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Computer-Aided Diagnosis of Tumors w...

Ta, Casey Nghia.

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Computer-Aided Diagnosis of Tumors with Contrast-Enhanced Ultrasound.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Computer-Aided Diagnosis of Tumors with Contrast-Enhanced Ultrasound./
Author:	Ta, Casey Nghia.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2015,
Description:	150 p.
Notes:	Source: Dissertation Abstracts International, Volume: 77-02(E), Section: B.
Contained By:	Dissertation Abstracts International77-02B(E).
Subject:	Medical imaging. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3725227
ISBN:	9781339091914

Computer-Aided Diagnosis of Tumors with Contrast-Enhanced Ultrasound.
Ta, Casey Nghia.

Computer-Aided Diagnosis of Tumors with Contrast-Enhanced Ultrasound. - Ann Arbor : ProQuest Dissertations & Theses, 2015 - 150 p.

Source: Dissertation Abstracts International, Volume: 77-02(E), Section: B.

Thesis (Ph.D.)--University of California, San Diego, 2015.

This item is not available from ProQuest Dissertations & Theses.

In the fight against cancer, early detection and accurate diagnosis of tumors are critical. Advances over the past decade with contrast-enhanced ultrasound (CEUS) have enabled real time imaging of tumor vasculature, improving ultrasound's diagnostic potential. Additionally, CEUS is cheaper and safer than other medical imaging modalities, but analysis of CEUS requires highly experienced radiologists to accurately and reliably diagnose tumors. To overcome this issue, a computer-aided diagnosis (CAD) system for CEUS was developed to differentiate benign and malignant tumors.

ISBN: 9781339091914Subjects--Topical Terms:

3172799
Medical imaging.

Computer-Aided Diagnosis of Tumors with Contrast-Enhanced Ultrasound.
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100 1 $a Ta, Casey Nghia. $3 3286165
245 1 0 $a Computer-Aided Diagnosis of Tumors with Contrast-Enhanced Ultrasound.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2015
300 $a 150 p.
500 $a Source: Dissertation Abstracts International, Volume: 77-02(E), Section: B.
500 $a Advisers: Sadik Esener; Andrew Kummel.
502 $a Thesis (Ph.D.)--University of California, San Diego, 2015.
506 $a This item is not available from ProQuest Dissertations & Theses.
520 $a In the fight against cancer, early detection and accurate diagnosis of tumors are critical. Advances over the past decade with contrast-enhanced ultrasound (CEUS) have enabled real time imaging of tumor vasculature, improving ultrasound's diagnostic potential. Additionally, CEUS is cheaper and safer than other medical imaging modalities, but analysis of CEUS requires highly experienced radiologists to accurately and reliably diagnose tumors. To overcome this issue, a computer-aided diagnosis (CAD) system for CEUS was developed to differentiate benign and malignant tumors.
520 $a A prototype CAD system was developed using data from a murine breast tumor model. CEUS cines of microbubble bolus injections were acquired continuously to capture microbubble wash-in and wash-out. The time-intensity curves were analyzed on a pixel-by-pixel basis to measure kinetic parameters throughout the tumor. Linear discriminant analysis was used to differentiate benign and malignant tumors, achieving 100% cross-validation accuracy. While traditional region-of-interest analyses could measure general tumor perfusion, pixel-by-pixel analysis made possible the detection of blood flow heterogeneity, which was shown to improve tumor differentiation.
520 $a Although results in animal tumor models were promising, human clinical CEUS was challenging for CAD. Clinical CEUS acquired during free breathing suffer from in-plane and out-of-plane motion, reducing accuracy of quantitative measurements. To reduce motion artifacts, 2-tier in-plane motion correction (IPMC) and out-of-plane motion filtering (OPMF) algorithms were designed and tested on CEUS of focal liver lesions (FLLs). The 2-tier IPMC strategy provided stable motion correction and OPMF reduced apparent motion throughout the cine. These algorithms significantly improved visual stability and quantitative analysis of tumor perfusion.
520 $a Finally, a CAD system was developed on clinically acquired CEUS bolus injection cines of FLLs. The previously formulated quantitative techniques were combined with newly developed algorithms to detect enhancement and flow patterns known to differentiate FLLs. Support vector machines and artificial neural networks were employed to classify lesions as benign or malignant, achieving 84.6% accuracy in the untrained testing set. The methods developed here have great potential to improve cancer care globally by aiding physicians to differentiate the disease and determine the optimal treatment plan, thus allowing more patients to receive the care that they need.
590 $a School code: 0033.
650 4 $a Medical imaging. $3 3172799
650 4 $a Computer science. $3 523869
650 4 $a Biomedical engineering. $3 535387
690 $a 0574
690 $a 0984
690 $a 0541
710 2 $a University of California, San Diego. $b Electrical Engineering. $3 1669302
773 0 $t Dissertation Abstracts International $g 77-02B(E).
790 $a 0033
791 $a Ph.D.
792 $a 2015
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3725227