Tomography Feature Extraction with Application to Three-Dimensional Reconstruction

Carpenter¹,

Ghayesh²,

Zander³

et al. 2022

Preprint

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Coronary optical coherence tomography (OCT) is an intravascular, near-infrared light-based imaging modality capable of reaching axial resolutions of 10-20 µm. This resolution allows for accurate determination of high-risk plaque features, such as thin cap fibroatheroma; however, visualisation of morphological features alone still provides unreliable positive predictive capability for plaque progression or future major adverse cardiovascular events (MACE). Biomechanical simulation could assist in this prediction, but this requires extracting morphological features from intravascular imaging to construct accurate three-dimensional simulations of patients’ arteries. Extracting these features is a laborious process, often carried out manually by trained experts. To address this challenge, numerous techniques have emerged to automate these processes while simultaneously overcoming difficulties associated with OCT imaging, such as its limited penetration depth. This systematic review summarises advances in automated segmentation techniques from the past five years (2016-2021) with a focus on their application to the three-dimensional reconstruction of vessels and their subsequent simulation. We discuss four categories based on the feature being processed, namely: coronary lumen; plaque characteristics and subtypes; artery layers; and stents. Areas for future innovation are also discussed as well as their potential for future translation.

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Efficient Autonomous Lumen Segmentation in Intravascular Optical Coherence Tomography Images: Unveiling the Potential of Polynomial-Regression Convolutional Neural Network

Lau,

Tan,

Chee

et al. 2024

IRBM

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Automatic lumen segmentation using uniqueness of vascular connected region for intravascular optical coherence tomography

Cited by 6 publications

References 38 publications

Automated Coronary Optical Coherence Tomography Feature Extraction with Application to Three-Dimensional Reconstruction

Automated Coronary Optical Coherence Tomography Feature Extraction with Application to Three-Dimensional Reconstruction

Automated Coronary Optical Coherence Tomography Feature Extraction with Application to Three-Dimensional Reconstruction

Efficient Autonomous Lumen Segmentation in Intravascular Optical Coherence Tomography Images: Unveiling the Potential of Polynomial-Regression Convolutional Neural Network

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