東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Deep Learning for Ultrasound Image A...

Yang, Xin.

Linked to FindBook

Google Book

Amazon

博客來

Deep Learning for Ultrasound Image Analysis: From Feasibility to Robustness.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep Learning for Ultrasound Image Analysis: From Feasibility to Robustness./
Author:	Yang, Xin.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	181 p.
Notes:	Source: Dissertations Abstracts International, Volume: 81-09, Section: B.
Contained By:	Dissertations Abstracts International81-09B.
Subject:	Computer science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27784024
ISBN:	9781392486108

Deep Learning for Ultrasound Image Analysis: From Feasibility to Robustness.
Yang, Xin.

Deep Learning for Ultrasound Image Analysis: From Feasibility to Robustness. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 181 p.

Source: Dissertations Abstracts International, Volume: 81-09, Section: B.

Thesis (Ph.D.)--The Chinese University of Hong Kong (Hong Kong), 2019.

Ultrasound is a dominant and unique imaging modality in several important clinic scenarios, like surgical planning and prenatal examinations. Segmenting interest objects, localizing landmarks and measuring biometrics are frequently requested during ultrasound scanning. However, subject to the user dependency, manual analyses often present low reproducibility and high discrepancy. Automatic ultrasound image analyses are highly desired to improve the examination quality and extend the ultrasound to its full use in more applications.The great resurgence of deep learning brings breakthroughs for medical image analysis. Whereas, the challenges in ultrasound images, including the non-standard acquisition, poor image quality, varying appearance shift and large volume, still hinder the progress and robustness of deep learning solutions.In this thesis, we focus on improving the feasibility and robustness of deep learning for ultrasound image analysis. Our contributions are as follows. For application part: (a) we proposed the first deep learning method for prostate segmentation in 2D ultrasound images and achieved state-of-the-art performance. (b) we devised the first fully automated method to simultaneously segment fetus, gestational sac and placenta in ultrasound volumes. This study provides new opportunities for precise monitoring of fetal growth. (c) we created the first work about 3D fetal pose estimation in the literature, with the desire to build a general navigation map for many advanced studies. Tackling the challenges in ultrasound images, our methodology contributions are four-fold. (a) we proposed a novel sequentiality based method to address the boundary ambiguity for ultrasound segmentation. (b) we proposed a case adaptation strategy to cope with the appearance shift under different ultrasound imaging conditions. (c) we successfully customized the deep reinforcement learning to effectively narrow the search space for plane detection in ultrasound volumes. (d) we optimized the GPU memory management under limited GPU resources and proved our strategy in improving volumetric segmentation with large volume input.The efforts in this thesis are dedicated to exploring new chances of ultrasound imaging and making it robust in clinic, especially building a comprehensive system with high accuracy and reliability for automated prenatal ultrasound examinations.

ISBN: 9781392486108Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Ultrasound

Deep Learning for Ultrasound Image Analysis: From Feasibility to Robustness.
LDR:03381nmm a2200313 4500 001 2272109
005 20201102111707.5
008 220629s2019 ||||||||||||||||| ||eng d
020 $a 9781392486108
035 $a (MiAaPQ)AAI27784024
035 $a AAI27784024
040 $a MiAaPQ $c MiAaPQ
100 1 $a Yang, Xin. $3 1920980
245 1 0 $a Deep Learning for Ultrasound Image Analysis: From Feasibility to Robustness.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2019
300 $a 181 p.
500 $a Source: Dissertations Abstracts International, Volume: 81-09, Section: B.
502 $a Thesis (Ph.D.)--The Chinese University of Hong Kong (Hong Kong), 2019.
520 $a Ultrasound is a dominant and unique imaging modality in several important clinic scenarios, like surgical planning and prenatal examinations. Segmenting interest objects, localizing landmarks and measuring biometrics are frequently requested during ultrasound scanning. However, subject to the user dependency, manual analyses often present low reproducibility and high discrepancy. Automatic ultrasound image analyses are highly desired to improve the examination quality and extend the ultrasound to its full use in more applications.The great resurgence of deep learning brings breakthroughs for medical image analysis. Whereas, the challenges in ultrasound images, including the non-standard acquisition, poor image quality, varying appearance shift and large volume, still hinder the progress and robustness of deep learning solutions.In this thesis, we focus on improving the feasibility and robustness of deep learning for ultrasound image analysis. Our contributions are as follows. For application part: (a) we proposed the first deep learning method for prostate segmentation in 2D ultrasound images and achieved state-of-the-art performance. (b) we devised the first fully automated method to simultaneously segment fetus, gestational sac and placenta in ultrasound volumes. This study provides new opportunities for precise monitoring of fetal growth. (c) we created the first work about 3D fetal pose estimation in the literature, with the desire to build a general navigation map for many advanced studies. Tackling the challenges in ultrasound images, our methodology contributions are four-fold. (a) we proposed a novel sequentiality based method to address the boundary ambiguity for ultrasound segmentation. (b) we proposed a case adaptation strategy to cope with the appearance shift under different ultrasound imaging conditions. (c) we successfully customized the deep reinforcement learning to effectively narrow the search space for plane detection in ultrasound volumes. (d) we optimized the GPU memory management under limited GPU resources and proved our strategy in improving volumetric segmentation with large volume input.The efforts in this thesis are dedicated to exploring new chances of ultrasound imaging and making it robust in clinic, especially building a comprehensive system with high accuracy and reliability for automated prenatal ultrasound examinations.
590 $a School code: 1307.
650 4 $a Computer science. $3 523869
653 $a Ultrasound
653 $a Imaging modality
653 $a Biometrics
653 $a Deep learning
690 $a 0984
710 2 $a The Chinese University of Hong Kong (Hong Kong). $3 1017547
773 0 $t Dissertations Abstracts International $g 81-09B.
790 $a 1307
791 $a Ph.D.
792 $a 2019
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27784024