Syed Ahmed Nadeem, Eric A Hoffman, Alejandro P Comellas, Punam K Saha
{"title":"气道腔面积和壁厚的局部自适应半最大值方法及其重复 CT 扫描的再现性。","authors":"Syed Ahmed Nadeem, Eric A Hoffman, Alejandro P Comellas, Punam K Saha","doi":"10.1109/isbi45749.2020.9098558","DOIUrl":null,"url":null,"abstract":"<p><p>Quantitative computed tomography (CT)-based characterization of bronchial metrics is increasingly being used to investigate chronic obstructive pulmonary disease (COPD)-related phenotypes. Automated methods for airway measurements benefit large multi-site studies by reducing cost and subjectivity errors. Critical challenges for CT-based analysis of airway morphology are related to location of lumen and wall transitions in the presence of varying scales and intensity-contrasts from proximal to distal sites. This paper introduces locally adaptive half-max methods to locate airway lumen and wall transitions and compute cross-sectional lumen area and wall-thickness. Also, the method uses a consistency analysis of wall-thickness to avoid adjoining-structure-artifacts. Experimental results show that computed bronchial measures at individual anatomic airway tree locations are repeat CT scan reproducible with intra-class correlation coefficient (ICC) values exceeding 0.9 and 0.8 for lumen-area and wall-thickness, respectively. Observed ICC values for derived morphologic measures, e.g., lumen-area compactness (ICC>0.67) and tapering (ICC>0.47) are relatively lower.</p>","PeriodicalId":74566,"journal":{"name":"Proceedings. IEEE International Symposium on Biomedical Imaging","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8375398/pdf/nihms-1571624.pdf","citationCount":"0","resultStr":"{\"title\":\"LOCALLY ADAPTIVE HALF-MAX METHODS FOR AIRWAY LUMEN-AREA AND WALL-THICKNESS AND THEIR REPEAT CT SCAN REPRODUCIBILITY.\",\"authors\":\"Syed Ahmed Nadeem, Eric A Hoffman, Alejandro P Comellas, Punam K Saha\",\"doi\":\"10.1109/isbi45749.2020.9098558\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Quantitative computed tomography (CT)-based characterization of bronchial metrics is increasingly being used to investigate chronic obstructive pulmonary disease (COPD)-related phenotypes. Automated methods for airway measurements benefit large multi-site studies by reducing cost and subjectivity errors. Critical challenges for CT-based analysis of airway morphology are related to location of lumen and wall transitions in the presence of varying scales and intensity-contrasts from proximal to distal sites. This paper introduces locally adaptive half-max methods to locate airway lumen and wall transitions and compute cross-sectional lumen area and wall-thickness. Also, the method uses a consistency analysis of wall-thickness to avoid adjoining-structure-artifacts. Experimental results show that computed bronchial measures at individual anatomic airway tree locations are repeat CT scan reproducible with intra-class correlation coefficient (ICC) values exceeding 0.9 and 0.8 for lumen-area and wall-thickness, respectively. Observed ICC values for derived morphologic measures, e.g., lumen-area compactness (ICC>0.67) and tapering (ICC>0.47) are relatively lower.</p>\",\"PeriodicalId\":74566,\"journal\":{\"name\":\"Proceedings. IEEE International Symposium on Biomedical Imaging\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8375398/pdf/nihms-1571624.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings. IEEE International Symposium on Biomedical Imaging\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/isbi45749.2020.9098558\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2020/5/22 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. IEEE International Symposium on Biomedical Imaging","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/isbi45749.2020.9098558","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2020/5/22 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
LOCALLY ADAPTIVE HALF-MAX METHODS FOR AIRWAY LUMEN-AREA AND WALL-THICKNESS AND THEIR REPEAT CT SCAN REPRODUCIBILITY.
Quantitative computed tomography (CT)-based characterization of bronchial metrics is increasingly being used to investigate chronic obstructive pulmonary disease (COPD)-related phenotypes. Automated methods for airway measurements benefit large multi-site studies by reducing cost and subjectivity errors. Critical challenges for CT-based analysis of airway morphology are related to location of lumen and wall transitions in the presence of varying scales and intensity-contrasts from proximal to distal sites. This paper introduces locally adaptive half-max methods to locate airway lumen and wall transitions and compute cross-sectional lumen area and wall-thickness. Also, the method uses a consistency analysis of wall-thickness to avoid adjoining-structure-artifacts. Experimental results show that computed bronchial measures at individual anatomic airway tree locations are repeat CT scan reproducible with intra-class correlation coefficient (ICC) values exceeding 0.9 and 0.8 for lumen-area and wall-thickness, respectively. Observed ICC values for derived morphologic measures, e.g., lumen-area compactness (ICC>0.67) and tapering (ICC>0.47) are relatively lower.