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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...

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Autores principales: Lee, Juhwan, Kim, Justin N., Gomez-Perez, Lia, Gharaibeh, Yazan, Motairek, Issam, Pereira, Gabriel T. R., Zimin, Vladislav N., Dallan, Luis A. P., Hoori, Ammar, Al-Kindi, Sadeer, Guagliumi, Giulio, Bezerra, Hiram G., Wilson, David L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9687448/
https://www.ncbi.nlm.nih.gov/pubmed/36354559
http://dx.doi.org/10.3390/bioengineering9110648
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author Lee, Juhwan
Kim, Justin N.
Gomez-Perez, Lia
Gharaibeh, Yazan
Motairek, Issam
Pereira, Gabriel T. R.
Zimin, Vladislav N.
Dallan, Luis A. P.
Hoori, Ammar
Al-Kindi, Sadeer
Guagliumi, Giulio
Bezerra, Hiram G.
Wilson, David L.
author_facet Lee, Juhwan
Kim, Justin N.
Gomez-Perez, Lia
Gharaibeh, Yazan
Motairek, Issam
Pereira, Gabriel T. R.
Zimin, Vladislav N.
Dallan, Luis A. P.
Hoori, Ammar
Al-Kindi, Sadeer
Guagliumi, Giulio
Bezerra, Hiram G.
Wilson, David L.
author_sort Lee, Juhwan
collection PubMed
description 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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spelling pubmed-96874482022-11-25 Automated Segmentation of Microvessels in Intravascular OCT Images Using Deep Learning Lee, Juhwan Kim, Justin N. Gomez-Perez, Lia Gharaibeh, Yazan Motairek, Issam Pereira, Gabriel T. R. Zimin, Vladislav N. Dallan, Luis A. P. Hoori, Ammar Al-Kindi, Sadeer Guagliumi, Giulio Bezerra, Hiram G. Wilson, David L. Bioengineering (Basel) Article 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. MDPI 2022-11-03 /pmc/articles/PMC9687448/ /pubmed/36354559 http://dx.doi.org/10.3390/bioengineering9110648 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Lee, Juhwan
Kim, Justin N.
Gomez-Perez, Lia
Gharaibeh, Yazan
Motairek, Issam
Pereira, Gabriel T. R.
Zimin, Vladislav N.
Dallan, Luis A. P.
Hoori, Ammar
Al-Kindi, Sadeer
Guagliumi, Giulio
Bezerra, Hiram G.
Wilson, David L.
Automated Segmentation of Microvessels in Intravascular OCT Images Using Deep Learning
title Automated Segmentation of Microvessels in Intravascular OCT Images Using Deep Learning
title_full Automated Segmentation of Microvessels in Intravascular OCT Images Using Deep Learning
title_fullStr Automated Segmentation of Microvessels in Intravascular OCT Images Using Deep Learning
title_full_unstemmed Automated Segmentation of Microvessels in Intravascular OCT Images Using Deep Learning
title_short Automated Segmentation of Microvessels in Intravascular OCT Images Using Deep Learning
title_sort automated segmentation of microvessels in intravascular oct images using deep learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9687448/
https://www.ncbi.nlm.nih.gov/pubmed/36354559
http://dx.doi.org/10.3390/bioengineering9110648
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