OCTOPUS – Optical coherence tomography plaque and stent analysis software
Background and objective: Compared with other imaging modalities, intravascular optical coherence tomography (IVOCT) has significant advantages for guiding percutaneous coronary interventions, assessing their outcomes, and characterizing plaque components. To aid IVOCT research studies, we developed...
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| Format: | Article |
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Elsevier
2023-02-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844023006035 |
| _version_ | 1811161772445401088 |
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| author | Juhwan Lee Justin N. Kim Yazan Gharaibeh Vladislav N. Zimin Luis A.P. Dallan Gabriel T.R. Pereira Armando Vergara-Martel Chaitanya Kolluru Ammar Hoori Hiram G. Bezerra David L. Wilson |
| author_facet | Juhwan Lee Justin N. Kim Yazan Gharaibeh Vladislav N. Zimin Luis A.P. Dallan Gabriel T.R. Pereira Armando Vergara-Martel Chaitanya Kolluru Ammar Hoori Hiram G. Bezerra David L. Wilson |
| author_sort | Juhwan Lee |
| collection | DOAJ |
| description | Background and objective: Compared with other imaging modalities, intravascular optical coherence tomography (IVOCT) has significant advantages for guiding percutaneous coronary interventions, assessing their outcomes, and characterizing plaque components. To aid IVOCT research studies, we developed the Optical Coherence TOmography PlaqUe and Stent (OCTOPUS) analysis software, which provides highly automated, comprehensive analysis of coronary plaques and stents in IVOCT images. Methods: User specifications for OCTOPUS were obtained from detailed, iterative discussions with IVOCT analysts in the Cardiovascular Imaging Core Laboratory at University Hospitals Cleveland Medical Center, a leading laboratory for IVOCT image analysis. To automate image analysis results, the software includes several important algorithmic steps: pre-processing, deep learning plaque segmentation, machine learning identification of stent struts, and registration of pullbacks for sequential comparisons. Intuitive, interactive visualization and manual editing of segmentations were included in the software. Quantifications include stent deployment characteristics (e.g., stent area and stent strut malapposition), strut level analysis, calcium angle, and calcium thickness measurements. Interactive visualizations include (x,y) anatomical, en face, and longitudinal views with optional overlays (e.g., segmented calcifications). To compare images over time, linked visualizations were enabled to display up to four registered vessel segments at a time. Results: OCTOPUS has been deployed for nearly 1 year and is currently being used in multiple IVOCT studies. Underlying plaque segmentation algorithm yielded excellent pixel-wise results (86.2% sensitivity and 0.781 F1 score). Using OCTOPUS on 34 new pullbacks, we determined that following automated segmentation, only 13% and 23% of frames needed any manual touch up for detailed lumen and calcification labeling, respectively. Only up to 3.8% of plaque pixels were modified, leading to an average editing time of only 7.5 s/frame, an approximately 80% reduction compared to manual analysis. Regarding stent analysis, sensitivity and precision were both greater than 90%, and each strut was successfully classified as either covered or uncovered with high sensitivity (94%) and specificity (90%). We demonstrated use cases for sequential analysis. To analyze plaque progression, we loaded multiple pullbacks acquired at different points (e.g., pre-stent, 3-month follow-up, and 18-month follow-up) and evaluated frame-level development of in-stent neo-atherosclerosis. In ex vivo cadaver experiments, the OCTOPUS software enabled visualization and quantitative evaluation of irregular stent deployment in the presence of calcifications identified in pre-stent images. Conclusions: We introduced and evaluated the clinical application of a highly automated software package, OCTOPUS, for quantitative plaque and stent analysis in IVOCT images. The software is currently used as an offline tool for research purposes; however, the software's embedded algorithms may also be useful for real-time treatment planning. |
| first_indexed | 2024-04-10T06:19:38Z |
| format | Article |
| id | doaj.art-8bc7ce4fbd53402da56c804e5d6276d7 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-04-10T06:19:38Z |
| publishDate | 2023-02-01 |
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| spelling | doaj.art-8bc7ce4fbd53402da56c804e5d6276d72023-03-02T05:01:23ZengElsevierHeliyon2405-84402023-02-0192e13396OCTOPUS – Optical coherence tomography plaque and stent analysis softwareJuhwan Lee0Justin N. Kim1Yazan Gharaibeh2Vladislav N. Zimin3Luis A.P. Dallan4Gabriel T.R. Pereira5Armando Vergara-Martel6Chaitanya Kolluru7Ammar Hoori8Hiram G. Bezerra9David L. Wilson10Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106, USADepartment of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106, USADepartment of Biomedical Engineering, Faculty of Engineering, The Hashemite University, Zarqa, 13133, JordanCardiovascular Imaging Core Laboratory, Harrington Heart and Vascular Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, 44106, USACardiovascular Imaging Core Laboratory, Harrington Heart and Vascular Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, 44106, USACardiovascular Imaging Core Laboratory, Harrington Heart and Vascular Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, 44106, USACardiovascular Imaging Core Laboratory, Harrington Heart and Vascular Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, 44106, USADepartment of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106, USADepartment of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106, USAInterventional Cardiology Center, Heart and Vascular Institute, University of South Florida, Tampa, FL, 33606, USADepartment of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA; Case Western Reserve University, Department of Radiology, Cleveland, OH, 44106, USA; Corresponding author. Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA.Background and objective: Compared with other imaging modalities, intravascular optical coherence tomography (IVOCT) has significant advantages for guiding percutaneous coronary interventions, assessing their outcomes, and characterizing plaque components. To aid IVOCT research studies, we developed the Optical Coherence TOmography PlaqUe and Stent (OCTOPUS) analysis software, which provides highly automated, comprehensive analysis of coronary plaques and stents in IVOCT images. Methods: User specifications for OCTOPUS were obtained from detailed, iterative discussions with IVOCT analysts in the Cardiovascular Imaging Core Laboratory at University Hospitals Cleveland Medical Center, a leading laboratory for IVOCT image analysis. To automate image analysis results, the software includes several important algorithmic steps: pre-processing, deep learning plaque segmentation, machine learning identification of stent struts, and registration of pullbacks for sequential comparisons. Intuitive, interactive visualization and manual editing of segmentations were included in the software. Quantifications include stent deployment characteristics (e.g., stent area and stent strut malapposition), strut level analysis, calcium angle, and calcium thickness measurements. Interactive visualizations include (x,y) anatomical, en face, and longitudinal views with optional overlays (e.g., segmented calcifications). To compare images over time, linked visualizations were enabled to display up to four registered vessel segments at a time. Results: OCTOPUS has been deployed for nearly 1 year and is currently being used in multiple IVOCT studies. Underlying plaque segmentation algorithm yielded excellent pixel-wise results (86.2% sensitivity and 0.781 F1 score). Using OCTOPUS on 34 new pullbacks, we determined that following automated segmentation, only 13% and 23% of frames needed any manual touch up for detailed lumen and calcification labeling, respectively. Only up to 3.8% of plaque pixels were modified, leading to an average editing time of only 7.5 s/frame, an approximately 80% reduction compared to manual analysis. Regarding stent analysis, sensitivity and precision were both greater than 90%, and each strut was successfully classified as either covered or uncovered with high sensitivity (94%) and specificity (90%). We demonstrated use cases for sequential analysis. To analyze plaque progression, we loaded multiple pullbacks acquired at different points (e.g., pre-stent, 3-month follow-up, and 18-month follow-up) and evaluated frame-level development of in-stent neo-atherosclerosis. In ex vivo cadaver experiments, the OCTOPUS software enabled visualization and quantitative evaluation of irregular stent deployment in the presence of calcifications identified in pre-stent images. Conclusions: We introduced and evaluated the clinical application of a highly automated software package, OCTOPUS, for quantitative plaque and stent analysis in IVOCT images. The software is currently used as an offline tool for research purposes; however, the software's embedded algorithms may also be useful for real-time treatment planning.http://www.sciencedirect.com/science/article/pii/S2405844023006035OCTOPUSOptical coherence tomographyPlaque characterizationStent deployment analysisDeep learningMachine learning |
| spellingShingle | Juhwan Lee Justin N. Kim Yazan Gharaibeh Vladislav N. Zimin Luis A.P. Dallan Gabriel T.R. Pereira Armando Vergara-Martel Chaitanya Kolluru Ammar Hoori Hiram G. Bezerra David L. Wilson OCTOPUS – Optical coherence tomography plaque and stent analysis software Heliyon OCTOPUS Optical coherence tomography Plaque characterization Stent deployment analysis Deep learning Machine learning |
| title | OCTOPUS – Optical coherence tomography plaque and stent analysis software |
| title_full | OCTOPUS – Optical coherence tomography plaque and stent analysis software |
| title_fullStr | OCTOPUS – Optical coherence tomography plaque and stent analysis software |
| title_full_unstemmed | OCTOPUS – Optical coherence tomography plaque and stent analysis software |
| title_short | OCTOPUS – Optical coherence tomography plaque and stent analysis software |
| title_sort | octopus optical coherence tomography plaque and stent analysis software |
| topic | OCTOPUS Optical coherence tomography Plaque characterization Stent deployment analysis Deep learning Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2405844023006035 |
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