Automatic detection of lumen and media in the IVUS images using U-Net with VGG16 Encoder

TL;DR

This paper presents a deep learning approach using U-Net with VGG16 encoder for automatic segmentation of lumen and media in IVUS images, aiding in coronary artery analysis.

Contribution

It introduces a novel U-Net based segmentation model with VGG16 encoder specifically designed for IVUS image analysis.

Findings

The proposed model achieves promising segmentation accuracy.

Qualitative results demonstrate effective delineation of arterial walls.

Quantitative metrics indicate improved performance over baseline methods.

Abstract

Coronary heart disease is one of the top rank leading cause of mortality in the world which can be because of plaque burden inside the arteries. Intravascular Ultrasound (IVUS) has been recognized as power- ful imaging technology which captures the real time and high resolution images of the coronary arteries and can be used for the analysis of these plaques. The IVUS segmentation involves the extraction of two arterial walls components namely, lumen and media. In this paper, we investi- gate the effectiveness of Convolutional Neural Networks including U-Net to segment ultrasound scans of arteries. In particular, the proposed seg- mentation network was built based on the the U-Net with the VGG16 encoder. Experiments were done for evaluating the proposed segmen- tation architecture which show promising quantitative and qualitative results.