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
{"title":"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。","authors":"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","doi":"10.1117/12.3047189","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"13409 ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12034099/pdf/","citationCount":"0","resultStr":"{\"title\":\"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.\",\"authors\":\"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\",\"doi\":\"10.1117/12.3047189\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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.</p>\",\"PeriodicalId\":74505,\"journal\":{\"name\":\"Proceedings of SPIE--the International Society for Optical Engineering\",\"volume\":\"13409 \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12034099/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of SPIE--the International Society for Optical Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1117/12.3047189\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/4/10 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of SPIE--the International Society for Optical Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1117/12.3047189","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/4/10 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
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.