肺部影像解剖结构分割数据集及应用  

Pulmonary image anatomical structure segmentation dataset and applications

作　　者：覃文军[1,2] 李小硕 周庆华 刘盼 杨金柱 Tan Wenjun;Li Xiaoshuo;Zhou Qinghua;Liu Pan;Yang Jinzhu(Key Laboratory of Intelligent Computing in Medical Image,Ministry of Education,Northeastern University,Shenyang 110169,China;School of Computer Science and Engineering,Northeastern University,Shenyang 110169,China)

机构地区：[1]东北大学医学影像智能计算教育部重点实验室,沈阳110169 [2]东北大学计算机科学与工程学院,沈阳110169

出　　处：《中国图象图形学报》2021年第9期2111-2120,共10页Journal of Image and Graphics

基　　金：中央高校基本科研业务费专项资金资助(N182410001);国家自然科学基金项目(61971118)。

摘　　要：目的从影像中快速精准地分割出肺部解剖结构可以清晰直观地分辨各解剖结构间的关系,提供有效、客观的辅助诊断信息,大大提高医生的阅片效率并降低医生的工作量。随着影像分割算法的发展,越来越多的方法应用于分割肺部影像中感兴趣的解剖结构区域,但目前尚缺乏包含多种肺部精细解剖结构的影像数据集。本文创建了一个带标签的肺部CT/CTA(computer tomography/computer tomography angiography)影像数据集,以促进肺部解剖结构分割算法的发展。方法该数据集共标记了67组肺部CT/CTA影像,包括CT影像24组、CTA影像43组,共计切片图像26 157幅。每组CT/CTA有4个不同的目标区域类别,标记对应支气管、肺实质、肺叶、肺动脉和肺静脉。结果本文利用该数据集,用于肺部CT解剖结构分割医学影像挑战赛——2020年第四届国际图像计算与数字医学研讨会,该挑战赛提供了一个肺血管、支气管和肺实质的评估平台,通过Dice系数、过分割率、欠分割率、医学和算法行业专家对分割和3维重建效果进行了评估,目的是比较各种算法分割肺部解剖结构的性能。结论本文详细描述了包括支气管、肺实质、肺叶、肺动脉和肺静脉等解剖结构标签的肺部影像数据集和应用结果,为相关研究人员利用本数据集进行更深入的研究提供参考。Objective Images-based segmentation of pulmonary anatomy has been set up the anatomical structures to formulate rapid and targeted diagnostic information.The purpose of pulmonary anatomy segmentation has been associated to a pixel in an image with an anatomical structure without the need for manual initialization.A lots of supervised deep learning image segmentation have been illustrated for segmenting regions of interest in pulmonary CT(computer tomography) images.The medical image segmentation has greatly relied on high-quality labeled medical image data, CT images-based lung anatomy labeled data has been insufficient adopted due to the lack of expert annotation of regions of interest and the lack of infrastructure and standards for sharing labeled data.Most of pulmonary CT annotation datasets have focused on thoracic cancer,pulmonary nodules, tuberculosis, pneumonia and lung segmentation.A dataset of pulmonary CT/CTA(computer tomography/computer tomography angiography) scan images with labels has facilitated the evolvement of pulmonary anatomical structure segmentation algorithms.The dataset has been evaluated the performance of state-of-the-art pulmonary anatomy structure segmentation methods for chest CT scans.It has been difficult to compare various algorithms for pulmonary anatomy structure segmentation.Different methods have been evaluated on different datasets using different evaluation measures in common.The related dataset has implemented a dataset of chest CT scans to identify varying abnormalities based on the reference standards in the context of airway, lung parenchyma, lobe and pulmonary artery.The vein segmentations have been established.The dataset has a unique calculation to compare pulmonary anatomy structure algorithms via the comparison all methods against the reference standard baseline.Method A sum of 67 sets of CT/CTA images of the pulmonary have labeled in this dataset including 24 sets of CT images and 43 sets of CTA images via a total of 26 157 slices images.Each set of CT/CTA images h

关 键 词：肺部解剖结构 肺部CT影像 数据集 图像分割 医学影像 

分 类 号：TP391.6[自动化与计算机技术—计算机应用技术]