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A New Image Quantitative Method for ...

Xue, Wenzhe.

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A New Image Quantitative Method for Diagnosis and Therapeutic Response.

Record Type:	Electronic resources : Monograph/item
Title/Author:	A New Image Quantitative Method for Diagnosis and Therapeutic Response./
Author:	Xue, Wenzhe.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2016,
Description:	105 p.
Notes:	Source: Dissertation Abstracts International, Volume: 78-04(E), Section: B.
Contained By:	Dissertation Abstracts International78-04B(E).
Subject:	Medical imaging. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10244564
ISBN:	9781369401882

A New Image Quantitative Method for Diagnosis and Therapeutic Response.
Xue, Wenzhe.

A New Image Quantitative Method for Diagnosis and Therapeutic Response. - Ann Arbor : ProQuest Dissertations & Theses, 2016 - 105 p.

Source: Dissertation Abstracts International, Volume: 78-04(E), Section: B.

Thesis (Ph.D.)--Arizona State University, 2016.

Accurate quantitative information of tumor/lesion volume plays a critical role in diagnosis and treatment assessment. The current clinical practice emphasizes on efficiency, but sacrifices accuracy (bias and precision). In the other hand, many computational algorithms focus on improving the accuracy, but are often time consuming and cumbersome to use. Not to mention that most of them lack validation studies on real clinical data. All of these hinder the translation of these advanced methods from benchside to bedside.

ISBN: 9781369401882Subjects--Topical Terms:

3172799
Medical imaging.

A New Image Quantitative Method for Diagnosis and Therapeutic Response.
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100 1 $a Xue, Wenzhe. $3 3287221
245 1 2 $a A New Image Quantitative Method for Diagnosis and Therapeutic Response.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2016
300 $a 105 p.
500 $a Source: Dissertation Abstracts International, Volume: 78-04(E), Section: B.
500 $a Advisers: David Kaufman; J. Ross Mitchell.
502 $a Thesis (Ph.D.)--Arizona State University, 2016.
520 $a Accurate quantitative information of tumor/lesion volume plays a critical role in diagnosis and treatment assessment. The current clinical practice emphasizes on efficiency, but sacrifices accuracy (bias and precision). In the other hand, many computational algorithms focus on improving the accuracy, but are often time consuming and cumbersome to use. Not to mention that most of them lack validation studies on real clinical data. All of these hinder the translation of these advanced methods from benchside to bedside.
520 $a In this dissertation, I present a user interactive image application to rapidly extract accurate quantitative information of abnormalities (tumor/lesion) from multi-spectral medical images, such as measuring brain tumor volume from MRI. This is enabled by a GPU level set method, an intelligent algorithm to learn image features from user inputs, and a simple and intuitive graphical user interface with 2D/3D visualization. In addition, a comprehensive workflow is presented to validate image quantitative methods for clinical studies.
520 $a This application has been evaluated and validated in multiple cases, including quantifying healthy brain white matter volume from MRI and brain lesion volume from CT or MRI. The evaluation studies show that this application has been able to achieve comparable results to the state-of-the-art computer algorithms. More importantly, the retrospective validation study on measuring intracerebral hemorrhage volume from CT scans demonstrates that not only the measurement attributes are superior to the current practice method in terms of bias and precision but also it is achieved without a significant delay in acquisition time. In other words, it could be useful to the clinical trials and clinical practice, especially when intervention and prognostication rely upon accurate baseline lesion volume or upon detecting change in serial lesion volumetric measurements. Obviously, this application is useful to biomedical research areas which desire an accurate quantitative information of anatomies from medical images. In addition, the morphological information is retained also. This is useful to researches which require an accurate delineation of anatomic structures, such as surgery simulation and planning.
590 $a School code: 0010.
650 4 $a Medical imaging. $3 3172799
650 4 $a Computer science. $3 523869
650 4 $a Biomedical engineering. $3 535387
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710 2 $a Arizona State University. $b Biomedical Informatics. $3 1680799
773 0 $t Dissertation Abstracts International $g 78-04B(E).
790 $a 0010
791 $a Ph.D.
792 $a 2016
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10244564