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dc.contributor.authorXie, Yiting
dc.date.accessioned2018-04-26T14:16:15Z
dc.date.available2018-09-11T06:01:06Z
dc.date.issued2017-08-30
dc.identifier.otherXie_cornellgrad_0058F_10366
dc.identifier.otherhttp://dissertations.umi.com/cornellgrad:10366
dc.identifier.otherbibid: 10361461
dc.identifier.urihttps://hdl.handle.net/1813/56784
dc.description.abstractQuantitative image biomarkers are emerging as a method for precision medical diagnosis. Fully-automated computer algorithms are explored to provide clinically useful biomarker measurements for the assessment of cardiovascular and lung diseases from low-dose thoracic computed tomography (CT) images. The recent regulatory approval of annual lung cancer screening (LCS) provides the opportunity for the application of these methods to a large at-risk population that will already be receiving annual low-dose chest CT scans. These computer algorithms must specifically address the high image noise levels concordant with the low-dose imaging protocol. Quantitative evaluation of cardiovascular disease is facilitated by automated segmentations of cardiac organs (aorta, heart region, pulmonary trunk); primarily coronary artery calcification (CAC), a major indicator of coronary heart diseases, is scored. For lung disease assessment, the automated detection of interstitial lung disease (ILD) at its earliest detectable stage is performed. In addition, CT image quality (noise and calibration) is automatically assessed from segmented homogeneous regions for quality control and increased measurement precision. Automated CAC scores have shown a 0.90 correlation with reference measurements provided by radiologists. The automated ILD detection algorithm is able to distinguish between early-stage ILD and normal cases with an Area Under the ROC curve of 0.95. The image quality assessment method has also shown to be repeatable and robust when evaluated on phantom images and a large LCS cohort. This research advances the state-of-the-art of computer algorithms for precise region segmentation and biomarker measurements that permit the evaluation of cardiac and lung health in the context of LDCT. The successful outcomes of these algorithms have demonstrated the possibility of automated chest health monitoring on an annual basis for a large population through the LCS process.
dc.language.isoen_US
dc.subjectElectrical engineering
dc.subjectCardiac and lung biomarkers
dc.subjectComputer analysis of medical images
dc.subjectFully-automated CAD
dc.subjectLow-dose CT images
dc.subjectComputer science
dc.subjectBiomedical engineering
dc.titleFULLY-AUTOMATED QUANTITATIVE ANALYSIS OF CARDIAC AND LUNG DISEASES FROM THORACIC LOW-DOSE CT IMAGES
dc.typedissertation or thesis
thesis.degree.disciplineElectrical and Computer Engineering
thesis.degree.grantorCornell University
thesis.degree.levelDoctor of Philosophy
thesis.degree.namePh. D., Electrical and Computer Engineering
dc.contributor.chairReeves, Anthony P.
dc.contributor.committeeMemberChen, Tsuhan
dc.contributor.committeeMemberSnavely, Keith Noah
dcterms.licensehttps://hdl.handle.net/1813/59810
dc.identifier.doihttps://doi.org/10.7298/X4GF0RP5


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