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Cardiac computed tomography (CT) scanning is an advanced cardiac imaging technique that utilizes CT technology, with or without intravenous (IV) contrast, to produce accurate cross-sectional virtual slices of specific areas of the heart, coronary circulation, and major blood vessels such as the aorta, pulmonary veins, and arteries. The computer processes these slices to generate three-dimensional images. Multidetector CT (MDCT) is a rapid form of CT scanning that captures multiple slices...
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Longitudinal Morphological and Physiological Monitoring of Three-dimensional Tumor Spheroids Using Optical Coherence Tomography
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OCTOPUS - Optical coherence tomography plaque and stent analysis software.

Juhwan Lee1, Justin N Kim1, Yazan Gharaibeh2

  • 1Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA.

Heliyon
|February 23, 2023
PubMed
Summary
This summary is machine-generated.

The new OCTOPUS software offers automated analysis of intravascular optical coherence tomography (IVOCT) images, significantly reducing manual editing time for coronary plaque and stent assessment in research studies.

Keywords:
Deep learningMachine learningOCTOPUSOptical coherence tomographyPlaque characterizationStent deployment analysis

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Area of Science:

  • Cardiovascular Imaging
  • Medical Software Development
  • Interventional Cardiology

Background:

  • Intravascular optical coherence tomography (IVOCT) is crucial for guiding percutaneous coronary interventions and characterizing plaque.
  • Existing analysis methods for IVOCT images are often manual and time-consuming.
  • There is a need for automated tools to enhance efficiency and accuracy in IVOCT analysis.

Purpose of the Study:

  • To develop and evaluate the Optical Coherence TOmography PlaqUe and Stent (OCTOPUS) analysis software.
  • To provide highly automated and comprehensive analysis of coronary plaques and stents in IVOCT images.
  • To reduce the time and effort required for IVOCT image analysis in research settings.

Main Methods:

  • Developed OCTOPUS software incorporating deep learning for plaque segmentation and machine learning for stent strut identification.
  • Integrated pre-processing, registration of pullbacks, and interactive visualization with manual editing capabilities.
  • Quantified stent deployment, strut characteristics, and calcium properties, enabling sequential comparisons of vessel segments.

Main Results:

  • OCTOPUS demonstrated high accuracy in plaque segmentation (86.2% sensitivity, 0.781 F1 score) and stent strut classification (>90% sensitivity/precision).
  • Automated analysis required minimal manual touch-up (13% lumen, 23% calcification), reducing editing time by approximately 80%.
  • Successfully applied OCTOPUS for analyzing plaque progression and evaluating stent deployment in complex cases.

Conclusions:

  • OCTOPUS is a highly automated software package for quantitative plaque and stent analysis in IVOCT images.
  • The software significantly reduces analysis time and improves accuracy for IVOCT research.
  • OCTOPUS algorithms hold potential for real-time treatment planning in interventional cardiology.