OCTOPUS -- optical coherence tomography plaque and stent analysis software

Lee, Juhwan; Kim, Justin N.; Gharaibeh, Yazan; Zimin, Vladislav N.; Dallan, Luís Augusto Palma; Pereira, Gabriel Tensol Rodrigues; Vergara-Martel, Armando; Kolluru, Chaitanya; Hoori, Ammar; Bezerra, Hiram G.; Wilson, David L.

doi:10.48550/arxiv.2204.10212

Cited by 4 publications

(11 citation statements)

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“…The manual analysis of microvessels in IVOCT images is challenging, highlighting the importance of an automated image analysis method. Building on our previous studies of IVOCT image analysis [6][7][8][9][18][19][20][21][22][23][24][25], we developed an automated method using deep learning. The main findings of this study can be summarized as follows: (1) We found very high intra-observer agreement in our manual analysis, indicating that our labels are reasonably accurate.…”

Section: Discussionmentioning

confidence: 99%

“…Ideally, the proposed method can process an entire pullback of 375 frames in 60 s. However, processing lesions (segments) after specifying ROIs will be the preferred solution to reduce potential false positives from a whole pullback. The algorithm is simple to implement in IVOCT image analysis software (e.g., our OCTOPUS software [ 6 ]), where starting and ending frames for the analysis can be specified. Our method would be suitable for a wide range of clinical research projects, particularly those involving a large number of patients.…”

Section: Discussionmentioning

confidence: 99%

“…Manual annotation was carried out using Optical Coherence Tomography Plaque and Stent (OCTOPUS) software previously developed by our group [ 6 ]. An expert cardiologist (10+ years of experience) manually labeled each image from the Cardiovascular Imaging Core Laboratory, University Hospitals Cleveland Medical Center, a leading IVOCT analysis laboratory with over 3000 clinical trial cases analyzed.…”

Section: Methodsmentioning

confidence: 99%

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Automated Segmentation of Microvessels in Intravascular OCT Images Using Deep Learning

et al. 2022

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Microvessels in vascular plaque are associated with plaque progression and are found in plaque rupture and intra-plaque hemorrhage. To analyze this characteristic of vulnerability, we developed an automated deep learning method for detecting microvessels in intravascular optical coherence tomography (IVOCT) images. A total of 8403 IVOCT image frames from 85 lesions and 37 normal segments were analyzed. Manual annotation was performed using a dedicated software (OCTOPUS) previously developed by our group. Data augmentation in the polar (r,θ) domain was applied to raw IVOCT images to ensure that microvessels appear at all possible angles. Pre-processing methods included guidewire/shadow detection, lumen segmentation, pixel shifting, and noise reduction. DeepLab v3+ was used to segment microvessel candidates. A bounding box on each candidate was classified as either microvessel or non-microvessel using a shallow convolutional neural network. For better classification, we used data augmentation (i.e., angle rotation) on bounding boxes with a microvessel during network training. Data augmentation and pre-processing steps improved microvessel segmentation performance significantly, yielding a method with Dice of 0.71 ± 0.10 and pixel-wise sensitivity/specificity of 87.7 ± 6.6%/99.8 ± 0.1%. The network for classifying microvessels from candidates performed exceptionally well, with sensitivity of 99.5 ± 0.3%, specificity of 98.8 ± 1.0%, and accuracy of 99.1 ± 0.5%. The classification step eliminated the majority of residual false positives and the Dice coefficient increased from 0.71 to 0.73. In addition, our method produced 698 image frames with microvessels present, compared with 730 from manual analysis, representing a 4.4% difference. When compared with the manual method, the automated method improved microvessel continuity, implying improved segmentation performance. The method will be useful for research purposes as well as potential future treatment planning.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Automated Segmentation of Microvessels in Intravascular OCT Images Using Deep Learning

et al. 2022

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“…Given that IVOCT provides better resolution of the neointima and neoatherosclerotic plaque characteristics than any other imaging modality, we investigated the value of quantitative IVOCT measurements before stent implantation for predicting post-stent neoatherosclerosis. Building on our previous work in the analysis of atherosclerotic plaque (38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48)(49)(50)(51), we used a number of computational tools (i.e., machine learning, deep learning, and statistical modeling) to evaluate serial IVOCT images collected as part of a large clinical study. Using a dedicated software (OCTOPUS) (38), we performed plaque characterization in the baseline IVOCT images, computed IVOCT plaque features, and determined their association with the development of neoatherosclerosis after stent implantation.…”

Section: Introductionmentioning

confidence: 99%

“…Building on our previous work in the analysis of atherosclerotic plaque (38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48)(49)(50)(51), we used a number of computational tools (i.e., machine learning, deep learning, and statistical modeling) to evaluate serial IVOCT images collected as part of a large clinical study. Using a dedicated software (OCTOPUS) (38), we performed plaque characterization in the baseline IVOCT images, computed IVOCT plaque features, and determined their association with the development of neoatherosclerosis after stent implantation. examination were eligible for the study.…”

Section: Introductionmentioning

confidence: 99%

Neoatherosclerosis prediction using plaque markers in intravascular optical coherence tomography images

Lee

Pereira²,

Motairek³

et al. 2022

Front. Cardiovasc. Med.

