Coronary calcification segmentation in intravascular OCT images using deep learning: application to calcification...

Area of Science:

• Cardiovascular Imaging
• Medical Image Analysis
• Interventional Cardiology

Background:

• Major coronary calcifications pose significant challenges during percutaneous coronary interventions, impeding optimal stent deployment.
• Accurate assessment of calcification severity is crucial for effective treatment planning and procedural success.

Purpose of the Study:

• To develop and evaluate a comprehensive software tool for segmenting coronary calcifications in intravascular optical coherence tomography (IVOCT) images.
• To quantify the impact of calcifications using a stent-deployment calcification score derived from automated analysis.

Main Methods:

• Utilized a pretrained SegNet convolutional neural network, refined for segmentation of vascular lumen and calcifications in IVOCT images.
• Applied conditional random field processing for noise cleaning of segmentation results.
• Evaluated the method on 48 manually annotated IVOCT volumes of interest (VOIs) using 10-fold cross-validation.

Main Results:

• Achieved high sensitivities for segmenting calcified, lumen, and other tissue classes after noise cleaning.
• Demonstrated strong correlation between manual and automated measurements of lumen and calcification attributes via Bland-Altman analysis.
• Reported good agreement between manual and automated stent-deployment calcification scores, with exact agreement in four out of five lesions.

Conclusions:

• The developed software accurately segments coronary calcifications and quantifies their impact on stent deployment.
• The automated approach shows strong correlation with manual assessments and good agreement for calcification scores.
• This classification approach holds potential for clinical application in assessing and planning treatments for coronary calcification lesions.