Coronary artery segmentation in angiographic videos utilizing spatial-temporal information
- PDF / 2,995,579 Bytes
- 10 Pages / 595 x 791 pts Page_size
- 7 Downloads / 184 Views
RESEARCH ARTICLE
Open Access
Coronary artery segmentation in angiographic videos utilizing spatial-temporal information Lu Wang1,2 , Dongxue Liang1* and Zhaoyuan Ma1
, Xiaolei Yin1,2 , Jing Qiu1 , Zhiyun Yang3 , Junhui Xing4 , Jianzeng Dong3,4
Abstract Background: Coronary artery angiography is an indispensable assistive technique for cardiac interventional surgery. Segmentation and extraction of blood vessels from coronary angiographic images or videos are very essential prerequisites for physicians to locate, assess and diagnose the plaques and stenosis in blood vessels. Methods: This article proposes a novel coronary artery segmentation framework that combines a three–dimensional (3D) convolutional input layer and a two–dimensional (2D) convolutional network. Instead of a single input image in the previous medical image segmentation applications, our framework accepts a sequence of coronary angiographic images as input, and outputs the clearest mask of segmentation result. The 3D input layer leverages the temporal information in the image sequence, and fuses the multiple images into more comprehensive 2D feature maps. The 2D convolutional network implements down–sampling encoders, up–sampling decoders, bottle–neck modules, and skip connections to accomplish the segmentation task. Results: The spatial–temporal model of this article obtains good segmentation results despite the poor quality of coronary angiographic video sequences, and outperforms the state–of–the–art techniques. Conclusions: The results justify that making full use of the spatial and temporal information in the image sequences will promote the analysis and understanding of the images in videos. Keywords: Coronary artery angiography, Image segmentation, Video segmentation
Background Physicians have been practicing interventional surgeries to diagnose and treat cardiovascular diseases for several decades. They locate, assess and diagnose the blood vessel stenosis and plaques by directly watching the angiographic videos with naked eyes during the surgeries. Based on their experiences, the physicians quickly make a qualitative judgment on the patient’s coronary artery condition and plan the treatment. This direct method is greatly affected by human factors and lacks accuracy, *Correspondence: [email protected] The Future Laboratory, Tsinghua University, 100084 Beijing, China Full list of author information is available at the end of the article 1
objectivity and consistency. Automated cardiovascular segmentation will help reduce the diagnostic inaccuracies for physicians. Many blood vessel extraction methods based on image segmentation have emerged driven by this motivation. Recently, with the development of deep learning, various deep neural network architectures have been proposed and applied in the medical image segmentation field [1–4]. Early deep learning–based approaches used the image patches and a sliding window block to traverse the image [5]. But the sliding window method casts a huge amount of computation, and misses the globa
Data Loading...
