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Medical Image Segmentation with Deep...

Wang, Chuanbo.

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Medical Image Segmentation with Deep Learning.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Medical Image Segmentation with Deep Learning./
Author:	Wang, Chuanbo.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	42 p.
Notes:	Source: Masters Abstracts International, Volume: 82-01.
Contained By:	Masters Abstracts International82-01.
Subject:	Electrical engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27961096
ISBN:	9798607384883

Medical Image Segmentation with Deep Learning.
Wang, Chuanbo.

Medical Image Segmentation with Deep Learning. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 42 p.

Source: Masters Abstracts International, Volume: 82-01.

Thesis (M.S.)--The University of Wisconsin - Milwaukee, 2020.

This item must not be sold to any third party vendors.

Medical imaging is the technique and process of creating visual representations of the body of a patient for clinical analysis and medical intervention. Healthcare professionals rely heavily on medical images and image documentation for proper diagnosis and treatment. However, manual interpretation and analysis of medical images is time-consuming, and inaccurate when the interpreter is not well-trained. Fully automatic segmentation of the region of interest from medical images have been researched for years to enhance the efficiency and accuracy of understanding such images. With the advance of deep learning, various neural network models have gained great success in semantic segmentation and spark research interests in medical image segmentation using deep learning. We propose two convolutional frameworks to segment tissues from different types of medical images. Comprehensive experiments and analyses are conducted on various segmentation neural networks to demonstrate the effectiveness of our methods. Furthermore, datasets built for training our networks and full implementations are published.

ISBN: 9798607384883Subjects--Topical Terms:

649834
Electrical engineering.
Subjects--Index Terms:

Convolutional neural networks

Medical Image Segmentation with Deep Learning.
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300 $a 42 p.
500 $a Source: Masters Abstracts International, Volume: 82-01.
500 $a Advisor: Yu, Zeyun.
502 $a Thesis (M.S.)--The University of Wisconsin - Milwaukee, 2020.
506 $a This item must not be sold to any third party vendors.
520 $a Medical imaging is the technique and process of creating visual representations of the body of a patient for clinical analysis and medical intervention. Healthcare professionals rely heavily on medical images and image documentation for proper diagnosis and treatment. However, manual interpretation and analysis of medical images is time-consuming, and inaccurate when the interpreter is not well-trained. Fully automatic segmentation of the region of interest from medical images have been researched for years to enhance the efficiency and accuracy of understanding such images. With the advance of deep learning, various neural network models have gained great success in semantic segmentation and spark research interests in medical image segmentation using deep learning. We propose two convolutional frameworks to segment tissues from different types of medical images. Comprehensive experiments and analyses are conducted on various segmentation neural networks to demonstrate the effectiveness of our methods. Furthermore, datasets built for training our networks and full implementations are published.
590 $a School code: 0263.
650 4 $a Electrical engineering. $3 649834
650 4 $a Biomedical engineering. $3 535387
650 4 $a Medical imaging. $3 3172799
653 $a Convolutional neural networks
653 $a Deep learning
653 $a Medical image segmentation
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773 0 $t Masters Abstracts International $g 82-01.
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