Automatic vessel lumen segmentation and stent strut detection in intravascular optical coherence tomography

Tsantis, Stavros; Kagadis, George C.; Κατσάνος, Κωνσταντίνος; Karnabatidis, Dimitris; Bourantas, George C.; Nikiforidis, George

doi:10.1118/1.3673067

Cited by 104 publications

(105 citation statements)

References 41 publications

(57 reference statements)

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“…Como foi discutido anteriormente, a literatura mostra alguns enfoques computacionais para a segmentação do lúmen na coronária em imagens IOCT (Dubuisson et al, 2009;Gurmeric et al, 2009;Sihan et al, 2008;Tsantis et al, 2012;Tung et al, 2011). Contudo, apesar dos esforços, a ausência de um número razoável de métodos, e resultados mais acurados dos mesmos, faz com que a procura de métodos alternativos de segmentação da coronária continue.…”

Section: Discussionunclassified

“…Contudo, o método que utiliza afinidade Bhattacharyya apresenta menor FP e possui a vantagem de ser mais robusto, pois esse, não utiliza somente características da intensidade dos pixels (c, d), mais também características da região vizinha a cada pixel com seu kernel. Comparando com a literatura, Tsantis et al (2012) oferece um método de segmentação automática do lúmen para imagens da artéria femoral com um valor de overlap de 93,7 ± 4,5, eficácia quase 3% inferior ao da nossa proposta. Em Tung et al (2011) a segmentação automática do lúmen de coronárias é feita obtendo um valor médio de Overlap DICE de 97,25 ± 2,0, o qual é relevante, mas ainda inferior ao obtido neste trabalho.…”

Section: Discussionunclassified

“…Na técnica proposta por Sihan et al (2008), é empregado um pré-processamento com o filtro gaussiano e operações morfológicas para tirar o lúmen, um filtro de Canny (Canny, 1986) para detectar bordas e um filtro baseado em threshold para tirar as bordas que não pertencem ao lúmen. No artigo de Tsantis et al (2012), é aplicado um pré-processamento com a transformada de Hough e um modelo de Campos Aleatórios de Markov (Besag, 1986) para obter o lúmen em artéria femoral.…”

Section: Introductionunclassified

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Segmentação do lúmen em imagens de IOCT usando Fuzzy Connectedness e Reconstrução Binária Morfológica

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2013

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Segmentação do lúmen em imagens de IOCT usando Fuzzy Connectedness e Reconstrução Binária Morfológica

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2013

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“…1 Automated assessment can reduce this error and produce more consistent image interpretation. Various techniques have been reported for lumen segmentation, [1][2][3][4][5][6][7][8] stent strut detection, 7 and plaque characterization. 1,9 These approaches are either semi or fully automated.…”

Section: Introductionmentioning

confidence: 99%

Automatic segmentation of coronary morphology using transmittance-based lumen intensity-enhanced intravascular optical coherence tomography images and applying a localized level-set-based active contour method

2016

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, "Automatic segmentation of coronary morphology using transmittance-based lumen intensity-enhanced intravascular optical coherence tomography images and applying a localized level-set-based active contour method," J. Abstract. Lumen segmentation from clinical intravascular optical coherence tomography (IV-OCT) images has clinical relevance as it provides a full three-dimensional perspective of diseased coronary artery sections. Inaccurate segmentation may occur when there are artifacts in the image, resulting from issues such as inadequate blood clearance. This study proposes a transmittance-based lumen intensity enhancement method that ensures only lumen regions are highlighted. A level-set-based active contour method that utilizes the local speckle distribution properties of the image is then employed to drive an image-specific active contour toward the true lumen boundaries. By utilizing local speckle properties, the intensity variation issues within the image are resolved. This combined approach has been successfully applied to challenging clinical IV-OCT datasets that contains multiple lumens, residual blood flow, and its shadowing artifact. A method to identify the guide-wire and interpolate the lost lumen segments has been implemented. This approach is fast and can be performed even when guide-wire boundaries are not easily identified. Lumen enhancement also makes it easy to identify vessel side branches. This automated approach is not only able to extract the arterial lumen, but also the smaller microvascular lumens that are associated with the vasa vasorum and with atherosclerotic plaque.

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“…Initially, OCT lumen detection [6] was performed manually; a time consuming process sensitive to intra and inter observer variability. These drawbacks led to implementation of automated and semiautomated methodologies detecting only vascular lumen borders [7][8][9][10][11] or detecting lumen border and estimating vessel plaque area [12].…”

Section: Introductionmentioning

confidence: 99%

Fully automated lumen segmentation of intracoronary optical coherence tomography images

et al. 2017

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Optical coherence tomography (OCT) provides high-resolution cross-sectional images of arterial luminal morphology. Traditionally lumen segmentation of OCT images is performed manually by expert observers; a laborious, time consuming effort, sensitive to inter-observer variability process. Although several automated methods have been developed, the majority cannot be applied in real time because of processing demands.To address these limitations we propose a new method for rapid image segmentation of arterial lumen borders using OCT images that involves the following steps: 1) OCT image acquisition using the raw OCT data, 2) reconstruction of longitudinal cross-section (LOCS) images from four different acquisition angles, 3) segmentation of the LOCS images and 4) lumen contour construction in each 2D cross-sectional image.The efficiency of the developed method was evaluated using 613 annotated images from 10 OCT pullbacks acquired from 10 patients at the time of coronary arterial interventions. High Pearson's correlation coefficient was obtained when lumen areas detected by the method were compared to areas annotated by experts (r=0.98, R 2 =0.96); Bland-Altman analysis showed no significant bias with good limits of agreement.The proposed methodology permits reliable border detection especially in lumen areas having artifacts and is faster than traditional techniques making it capable of being used in real time applications. The method is likely to assist in a number of research and clinical applications -further testing in an expanded clinical arena will more fully define the limits and potential of this approach.

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Automatic vessel lumen segmentation and stent strut detection in intravascular optical coherence tomography

Cited by 104 publications

References 41 publications

Segmentação do lúmen em imagens de IOCT usando Fuzzy Connectedness e Reconstrução Binária Morfológica

Segmentação do lúmen em imagens de IOCT usando Fuzzy Connectedness e Reconstrução Binária Morfológica

Automatic segmentation of coronary morphology using transmittance-based lumen intensity-enhanced intravascular optical coherence tomography images and applying a localized level-set-based active contour method

Fully automated lumen segmentation of intracoronary optical coherence tomography images

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