Angiographic dataset for stenosis detection

In this dataset, we present a set of angiographic imaging series of one hundred patients who underwent coronary angiography using Coroscop (Siemens) and Innova (GE Healthcare) image-guided surgery systems at the Research Institute for Complex Problems of Cardiovascular Diseases (Kemerovo, Russia). All patients had angiographically and/or functionally confirmed one-vessel coronary artery disease (≥70% diameter stenosis by quantitative coronary analysis or 50 - 69% with FFR (fractional flow reserve) ≤ 0.80 or stress echocardiography evidence of regional ischemia). For the purpose of our study, significant coronary stenosis was defined according to 2017 US appropriate use criteria for coronary revascularization in patients with stable ischemic heart disease. The study design was approved by the Local Ethics Committee of the Research Institute for Complex Issues of Cardiovascular Diseases (approval letter No. 112 issued on May 11, 2018). All participants provided written informed consent to participate in the study. Coronary angiography was performed by the single operator according to the indications and recommendations stated in the 2018 ESC/EACTS Guidelines on myocardial revascularization. The presence or absence of coronary stenosis was confirmed by the same operator using angiography imaging series according to the 2018 ESC/EACTS Guidelines on myocardial revascularization.

Angiographic images of the radiopaque overlaid coronary arteries with stenotic segments were selected and then converted into separate images. An interventional cardiologist rejected non-informative images and selected only those containing contrast passages through a stenotic vessel. A total of 8325 grayscale images (100 patients) of 512×512 to 1000×1000 pixels were included in the dataset. Data were labeled using LabelBox, a free version of SaaS (Software as a Service).

We additionally estimated the size of the stenotic region by computing the area of the bounding box. Similar to the Common Objects in Context dataset, we divided objects by their area into three types: small (area < 322), medium (322 ≤ area ≤ 962), and large (area > 962). 2509 small objects (30%), 5704 medium objects (69%), and 113 large objects (1%) were obtained in the input data.

本数据集收录了百名接受冠脉造影检查的患者所进行的冠脉造影成像系列，这些检查由俄罗斯克麦罗沃的心血管疾病复杂问题研究机构采用Coroscop（西门子）和Innova（通用电气医疗保健）图像引导手术系统完成。所有患者均经造影或功能检查证实患有单支冠状动脉疾病（通过定量冠脉分析，直径狭窄≥70%，或FFR（血流储备分数）50-69%且≤0.80，或压力超声心动图显示区域性缺血）。本研究中，显著冠脉狭窄的定义依据2017年美国针对稳定型缺血性心脏病患者冠脉再血管化适宜使用标准。研究设计已获得心血管疾病复杂问题研究机构地方伦理委员会的批准（批准函编号112，发布于2018年5月11日）。所有参与者均提供了书面知情同意书，同意参与本研究。冠脉造影由单一操作者根据2018年ESC/EACTS心肌再血管化指南中的指示和建议进行。是否存在冠脉狭窄，由同一操作者根据2018年ESC/EACTS心肌再血管化指南中的标准，通过造影成像系列进行确认。选取了放射不透明冠脉的狭窄段成像，并将其转换为单独的图像。一名介入心脏病学家拒绝了无信息量的图像，仅选择了那些包含通过狭窄血管的对比剂通道的图像。数据集中包含8325张灰度图像（100位患者），像素尺寸为512×512至1000×1000。数据标注使用的是LabelBox免费版的SaaS（软件即服务）工具。此外，我们还通过计算边界框的面积来估算狭窄区域的尺寸。与Common Objects in Context数据集类似，我们根据对象的面积将其分为三类：小型（面积<322）、中型（322≤面积≤962）和大型（面积>962）。输入数据中获得了2509个小型对象（30%）、5704个中型对象（69%）和113个大型对象（1%）。

搜集汇总

背景与挑战

背景概述

该数据集是一个用于冠状动脉狭窄检测的血管造影图像集合，包含100名确诊患者的8325张灰度图像，图像分辨率在512×512到1000×1000像素之间，并已通过标注工具进行标注，根据狭窄区域面积分为小、中、大三类对象。数据集专为对象检测和深度学习应用设计，适用于心血管医学研究，遵循CC BY 4.0开放许可。