Comprehensive Assessment of Coronary Calcification in Intravascular OCT Using a Spatial-Temporal Encoder-Decoder...

Area of Science:

• Cardiovascular Imaging
• Artificial Intelligence in Medicine
• Medical Image Analysis

Background:

• Coronary calcification is a critical marker for coronary artery disease and impacts percutaneous coronary intervention outcomes.
• Accurate segmentation and quantification of coronary calcification are essential for effective treatment planning.

Purpose of the Study:

• To develop and validate a fully automated method for segmenting and quantifying coronary calcification in intravascular optical coherence tomography (IVOCT) images using deep learning.
• To improve the accuracy and robustness of coronary calcification assessment for better clinical decision-making.

Main Methods:

• A spatial-temporal encoder-decoder convolutional neural network (CNN) was employed for plaque segmentation, leveraging 3D continuity.
• A DenseNet classifier was used to reduce false positives, and a novel data augmentation technique was introduced.
• Clinically relevant metrics (area, depth, angle, thickness, volume, stent-deployment score) were automatically computed.

Main Results:

• The proposed automated method demonstrated superior performance compared to existing state-of-the-art 2D and 3D CNN techniques.
• Data augmentation significantly improved calcification segmentation (Dice similarity coefficient from 0.615 to 0.756), reaching human-level agreement.
• The region-based classifier enhanced precision (0.964) and F1-score (0.883) for calcification classification.

Conclusions:

• The developed automated CNN-based method provides accurate and reliable assessment of coronary calcification from IVOCT images.
• This technique offers significant value for automated lesion assessment and in-procedure planning of stent deployment.
• The approach achieves human-level performance and close agreement with manual measurements, paving the way for clinical integration.