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Brain lesion image segmentation using modified U-NET architecture

Detecting stroke is important to reduce the likelihood of permanent disability and increase the chance of recovery. Brain stroke lesion segmentation is an important procedure, especially when a specific brain portion needs to be analyzed. In this project, a brain stroke lesion segmentation algorithm...

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Main Authors: Lee, Xin Yin, Mohd Jamil, Mohamed Mokhtarudin, Ramli, Junid
Format: Conference or Workshop Item
Language:English
English
Published: Springer 2024
Subjects:
QA75 Electronic computers. Computer science
TJ Mechanical engineering and machinery
TS Manufactures
Online Access:http://umpir.ump.edu.my/id/eprint/41975/1/Brain%20lesion%20image%20segmentation%20using%20modified%20U-NET.pdf
http://umpir.ump.edu.my/id/eprint/41975/2/Intelligent%20Manufacturing%20and%20Mechatronics.pdf
http://umpir.ump.edu.my/id/eprint/41975/
https://doi.org/10.1007/978-981-99-8819-8_46
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spelling my.ump.umpir.419752024-07-17T04:12:02Z http://umpir.ump.edu.my/id/eprint/41975/ Brain lesion image segmentation using modified U-NET architecture Lee, Xin Yin Mohd Jamil, Mohamed Mokhtarudin Ramli, Junid QA75 Electronic computers. Computer science TJ Mechanical engineering and machinery TS Manufactures Detecting stroke is important to reduce the likelihood of permanent disability and increase the chance of recovery. Brain stroke lesion segmentation is an important procedure, especially when a specific brain portion needs to be analyzed. In this project, a brain stroke lesion segmentation algorithm using a modified U-Net (MUN) architecture will be developed. The MUN has a dimension-fusion capability, in which the images are analyzed separately using 2D U-Net and 3D image downsampling processes, before being fused at two points during the downsampling processes. The MUN accuracy is then compared with a regular 3D U-Net (UN). Three training options are further developed and compared. It is found that the MUN architecture produces higher training accuracy, but slower training duration compared to UN. Despite the capabilities of MUN, it cannot be further validated due to software limitations. Further improvement on the algorithm using other libraries is essential to enhance the capability of the MUN. Springer 2024 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/41975/1/Brain%20lesion%20image%20segmentation%20using%20modified%20U-NET.pdf pdf en http://umpir.ump.edu.my/id/eprint/41975/2/Intelligent%20Manufacturing%20and%20Mechatronics.pdf Lee, Xin Yin and Mohd Jamil, Mohamed Mokhtarudin and Ramli, Junid (2024) Brain lesion image segmentation using modified U-NET architecture. In: Lecture Notes in Networks and Systems; 4th International conference on Innovative Manufacturing, Mechatronics and Materials Forum, iM3F2023 , 7 - 8 August 2023 , Pekan, Pahang. 549 -555., 850. ISSN 2367-3370 ISBN 978-981998818-1 https://doi.org/10.1007/978-981-99-8819-8_46
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
English
topic QA75 Electronic computers. Computer science
TJ Mechanical engineering and machinery
TS Manufactures
spellingShingle QA75 Electronic computers. Computer science
TJ Mechanical engineering and machinery
TS Manufactures
Lee, Xin Yin
Mohd Jamil, Mohamed Mokhtarudin
Ramli, Junid
Brain lesion image segmentation using modified U-NET architecture
description Detecting stroke is important to reduce the likelihood of permanent disability and increase the chance of recovery. Brain stroke lesion segmentation is an important procedure, especially when a specific brain portion needs to be analyzed. In this project, a brain stroke lesion segmentation algorithm using a modified U-Net (MUN) architecture will be developed. The MUN has a dimension-fusion capability, in which the images are analyzed separately using 2D U-Net and 3D image downsampling processes, before being fused at two points during the downsampling processes. The MUN accuracy is then compared with a regular 3D U-Net (UN). Three training options are further developed and compared. It is found that the MUN architecture produces higher training accuracy, but slower training duration compared to UN. Despite the capabilities of MUN, it cannot be further validated due to software limitations. Further improvement on the algorithm using other libraries is essential to enhance the capability of the MUN.
format Conference or Workshop Item
author Lee, Xin Yin
Mohd Jamil, Mohamed Mokhtarudin
Ramli, Junid
author_facet Lee, Xin Yin
Mohd Jamil, Mohamed Mokhtarudin
Ramli, Junid
author_sort Lee, Xin Yin
title Brain lesion image segmentation using modified U-NET architecture
title_short Brain lesion image segmentation using modified U-NET architecture
title_full Brain lesion image segmentation using modified U-NET architecture
title_fullStr Brain lesion image segmentation using modified U-NET architecture
title_full_unstemmed Brain lesion image segmentation using modified U-NET architecture
title_sort brain lesion image segmentation using modified u-net architecture
publisher Springer
publishDate 2024
url http://umpir.ump.edu.my/id/eprint/41975/1/Brain%20lesion%20image%20segmentation%20using%20modified%20U-NET.pdf
http://umpir.ump.edu.my/id/eprint/41975/2/Intelligent%20Manufacturing%20and%20Mechatronics.pdf
http://umpir.ump.edu.my/id/eprint/41975/
https://doi.org/10.1007/978-981-99-8819-8_46
_version_ 1822924483380379648
score 13.232414

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