Coronary Artery Segmentation in Angiographic Videos Using A 3D-2D CE-Net

TL;DR

This paper introduces a novel video segmentation framework combining 3D convolutional layers and 2D CE-Net to improve coronary artery segmentation in angiographic videos, aiding physicians in diagnosis.

Contribution

It presents a new framework that integrates spatial-temporal feature extraction with image segmentation for coronary angiography videos, enhancing segmentation accuracy.

Findings

Achieved effective segmentation despite poor video quality

Successfully extracted comprehensive blood vessel masks

Improved visualization for clinical diagnosis

Abstract

Coronary angiography is an indispensable assistive technique for cardiac interventional surgery. Segmentation and extraction of blood vessels from coronary angiography videos are very essential prerequisites for physicians to locate, assess and diagnose the plaques and stenosis in blood vessels. This article proposes a new video segmentation framework that can extract the clearest and most comprehensive coronary angiography images from a video sequence, thereby helping physicians to better observe the condition of blood vessels. This framework combines a 3D convolutional layer to extract spatial--temporal information from a video sequence and a 2D CE--Net to accomplish the segmentation task of an image sequence. The input is a few continuous frames of angiographic video, and the output is a mask of segmentation result. From the results of segmentation and extraction, we can get good segmentation results despite the poor quality of coronary angiography video sequences.