東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Automated Analysis of Quantitative I...

Liu, Shuang.

FindBook

Google Book

Amazon

博客來

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.
LDR:03669nmm a2200361 4500 001 2210489
005 20191121124228.5
008 201008s2018 ||||||||||||||||| ||eng d
020 $a 9780438343542
035 $a (MiAaPQ)AAI10845306
035 $a (MiAaPQ)cornellgrad:10992
035 $a AAI10845306
040 $a MiAaPQ $c MiAaPQ
100 1 $a Liu, Shuang. $3 1669598
245 1 0 $a Automated Analysis of Quantitative Image Biomarkers from Low-Dose Chest CT Scans.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2018
300 $a 170 p.
500 $a Source: Dissertations Abstracts International, Volume: 80-03, Section: B.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Reeves, Anthony P.
502 $a Thesis (Ph.D.)--Cornell University, 2018.
506 $a This item must not be sold to any third party vendors.
520 $a 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.
590 $a School code: 0058.
650 4 $a Computer Engineering. $3 1567821
650 4 $a Electrical engineering. $3 649834
650 4 $a Medical imaging. $3 3172799
650 4 $a Artificial intelligence. $3 516317
650 4 $a Computer science. $3 523869
690 $a 0464
690 $a 0544
690 $a 0574
690 $a 0800
690 $a 0984
710 2 $a Cornell University. $b Electrical & Computer Engineering. $3 3345477
773 0 $t Dissertations Abstracts International $g 80-03B.
790 $a 0058
791 $a Ph.D.
792 $a 2018
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10845306