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

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.

10 pages, under review for International Conference of Smart Multimedia (ICSM) 2018

References in corpus (2)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Segmentation of Arterial Walls in Intravascular Ultrasound Cross-Sectional Images Using Extremal Region Selection

Cited by in corpus (5)

• Precise Repositioning of Robotic Ultrasound: Improving Registration-based Motion Compensation using Ultrasound Confidence Optimization
• Automated Cardiothoracic Ratio Calculation and Cardiomegaly Detection using Deep Learning Approach
• Transfer Learning U-Net Deep Learning for Lung Ultrasound Segmentation
• Optimal Surface Segmentation with Convex Priors in Irregularly Sampled Space
• Globally Optimal Surface Segmentation using Deep Learning with Learnable Smoothness Priors