10 research outputs found
Deep learning segmentation of fibrous cap in intravascular optical coherence tomography images
Thin-cap fibroatheroma (TCFA) is a prominent risk factor for plaque rupture.
Intravascular optical coherence tomography (IVOCT) enables identification of
fibrous cap (FC), measurement of FC thicknesses, and assessment of plaque
vulnerability. We developed a fully-automated deep learning method for FC
segmentation. This study included 32,531 images across 227 pullbacks from two
registries. Images were semi-automatically labeled using our OCTOPUS with
expert editing using established guidelines. We employed preprocessing
including guidewire shadow detection, lumen segmentation, pixel-shifting, and
Gaussian filtering on raw IVOCT (r,theta) images. Data were augmented in a
natural way by changing theta in spiral acquisitions and by changing intensity
and noise values. We used a modified SegResNet and comparison networks to
segment FCs. We employed transfer learning from our existing much larger,
fully-labeled calcification IVOCT dataset to reduce deep-learning training.
Overall, our method consistently delivered better FC segmentation results
(Dice: 0.837+/-0.012) than other deep-learning methods. Transfer learning
reduced training time by 84% and reduced the need for more training samples.
Our method showed a high level of generalizability, evidenced by
highly-consistent segmentations across five-fold cross-validation (sensitivity:
85.0+/-0.3%, Dice: 0.846+/-0.011) and the held-out test (sensitivity: 84.9%,
Dice: 0.816) sets. In addition, we found excellent agreement of FC thickness
with ground truth (2.95+/-20.73 um), giving clinically insignificant bias.
There was excellent reproducibility in pre- and post-stenting pullbacks
(average FC angle: 200.9+/-128.0 deg / 202.0+/-121.1 deg). Our method will be
useful for multiple research purposes and potentially for planning stent
deployments that avoid placing a stent edge over an FC.Comment: 24 pages, 9 figures, 2 tables, 2 supplementary figures, 3
supplementary table
Automated analysis of fibrous cap in intravascular optical coherence tomography images of coronary arteries
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 4,360
IVOCT image frames of 77 lesions among 41 patients. To improve segmentation
performance, preprocessing included lumen segmentation, pixel-shifting, and
noise filtering on the raw polar (r, theta) 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
degree; mean 196 degree). 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 micron; mean 175 micron), 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.Comment: 18 pages, 9 figure
Neoatherosclerosis prediction using plaque markers in intravascular optical coherence tomography images
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
Modeling the coherence of the financial system and economy of Russia
The article assesses the coherence of the financial system and economy of Russia, and also reveals the uncertainties in interrelation between the flow of finance and economic activity. The work reveals the possibilities of ensuring preservation of the form and content of economic system in the process of creating and using monetary funds. An analysis of the actual change of pace in the growth rate of finances and economic activity was carried out to eliminate the uncertainty of interrelation between the flow of finance and economic activity. In the result of the analysis, it was revealed that financial and economic measures are resorted to in order to eliminate uncertainty, and thereby, to ensure the preservation of coherence of the financial system of the modern Russian economy. Financial measures include an increase in gold reserves and monetary base; economic measures include an increase in the production of crude oil and natural gas and petroleum (associated) gas, extraction and dressing of iron ores. As a result of modeling the dynamics of the financial and economic systems with application of the modified Cobb-Douglas formula it is revealed that the financial system actively follows the dynamics of the economic system and there is sufficient compensating reaction of the former.Coherence of the Russian financial system and economy determines the capability and ability to maintain the form and content of the Russian economy with the help of finance. Elimination of the uncertainty of interrelation between the flow of finance and economic activity is confirmed by active following of the Russian financial system in path of the economic system dynamics and compensating reaction of the financial system
Fractional Flow Reserve (FFR) Estimation from OCT-Based CFD Simulations: Role of Side Branches
The computational fluid dynamic method has been widely used to quantify the hemodynamic alterations in a diseased artery and investigate surgery outcomes. The artery model reconstructed based on optical coherence tomography (OCT) images generally does not include the side branches. However, the side branches may significantly affect the hemodynamic assessment in a clinical setting, i.e., the fractional flow reserve (FFR), defined as the ratio of mean distal coronary pressure to mean aortic pressure. In this work, the effect of the side branches on FFR estimation was inspected with both idealized and optical coherence tomography (OCT)-reconstructed coronary artery models. The electrical analogy of blood flow was further used to understand the impact of the side branches (diameter and location) on FFR estimation. Results have shown that the side branches decrease the total resistance of the vessel tree, resulting in a higher inlet flowrate. The side branches located at the downstream of the stenosis led to a lower FFR value, while the ones at the upstream had a minimal impact on the FFR estimation. Side branches with a diameter larger than one third of the main vessel diameter are suggested to be considered for a proper FFR estimation. The findings in this study could be extended to other coronary artery imaging modalities and facilitate treatment planning
