胸部x射线中的自动多病灶注释:使用基于人工智能的智能图像框架和真相(SIFT)系统注释来自公共数据集的超过45万张图像。

Lin Guo, Fleming Y M Lure, Teresa Wu, Fulin Cai, Stefan Jaeger, Bin Zheng, Jordan Fuhrman, Hui Li, Maryellen L Giger, Andrei Gabrielian, Alex Rosenthal, Darrell E Hurt, Ziv Yaniv, Li Xia, Weijun Fang, Jingzhe Liu
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引用次数: 0

摘要

这项工作利用基于人工智能(AI)的图像注释工具,智能图像框架和真相(SIFT),对来自四个公开数据集(CheXpert数据集,ChestX-ray14数据集,MIDRC数据集和NIAID TB Portals数据集)的452,602张胸部x射线(CXR)图像(22种不同类型的期望病变)的肺部病变和异常及其相应的边界进行注释。SIFT基于多任务、最优推荐和最大预测分类和分割(MOM ClaSeg)技术,用于识别和描绘CXR图像上65个不同的异常感兴趣区域(ROI),为每个标记的ROI提供置信度分数,如果置信度分数不够高,则为每个ROI提供各种异常建议。MOM ClaSeg系统集成了Mask R-CNN和Decision Fusion Network,是在超过30万个cxr的训练数据集上开发的,其中包含超过24万个已确认的异常cxr,超过30万个已确认的roi对应于65种不同的异常和超过67,000个正常(即“无发现”)cxr。质量控制后,将cxr输入到SIFT系统中,自动预测每个原始图像上显示的roi的异常类型(“预测异常”)和相应的边界位置。结果表明,与使用传统半自动方法相比,使用SIFT辅助放射科医师判断标记roi异常类型及其边界坐标的效率更高(提高7.92倍)。SIFT系统在4个数据集上的平均灵敏度为89.38%±11.46%。这可以显著提高训练和测试集的质量和数量,以开发人工智能技术。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automated multi-lesion annotation in chest X-rays: annotating over 450,000 images from public datasets using the AI-based Smart Imagery Framing and Truthing (SIFT) system.

This work utilized an artificial intelligence (AI)-based image annotation tool, Smart Imagery Framing and Truthing (SIFT), to annotate pulmonary lesions and abnormalities and their corresponding boundaries on 452,602 chest X-ray (CXR) images (22 different types of desired lesions) from four publicly available datasets (CheXpert Dataset, ChestX-ray14 Dataset, MIDRC Dataset, and NIAID TB Portals Dataset). SIFT is based on Multi-task, Optimal-recommendation, and Max-predictive Classification and Segmentation (MOM ClaSeg) technologies to identify and delineate 65 different abnormal regions of interest (ROI) on CXR images, provide a confidence score for each labeled ROI, and various recommendations of abnormalities for each ROI, if the confidence score is not high enough. The MOM ClaSeg System integrating Mask R-CNN and Decision Fusion Network is developed on a training dataset of over 300,000 CXRs, containing over 240,000 confirmed abnormal CXRs with over 300,000 confirmed ROIs corresponding to 65 different abnormalities and over 67,000 normal (i.e., "no finding") CXRs. After quality control, the CXRs are entered into the SIFT system to automatically predict the abnormality type ("Predicted Abnormality") and corresponding boundary locations for the ROIs displayed on each original image. The results indicated that the SIFT system can determine the abnormality types of labeled ROIs and their boundary coordinates with high efficiency (improved 7.92 times) when radiologists used SIFT as an aide compared to radiologists using a traditional semi-automatic method. The SIFT system achieves an average sensitivity of 89.38%±11.46% across four datasets. This can significantly improve the quality and quantity of training and testing sets to develop AI technologies.

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