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Automated Analysis of Quantitative I...
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Liu, Shuang.
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Automated Analysis of Quantitative Image Biomarkers from Low-Dose Chest CT Scans.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Automated Analysis of Quantitative Image Biomarkers from Low-Dose Chest CT Scans./
作者:
Liu, Shuang.
出版者:
Ann Arbor : ProQuest Dissertations & Theses, : 2018,
面頁冊數:
170 p.
附註:
Source: Dissertations Abstracts International, Volume: 80-03, Section: B.
Contained By:
Dissertations Abstracts International80-03B.
標題:
Computer Engineering. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10845306
ISBN:
9780438343542
Automated Analysis of Quantitative Image Biomarkers from Low-Dose Chest CT Scans.
Liu, Shuang.
Automated Analysis of Quantitative Image Biomarkers from Low-Dose Chest CT Scans.
- Ann Arbor : ProQuest Dissertations & Theses, 2018 - 170 p.
Source: Dissertations Abstracts International, Volume: 80-03, Section: B.
Thesis (Ph.D.)--Cornell University, 2018.
This item must not be sold to any third party vendors.
A quantitative imaging biomarker is a quantitatively measured characteristic derived from medical images, which serves as cost-effective and noninvasive tools for patient health assessment, including diagnosis and periodic screening of disease, therapy planning as well as longitudinal monitoring of treatment response. This dissertation presents an automated framework for quantitative image biomarker measurement and evaluation from the low-dose chest CT (LDCT) scans that are acquired during the annual lung cancer screening. Four categories of quantitative image biomarkers are investigated, including breast density and gynecomastia quantification, bone mineral density (BMD), airway dimensions and pulmonary nodule classification. An anatomy directed approach is applied to the analysis of the breast region and to the biomarker measurements. The fully automated breast density assessment and gynecomastia measurements have been demonstrated to be consistent with the reading of radiologists. Fully automated BMD assessment is achieved by building upon the model-based segmentation and anatomical labeling of individual vertebral body. Statistically significant strong correlation with the gold standard reference can be obtained at all vertebral levels. A fully automated knowledge-based approach is applied to the segmentation and anatomical labeling of each airway bronchus, which enables the measurements of precise and reproducible airway dimensions. For the classification of pulmonary nodule malignancy, a 3D CNN is trained from scratch and demonstrates various advantages over both the traditional machine learning approaches using hand-crafted 3D image features and the 2D CNN models. Classifier ensembles of the combinations of the 3D CNN and traditional machine learning models achieve the best performance by taking advantage of the complementary characteristics of the traditional models and the CNN models. In conclusion, with the recent large-scale implementation of annual lung cancer screening in the US using LDCT, great potential emerges for the concurrent extraction of quantitative image biomarkers from different regions in the chest, which are covered in LDCT. This dissertation has demonstrated the feasibility of fully automated measurement and evaluation of a rich set of quantitative image biomarkers, and the opportunity to significantly enhance the impact of LDCT by offering a comprehensive health assessment to each screening participant with no additional imaging or radiation exposure.
ISBN: 9780438343542Subjects--Topical Terms:
1567821
Computer Engineering.
Automated Analysis of Quantitative Image Biomarkers from Low-Dose Chest CT Scans.
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A quantitative imaging biomarker is a quantitatively measured characteristic derived from medical images, which serves as cost-effective and noninvasive tools for patient health assessment, including diagnosis and periodic screening of disease, therapy planning as well as longitudinal monitoring of treatment response. This dissertation presents an automated framework for quantitative image biomarker measurement and evaluation from the low-dose chest CT (LDCT) scans that are acquired during the annual lung cancer screening. Four categories of quantitative image biomarkers are investigated, including breast density and gynecomastia quantification, bone mineral density (BMD), airway dimensions and pulmonary nodule classification. An anatomy directed approach is applied to the analysis of the breast region and to the biomarker measurements. The fully automated breast density assessment and gynecomastia measurements have been demonstrated to be consistent with the reading of radiologists. Fully automated BMD assessment is achieved by building upon the model-based segmentation and anatomical labeling of individual vertebral body. Statistically significant strong correlation with the gold standard reference can be obtained at all vertebral levels. A fully automated knowledge-based approach is applied to the segmentation and anatomical labeling of each airway bronchus, which enables the measurements of precise and reproducible airway dimensions. For the classification of pulmonary nodule malignancy, a 3D CNN is trained from scratch and demonstrates various advantages over both the traditional machine learning approaches using hand-crafted 3D image features and the 2D CNN models. Classifier ensembles of the combinations of the 3D CNN and traditional machine learning models achieve the best performance by taking advantage of the complementary characteristics of the traditional models and the CNN models. In conclusion, with the recent large-scale implementation of annual lung cancer screening in the US using LDCT, great potential emerges for the concurrent extraction of quantitative image biomarkers from different regions in the chest, which are covered in LDCT. This dissertation has demonstrated the feasibility of fully automated measurement and evaluation of a rich set of quantitative image biomarkers, and the opportunity to significantly enhance the impact of LDCT by offering a comprehensive health assessment to each screening participant with no additional imaging or radiation exposure.
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