Deep learning segmentation of fibrous cap in intravascular optical coherence tomography images
Abstract Thin-cap fibroatheroma (TCFA) is a prominent risk factor for plaque rupture. Intravascular optical coherence tomography (IVOCT) enables identification of fibrous cap (FC), measurement of FC thicknesses, and assessment of plaque vulnerability. We developed a fully-automated deep learning method for FC segmentation. This study included 32,531 images across 227 pullbacks from two registries (TRANSFORM-OCT and UHCMC). Images were semi-automatically labeled using our OCTOPUS with expert editing using established guidelines. We employed preprocessing including guidewire shadow detection, lumen segmentation, pixel-shifting, and Gaussian filtering on raw IVOCT (r,θ) images. Data were augmented in a natural way by changing θ in spiral acquisitions and by changing intensity and noise values. We used a modified SegResNet and comparison networks to segment FCs. We employed transfer learning from our existing much larger, fully-labeled calcification IVOCT dataset to reduce deep-learning training. Postprocessing with a morphological operation enhanced segmentation performance. Overall, our method consistently delivered better FC segmentation results (Dice: 0.837 ± 0.012) than other deep-learning methods. Transfer learning reduced training time by 84% and reduced the need for more training samples. Our method showed a high level of generalizability, evidenced by highly-consistent segmentations across five-fold cross-validation (sensitivity: 85.0 ± 0.3%, Dice: 0.846 ± 0.011) and the held-out test (sensitivity: 84.9%, Dice: 0.816) sets. In addition, we found excellent agreement of FC thickness with ground truth (2.95 ± 20.73 µm), giving clinically insignificant bias. There was excellent reproducibility in pre- and post-stenting pullbacks (average FC angle: 200.9 ± 128.0°/202.0 ± 121.1°). Our fully automated, deep-learning FC segmentation method demonstrated excellent performance, generalizability, and reproducibility on multi-center datasets. It will be useful for multiple research purposes and potentially for planning stent deployments that avoid placing a stent edge over an FC
Degradation modeling of poly-l-lactide acid (PLLA) bioresorbable vascular scaffold within a coronary artery
In this work, a strain-based degradation model was implemented and validated to better understand the dynamic interactions between the bioresorbable vascular scaffold (BVS) and the artery during the degradation process. Integrating the strain-modulated degradation equation into commercial finite element codes allows a better control and visualization of local mechanical parameters. Both strut thinning and discontinuity of the stent struts within an artery were captured and visualized. The predicted results in terms of mass loss and fracture locations were validated by the documented experimental observations. In addition, results suggested that the heterogeneous degradation of the stent depends on its strain distribution following deployment. Degradation is faster at the locations with higher strains and resulted in the strut thinning and discontinuity, which contributes to the continuous mass loss, and the reduced contact force between the BVS and artery. A nonlinear relationship between the maximum principal strain of the stent and the fracture time was obtained, which could be transformed to predict the degradation process of the BVS in different mechanical environments. The developed computational model provided more insights into the degradation process, which could complement the discrete experimental data for improving the design and clinical management of the BVS
Automated Segmentation of Microvessels in Intravascular OCT Images Using Deep Learning
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
Prediction of stent under-expansion in calcified coronary arteries using machine-learning on intravascular optical coherence tomography
BACKGROUND Careful evaluation of the risk of stent under-expansions before
the intervention will aid treatment planning, including the application of a
pre-stent plaque modification strategy.
OBJECTIVES It remains challenging to achieve a proper stent expansion in the
presence of severely calcified coronary lesions. Building on our work in deep
learning segmentation, we created an automated machine learning approach that
uses lesion attributes to predict stent under-expansion from pre-stent images,
suggesting the need for plaque modification.
METHODS Pre- and post-stent intravascular optical coherence tomography image
data were obtained from 110 coronary lesions. Lumen and calcifications in
pre-stent images were segmented using deep learning, and numerous features per
lesion were extracted. We analyzed stent expansion along the lesion, enabling
frame, segmental, and whole-lesion analyses. We trained regression models to
predict the poststent lumen area and then to compute the stent expansion index
(SEI). Stents with an SEI /= 80% were classified as "under-expanded" and
"well-expanded," respectively.
RESULTS Best performance (root-mean-square-error = 0.04+/-0.02 mm2, r =
0.94+/-0.04, p < 0.0001) was achieved when we used features from both the lumen
and calcification to train a Gaussian regression model for a segmental analysis
over a segment length of 31 frames. Under-expansion classification results
(AUC=0.85+/-0.02) were significantly improved over other approaches.
CONCLUSIONS We used calcifications and lumen features to identify lesions at
risk of stent under-expansion. Results suggest that the use of pre-stent images
can inform physicians of the need to apply plaque modification approaches.Comment: 25 pages, 7 figures, 1 table, 6 supplemental figures, 3 supplemental
table