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

Spinal Cord Imaging in Multiple Scle...

Datta, Esha.

FindBook

Google Book

Amazon

博客來

Spinal Cord Imaging in Multiple Sclerosis Patients: Applications of Machine Learning and Computer Vision Methods.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Spinal Cord Imaging in Multiple Sclerosis Patients: Applications of Machine Learning and Computer Vision Methods./
作者:	Datta, Esha.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
面頁冊數:	103 p.
附註:	Source: Dissertation Abstracts International, Volume: 80-02(E), Section: B.
Contained By:	Dissertation Abstracts International80-02B(E).
標題:	Medical imaging. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10934091
ISBN:	9780438474499

Spinal Cord Imaging in Multiple Sclerosis Patients: Applications of Machine Learning and Computer Vision Methods.
Datta, Esha.

Spinal Cord Imaging in Multiple Sclerosis Patients: Applications of Machine Learning and Computer Vision Methods. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 103 p.

Source: Dissertation Abstracts International, Volume: 80-02(E), Section: B.

Thesis (Ph.D.)--University of California, San Francisco, 2018.

During the last few decades, researchers have struggled to find reliable biomarkers for multiple sclerosis (MS) that could aid in diagnosis, measurement of disease progression, evaluation of treatments in clinical trials, and prediction of treatment effect. Traditional metrics, such as brain and lesion volumes, are poor contenders since they do not reliably reflect clinical metrics. Until recently, spinal cord metrics were also poor contenders, due to the quality limitations of spinal cord imaging. However, with recent technological advances, we are now able to acquire better quality spinal cord images and capture these metrics more accurately. This thesis investigates the potential of using spinal cord images clinically in MS through four different studies. The first study investigates different spinal cord metrics and shows how spinal cord PSIR gradient independently predicts EDSS in RRMS patients. The second study demonstrates two different methods for how spinal cord gray matter can be automatically segmented so that metrics can be easily obtained in a clinical setting. The third study is an investigation of how spinal cord metrics change longitudinally. The fourth study is a voxel-wise analysis of spinal cord metrics that shows local patterns of intensity, gradient, and deformation.

ISBN: 9780438474499Subjects--Topical Terms:

3172799
Medical imaging.

Spinal Cord Imaging in Multiple Sclerosis Patients: Applications of Machine Learning and Computer Vision Methods.
LDR:02349nmm a2200325 4500 001 2202292
005 20190513114647.5
008 201008s2018 ||||||||||||||||| ||eng d
020 $a 9780438474499
035 $a (MiAaPQ)AAI10934091
035 $a (MiAaPQ)ucsf:11686
035 $a AAI10934091
040 $a MiAaPQ $c MiAaPQ
100 1 $a Datta, Esha. $3 3429036
245 1 0 $a Spinal Cord Imaging in Multiple Sclerosis Patients: Applications of Machine Learning and Computer Vision Methods.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2018
300 $a 103 p.
500 $a Source: Dissertation Abstracts International, Volume: 80-02(E), Section: B.
500 $a Adviser: Roland G. Henry.
502 $a Thesis (Ph.D.)--University of California, San Francisco, 2018.
520 $a During the last few decades, researchers have struggled to find reliable biomarkers for multiple sclerosis (MS) that could aid in diagnosis, measurement of disease progression, evaluation of treatments in clinical trials, and prediction of treatment effect. Traditional metrics, such as brain and lesion volumes, are poor contenders since they do not reliably reflect clinical metrics. Until recently, spinal cord metrics were also poor contenders, due to the quality limitations of spinal cord imaging. However, with recent technological advances, we are now able to acquire better quality spinal cord images and capture these metrics more accurately. This thesis investigates the potential of using spinal cord images clinically in MS through four different studies. The first study investigates different spinal cord metrics and shows how spinal cord PSIR gradient independently predicts EDSS in RRMS patients. The second study demonstrates two different methods for how spinal cord gray matter can be automatically segmented so that metrics can be easily obtained in a clinical setting. The third study is an investigation of how spinal cord metrics change longitudinally. The fourth study is a voxel-wise analysis of spinal cord metrics that shows local patterns of intensity, gradient, and deformation.
590 $a School code: 0034.
650 4 $a Medical imaging. $3 3172799
650 4 $a Artificial intelligence. $3 516317
650 4 $a Computer science. $3 523869
650 4 $a Bioengineering. $3 657580
690 $a 0574
690 $a 0800
690 $a 0984
690 $a 0202
710 2 $a University of California, San Francisco. $b Bioengineering. $3 1025199
773 0 $t Dissertation Abstracts International $g 80-02B(E).
790 $a 0034
791 $a Ph.D.
792 $a 2018
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10934091