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IntroductionIn-stent neoatherosclerosis has emerged as a crucial factor in post-stent complications including late in-stent restenosis and very late stent thrombosis. In this study, we investigated the ability of quantitative plaque characteristics from intravascular optical coherence tomography (IVOCT) images taken just prior to stent implantation to predict neoatherosclerosis after implantation.MethodsThis was a sub-study of the TRiple Assessment of Neointima Stent FOrmation to Reabsorbable polyMer with Optical Coherence Tomography (TRANSFORM-OCT) trial. Images were obtained before and 18 months after stent implantation. Final analysis included images of 180 lesions from 90 patients; each patient had images of two lesions in different coronary arteries. A total of 17 IVOCT plaque features, including lesion length, lumen (e.g., area and diameter); calcium (e.g., angle and thickness); and fibrous cap (FC) features (e.g., thickness, surface area, and burden), were automatically extracted from the baseline IVOCT images before stenting using dedicated software developed by our group (OCTOPUS). The predictive value of baseline IVOCT plaque features for neoatherosclerosis development after stent implantation was assessed using univariate/multivariate logistic regression and receiver operating characteristic (ROC) analyses.ResultsFollow-up IVOCT identified stents with (n = 19) and without (n = 161) neoatherosclerosis. Greater lesion length and maximum calcium angle and features related to FC were associated with a higher prevalence of neoatherosclerosis after stent implantation (p < 0.05). Hierarchical clustering identified six clusters with the best prediction p-values. In univariate logistic regression analysis, maximum calcium angle, minimum calcium thickness, maximum FC angle, maximum FC area, FC surface area, and FC burden were significant predictors of neoatherosclerosis. Lesion length and features related to the lumen were not significantly different between the two groups. In multivariate logistic regression analysis, only larger FC surface area was strongly associated with neoatherosclerosis (odds ratio 1.38, 95% confidence interval [CI] 1.05–1.80, p < 0.05). The area under the ROC curve was 0.901 (95% CI 0.859–0.946, p < 0.05) for FC surface area.ConclusionPost-stent neoatherosclerosis can be predicted by quantitative IVOCT imaging of plaque characteristics prior to stent implantation. Our findings highlight the additional clinical benefits of utilizing IVOCT imaging in the catheterization laboratory to inform treatment decision-making and improve outcomes.

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Automated analysis of fibrous cap in intravascular optical coherence tomography images of coronary arteries

Lee

Pereira

Gharaibeh

et al. 2022

Sci Rep

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Thin-cap fibroatheroma (TCFA) and plaque rupture have been recognized as the most frequent risk factor for thrombosis and acute coronary syndrome. Intravascular optical coherence tomography (IVOCT) can identify TCFA and assess cap thickness, which provides an opportunity to assess plaque vulnerability. We developed an automated method that can detect lipidous plaque and assess fibrous cap thickness in IVOCT images. This study analyzed a total of 4360 IVOCT image frames of 77 lesions among 41 patients. Expert cardiologists manually labeled lipidous plaque based on established criteria. To improve segmentation performance, preprocessing included lumen segmentation, pixel-shifting, and noise filtering on the raw polar (r, θ) IVOCT images. We used the DeepLab-v3 plus deep learning model to classify lipidous plaque pixels. After lipid detection, we automatically detected the outer border of the fibrous cap using a special dynamic programming algorithm and assessed the cap thickness. Our method provided excellent discriminability of lipid plaque with a sensitivity of 85.8% and A-line Dice coefficient of 0.837. By comparing lipid angle measurements between two analysts following editing of our automated software, we found good agreement by Bland–Altman analysis (difference 6.7° ± 17°; mean ~ 196°). Our method accurately detected the fibrous cap from the detected lipid plaque. Automated analysis required a significant modification for only 5.5% frames. Furthermore, our method showed a good agreement of fibrous cap thickness between two analysts with Bland–Altman analysis (4.2 ± 14.6 µm; mean ~ 175 µm), indicating little bias between users and good reproducibility of the measurement. We developed a fully automated method for fibrous cap quantification in IVOCT images, resulting in good agreement with determinations by analysts. The method has great potential to enable highly automated, repeatable, and comprehensive evaluations of TCFAs.

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OCTOPUS -- optical coherence tomography plaque and stent analysis software

Cited by 4 publications

References 0 publications

Automated Segmentation of Microvessels in Intravascular OCT Images Using Deep Learning

Automated Segmentation of Microvessels in Intravascular OCT Images Using Deep Learning

Neoatherosclerosis prediction using plaque markers in intravascular optical coherence tomography images

Automated analysis of fibrous cap in intravascular optical coherence tomography images of coronary arteries

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