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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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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. |
format | Online Article Text |
id | pubmed-10007092 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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