  Compared with other imaging modalities, intravascular optical coherence
tomography (IVOCT) has significant advantages for guiding percutaneous coronary
interventions. To aid IVOCT research studies, we developed the Optical
Coherence TOmography PlaqUe and Stent (OCTOPUS) analysis software. 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. Interactive
visualization and manual editing of segmentations were included in the
software. Quantifications include stent deployment characteristics (e.g., 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. 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 seconds/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 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.
