Coronary artery segmentation from intravascular optical coherence tomography using deep capsules

Area of Science:

• Medical Imaging
• Cardiovascular Disease Diagnostics
• Artificial Intelligence in Medicine

Background:

• Accurate segmentation of coronary arteries from intravascular optical coherence tomography (IVOCT) is crucial for diagnosing and managing coronary artery disease.
• Current methods face challenges due to time-consuming expert labeling and potential analysis bias.
• Automated, robust, and unbiased geometry extraction from IVOCT is needed for clinical applications.

Purpose of the Study:

• To develop a fast, low-memory deep learning model for automated lumen segmentation in IVOCT images.
• To achieve segmentation quality comparable to state-of-the-art methods without sacrificing speed.
• To create a tool that assists clinicians in unbiased geometrical data extraction from IVOCT.

Main Methods:

• A novel capsule-based deep learning method, DeepCap, was developed using a dataset of 12,011 expert-labeled IVOCT images.
• The dataset included images with blood, light artefacts, metallic, and bioresorbable stents.
• Rigorous investigation of design variations, including upsampling regimes and input selection, was performed.

Main Results:

• DeepCap achieves segmentation quality and robustness on par with state-of-the-art methods.
• The model utilizes only 12% of the parameters compared to other leading models.
• DeepCap demonstrates significantly faster inference times: up to 70% faster on GPU and 95% faster on CPU.

Conclusions:

• DeepCap is a highly efficient and accurate automated segmentation tool for IVOCT images.
• The model's speed and low memory footprint make it suitable for clinical deployment.
• DeepCap can assist clinicians in obtaining unbiased geometrical data for improved patient care.