Lumen Segmentation in Optical Coherence Tomography Images using Convolutional Neural Network.

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Cardiovascular Research

Background:

• Lumen segmentation in Optical Coherence Tomography (OCT) images is vital for analyzing atherosclerosis.
• Previous segmentation methods include level sets, morphological reconstruction, and Markov random fields.
• Convolutional Neural Networks (CNNs) have shown promise in image processing but haven't been applied to IVOCT lumen segmentation.

Purpose of the Study:

• To present a novel approach for lumen segmentation in IVOCT images using CNNs.
• To evaluate the effectiveness of different CNN architectures for this task.
• To investigate the impact of image size and coordinate system on segmentation performance.

Main Methods:

• Three different CNN architectures were evaluated for lumen segmentation.
• The CNNs were tested on image datasets with varying sizes (768x768 and 192x192 pixels).
• Both Cartesian and polar coordinate systems were used for image representation.

Main Results:

• The best segmentation performance was achieved using smaller images (192x192 pixels) represented in a polar coordinate system.
• Accuracy, Dice index, and Jaccard index exceeded 99%, 98%, and 97%, respectively, with this configuration.
• CNNs demonstrated superior performance compared to traditional methods not explicitly detailed here.

Conclusions:

• CNN-based lumen segmentation is a highly effective method for IVOCT images.
• Optimizing image size and coordinate system representation significantly improves segmentation accuracy.
• This approach holds potential for advancing atherosclerosis diagnosis and treatment planning.