Cargando…

U-Net-Based Models towards Optimal MR Brain Image Segmentation

Brain tumor segmentation from MRIs has always been a challenging task for radiologists, therefore, an automatic and generalized system to address this task is needed. Among all other deep learning techniques used in medical imaging, U-Net-based variants are the most used models found in the literatu...

Descripción completa

Detalles Bibliográficos
Autores principales: Yousef, Rammah, Khan, Shakir, Gupta, Gaurav, Siddiqui, Tamanna, Albahlal, Bader M., Alajlan, Saad Abdullah, Haq, Mohd Anul
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10178263/
https://www.ncbi.nlm.nih.gov/pubmed/37175015
http://dx.doi.org/10.3390/diagnostics13091624
_version_ 1785040820139720704
author Yousef, Rammah
Khan, Shakir
Gupta, Gaurav
Siddiqui, Tamanna
Albahlal, Bader M.
Alajlan, Saad Abdullah
Haq, Mohd Anul
author_facet Yousef, Rammah
Khan, Shakir
Gupta, Gaurav
Siddiqui, Tamanna
Albahlal, Bader M.
Alajlan, Saad Abdullah
Haq, Mohd Anul
author_sort Yousef, Rammah
collection PubMed
description Brain tumor segmentation from MRIs has always been a challenging task for radiologists, therefore, an automatic and generalized system to address this task is needed. Among all other deep learning techniques used in medical imaging, U-Net-based variants are the most used models found in the literature to segment medical images with respect to different modalities. Therefore, the goal of this paper is to examine the numerous advancements and innovations in the U-Net architecture, as well as recent trends, with the aim of highlighting the ongoing potential of U-Net being used to better the performance of brain tumor segmentation. Furthermore, we provide a quantitative comparison of different U-Net architectures to highlight the performance and the evolution of this network from an optimization perspective. In addition to that, we have experimented with four U-Net architectures (3D U-Net, Attention U-Net, R2 Attention U-Net, and modified 3D U-Net) on the BraTS 2020 dataset for brain tumor segmentation to provide a better overview of this architecture’s performance in terms of Dice score and Hausdorff distance 95%. Finally, we analyze the limitations and challenges of medical image analysis to provide a critical discussion about the importance of developing new architectures in terms of optimization.
format Online
Article
Text
id pubmed-10178263
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-101782632023-05-13 U-Net-Based Models towards Optimal MR Brain Image Segmentation Yousef, Rammah Khan, Shakir Gupta, Gaurav Siddiqui, Tamanna Albahlal, Bader M. Alajlan, Saad Abdullah Haq, Mohd Anul Diagnostics (Basel) Article Brain tumor segmentation from MRIs has always been a challenging task for radiologists, therefore, an automatic and generalized system to address this task is needed. Among all other deep learning techniques used in medical imaging, U-Net-based variants are the most used models found in the literature to segment medical images with respect to different modalities. Therefore, the goal of this paper is to examine the numerous advancements and innovations in the U-Net architecture, as well as recent trends, with the aim of highlighting the ongoing potential of U-Net being used to better the performance of brain tumor segmentation. Furthermore, we provide a quantitative comparison of different U-Net architectures to highlight the performance and the evolution of this network from an optimization perspective. In addition to that, we have experimented with four U-Net architectures (3D U-Net, Attention U-Net, R2 Attention U-Net, and modified 3D U-Net) on the BraTS 2020 dataset for brain tumor segmentation to provide a better overview of this architecture’s performance in terms of Dice score and Hausdorff distance 95%. Finally, we analyze the limitations and challenges of medical image analysis to provide a critical discussion about the importance of developing new architectures in terms of optimization. MDPI 2023-05-04 /pmc/articles/PMC10178263/ /pubmed/37175015 http://dx.doi.org/10.3390/diagnostics13091624 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Yousef, Rammah
Khan, Shakir
Gupta, Gaurav
Siddiqui, Tamanna
Albahlal, Bader M.
Alajlan, Saad Abdullah
Haq, Mohd Anul
U-Net-Based Models towards Optimal MR Brain Image Segmentation
title U-Net-Based Models towards Optimal MR Brain Image Segmentation
title_full U-Net-Based Models towards Optimal MR Brain Image Segmentation
title_fullStr U-Net-Based Models towards Optimal MR Brain Image Segmentation
title_full_unstemmed U-Net-Based Models towards Optimal MR Brain Image Segmentation
title_short U-Net-Based Models towards Optimal MR Brain Image Segmentation
title_sort u-net-based models towards optimal mr brain image segmentation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10178263/
https://www.ncbi.nlm.nih.gov/pubmed/37175015
http://dx.doi.org/10.3390/diagnostics13091624
work_keys_str_mv AT youseframmah unetbasedmodelstowardsoptimalmrbrainimagesegmentation
AT khanshakir unetbasedmodelstowardsoptimalmrbrainimagesegmentation
AT guptagaurav unetbasedmodelstowardsoptimalmrbrainimagesegmentation
AT siddiquitamanna unetbasedmodelstowardsoptimalmrbrainimagesegmentation
AT albahlalbaderm unetbasedmodelstowardsoptimalmrbrainimagesegmentation
AT alajlansaadabdullah unetbasedmodelstowardsoptimalmrbrainimagesegmentation
AT haqmohdanul unetbasedmodelstowardsoptimalmrbrainimagesegmentation