论文标题
使用3D-2D CE-NET在血管造影视频中的冠状动脉分割
Coronary Artery Segmentation in Angiographic Videos Using A 3D-2D CE-Net
论文作者
论文摘要
冠状动脉血管造影是一种必不可少的心脏介入手术辅助技术。从冠状动脉造影视频中分割和提取血管是医生在血管中定位，评估和诊断斑块和狭窄的非常重要的先决条件。本文提出了一个新的视频分割框架，可以从视频序列中提取最清晰，最全面的冠状动脉造影图像，从而帮助医生更好地观察血管的状况。该框架结合了一个3D卷积层，从视频序列和2D CE-NET提取时空信息，以完成图像序列的分割任务。输入是几个连续的血管造影视频帧，输出是分割结果的掩盖。从分割和提取的结果来看，尽管冠状动脉血管造影视频序列质量较差，我们仍可以获得良好的分割结果。
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.
