Korean Circ J.  2024 Jan;54(1):30-39. 10.4070/kcj.2023.0166.

Deep Learning-Based Lumen and Vessel Segmentation of Intravascular Ultrasound Images in Coronary Artery Disease

• Jeong GJ
• Lee G
• Lee JG
• Kang SJ

• KMID: 2550605
• DOI: http://doi.org/10.4070/kcj.2023.0166

Abstract

Background and Objectives
Intravascular ultrasound (IVUS) evaluation of coronary artery morphology is based on the lumen and vessel segmentation. This study aimed to develop an automatic segmentation algorithm and validate the performances for measuring quantitative IVUS parameters.
Methods
A total of 1,063 patients were randomly assigned, with a ratio of 4:1 to the training and test sets. The independent data set of 111 IVUS pullbacks was obtained to assess the vessel-level performance. The lumen and external elastic membrane (EEM) boundaries were labeled manually in every IVUS frame with a 0.2-mm interval. The Efficient-UNet was utilized for the automatic segmentation of IVUS images.
Results
At the frame-level, Efficient-UNet showed a high dice similarity coefficient (DSC, 0.93±0.05) and Jaccard index (JI, 0.87±0.08) for lumen segmentation, and demonstrated a high DSC (0.97±0.03) and JI (0.94±0.04) for EEM segmentation. At the vessel-level, there were close correlations between model-derived vs. experts-measured IVUS parameters; minimal lumen image area (r=0.92), EEM area (r=0.88), lumen volume (r=0.99) and plaque volume (r=0.95). The agreement between model-derived vs. expert-measured minimal lumen area was similarly excellent compared to the experts' agreement. The model-based lumen and EEM segmentation for a 20-mm lesion segment required 13.2 seconds, whereas manual segmentation with a 0.2-mm interval by an expert took 187.5 minutes on average.
Conclusions
The deep learning models can accurately and quickly delineate vascular geometry. The artificial intelligence-based methodology may support clinicians’ decisionmaking by real-time application in the catheterization laboratory.

Keyword

Deep learning; Diagnostic imaging; Artificial intelligence

Figure

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  Figure 1 Workflow for developing the Efficient-UNet for IVUS segmentation.IVUS = intravascular ultrasound.

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  Figure 2 Bland-Altman between the proposed model-derived and expert-measured minimal lumen area.

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  Figure 3 Correlation between the proposed model-derived and expert-measured minimal lumen area.

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