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Self-Supervised Wavelet-Based Attention Network for Semantic Segmentation of MRI Brain Tumor

To determine the appropriate treatment plan for patients, radiologists must reliably detect brain tumors. Despite the fact that manual segmentation involves a great deal of knowledge and ability, it may sometimes be inaccurate. By evaluating the size, location, structure, and grade of the tumor, aut...

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Autores principales: Anusooya, Govindarajan, Bharathiraja, Selvaraj, Mahdal, Miroslav, Sathyarajasekaran, Kamsundher, Elangovan, Muniyandy
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10007092/
https://www.ncbi.nlm.nih.gov/pubmed/36904923
http://dx.doi.org/10.3390/s23052719
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author Anusooya, Govindarajan
Bharathiraja, Selvaraj
Mahdal, Miroslav
Sathyarajasekaran, Kamsundher
Elangovan, Muniyandy
author_facet Anusooya, Govindarajan
Bharathiraja, Selvaraj
Mahdal, Miroslav
Sathyarajasekaran, Kamsundher
Elangovan, Muniyandy
author_sort Anusooya, Govindarajan
collection PubMed
description To determine the appropriate treatment plan for patients, radiologists must reliably detect brain tumors. Despite the fact that manual segmentation involves a great deal of knowledge and ability, it may sometimes be inaccurate. By evaluating the size, location, structure, and grade of the tumor, automatic tumor segmentation in MRI images aids in a more thorough analysis of pathological conditions. Due to the intensity differences in MRI images, gliomas may spread out, have low contrast, and are therefore difficult to detect. As a result, segmenting brain tumors is a challenging process. In the past, several methods for segmenting brain tumors in MRI scans were created. However, because of their susceptibility to noise and distortions, the usefulness of these approaches is limited. Self-Supervised Wavele- based Attention Network (SSW-AN), a new attention module with adjustable self-supervised activation functions and dynamic weights, is what we suggest as a way to collect global context information. In particular, this network’s input and labels are made up of four parameters produced by the two-dimensional (2D) Wavelet transform, which makes the training process simpler by neatly segmenting the data into low-frequency and high-frequency channels. To be more precise, we make use of the channel attention and spatial attention modules of the self-supervised attention block (SSAB). As a result, this method may more easily zero in on crucial underlying channels and spatial patterns. The suggested SSW-AN has been shown to outperform the current state-of-the-art algorithms in medical image segmentation tasks, with more accuracy, more promising dependability, and less unnecessary redundancy.
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spelling pubmed-100070922023-03-12 Self-Supervised Wavelet-Based Attention Network for Semantic Segmentation of MRI Brain Tumor Anusooya, Govindarajan Bharathiraja, Selvaraj Mahdal, Miroslav Sathyarajasekaran, Kamsundher Elangovan, Muniyandy Sensors (Basel) Article To determine the appropriate treatment plan for patients, radiologists must reliably detect brain tumors. Despite the fact that manual segmentation involves a great deal of knowledge and ability, it may sometimes be inaccurate. By evaluating the size, location, structure, and grade of the tumor, automatic tumor segmentation in MRI images aids in a more thorough analysis of pathological conditions. Due to the intensity differences in MRI images, gliomas may spread out, have low contrast, and are therefore difficult to detect. As a result, segmenting brain tumors is a challenging process. In the past, several methods for segmenting brain tumors in MRI scans were created. However, because of their susceptibility to noise and distortions, the usefulness of these approaches is limited. Self-Supervised Wavele- based Attention Network (SSW-AN), a new attention module with adjustable self-supervised activation functions and dynamic weights, is what we suggest as a way to collect global context information. In particular, this network’s input and labels are made up of four parameters produced by the two-dimensional (2D) Wavelet transform, which makes the training process simpler by neatly segmenting the data into low-frequency and high-frequency channels. To be more precise, we make use of the channel attention and spatial attention modules of the self-supervised attention block (SSAB). As a result, this method may more easily zero in on crucial underlying channels and spatial patterns. The suggested SSW-AN has been shown to outperform the current state-of-the-art algorithms in medical image segmentation tasks, with more accuracy, more promising dependability, and less unnecessary redundancy. MDPI 2023-03-02 /pmc/articles/PMC10007092/ /pubmed/36904923 http://dx.doi.org/10.3390/s23052719 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
Anusooya, Govindarajan
Bharathiraja, Selvaraj
Mahdal, Miroslav
Sathyarajasekaran, Kamsundher
Elangovan, Muniyandy
Self-Supervised Wavelet-Based Attention Network for Semantic Segmentation of MRI Brain Tumor
title Self-Supervised Wavelet-Based Attention Network for Semantic Segmentation of MRI Brain Tumor
title_full Self-Supervised Wavelet-Based Attention Network for Semantic Segmentation of MRI Brain Tumor
title_fullStr Self-Supervised Wavelet-Based Attention Network for Semantic Segmentation of MRI Brain Tumor
title_full_unstemmed Self-Supervised Wavelet-Based Attention Network for Semantic Segmentation of MRI Brain Tumor
title_short Self-Supervised Wavelet-Based Attention Network for Semantic Segmentation of MRI Brain Tumor
title_sort self-supervised wavelet-based attention network for semantic segmentation of mri brain tumor
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10007092/
https://www.ncbi.nlm.nih.gov/pubmed/36904923
http://dx.doi.org/10.3390/s23052719
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