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Philips Healthcare
vascular plaque quantification software Vascular Plaque Quantification Software, supplied by Philips Healthcare, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more https://www.bioz.com/product/vascular+plaque+quantification+software/philips+qlab+software/pm41813269-5-11-18 Average 86 stars, based on 1 article reviews
vascular plaque quantification software - by Bioz Stars,
2026-09
86/100 stars
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Abbott Laboratories
ai powered ivoct plaque characterization software  Ai Powered Ivoct Plaque Characterization Software, supplied by Abbott Laboratories, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more https://www.bioz.com/product/vascular+plaque+quantification+software/ai+characterization+ivoct+plaque+powered+software/pmc12341395-373-5-14 Average 86 stars, based on 1 article reviews
ai powered ivoct plaque characterization software - by Bioz Stars,
2026-09
86/100 stars
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Buy from Supplier |
Image Search Results
Journal: IEEE reviews in biomedical engineering
Article Title: Computational Analysis of Intravascular OCT Images for Future Clinical Support: A Comprehensive Review
doi: 10.1109/RBME.2025.3530244
Figure Lengend Snippet: Diagram illustrating the computational analysis of IVOCT for future clinical support.
Article Snippet: The recent FDA approval of
Techniques:
Journal: IEEE reviews in biomedical engineering
Article Title: Computational Analysis of Intravascular OCT Images for Future Clinical Support: A Comprehensive Review
doi: 10.1109/RBME.2025.3530244
Figure Lengend Snippet: Successful pixel-wise classification examples from certain datasets. column-(a) IVOCT image, column-(b) ground-truth, and column-(c) illustrates the segmentation results obtained with our proposed deep learning neural network. The annotation colors for each tissue is denoted at the bottom of the resulting plane.
Article Snippet: The recent FDA approval of
Techniques:
Journal: IEEE reviews in biomedical engineering
Article Title: Computational Analysis of Intravascular OCT Images for Future Clinical Support: A Comprehensive Review
doi: 10.1109/RBME.2025.3530244
Figure Lengend Snippet: Three-dimensional (3D) visualizations of fibrous cap thickness on the representative IVOCT pullbacks, including: (a) short lesion with TCFA, (b) long lesion with TCFA, (c) short lesion without TCFA, and (d) long lesion without TCFA. The reader can zoom in each artery to see variations of fibrous cap thickness. (a) Although the lesion length was not too long (< 7 mm), the average fibrous cap thickness was less than 65 μ m across the lesion indicating that the lesion is prone to rupture. (b) There were two lipidous lesions having 15 mm (left) and 5 mm (right) lengths. Both lesions were heavily lipidic with a mean cap thickness of < 65 μ m. The artery was much more prone to rupture than (a). (c) The lesion was stable, since the length was short (< 3 mm) and the fibrous cap thickness was always greater than 150 μ m. (d) Although the fibrous cap thickness was always over 80 μ m across the lesion, the lesion length was very long (> 30 mm). There were several spots approaching toward the vulnerable plaque than (c). The color map visualizes the fibrous cap in the range of 0 to 300 μ m. The yellow arrows indicate representative IVOCT frames of each rendering. Our method provides comprehensive fibrous cap map in the entire IVOCT pullback, so clinicians can make appropriate treatment decisions.
Article Snippet: The recent FDA approval of
Techniques: