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Medical Image Segmentation with Learning Semantic and Global Contextual Representation

Automatic medical image segmentation is an essential step toward accurate diseases diagnosis and designing a follow-up treatment. This assistive method facilitates the cancer detection process and provides a benchmark to highlight the affected area. The U-Net model has become the standard design cho...

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Autor principal: Alahmadi, Mohammad D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9319384/
https://www.ncbi.nlm.nih.gov/pubmed/35885454
http://dx.doi.org/10.3390/diagnostics12071548
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author Alahmadi, Mohammad D.
author_facet Alahmadi, Mohammad D.
author_sort Alahmadi, Mohammad D.
collection PubMed
description Automatic medical image segmentation is an essential step toward accurate diseases diagnosis and designing a follow-up treatment. This assistive method facilitates the cancer detection process and provides a benchmark to highlight the affected area. The U-Net model has become the standard design choice. Although the symmetrical structure of the U-Net model enables this network to encode rich semantic representation, the intrinsic locality of the CNN layers limits this network’s capability in modeling long-range contextual dependency. On the other hand, sequence to sequence Transformer models with a multi-head attention mechanism can enable them to effectively model global contextual dependency. However, the lack of low-level information stemming from the Transformer architecture limits its performance for capturing local representation. In this paper, we propose a two parallel encoder model, where in the first path the CNN module captures the local semantic representation whereas the second path deploys a Transformer module to extract the long-range contextual representation. Next, by adaptively fusing these two feature maps, we encode both representations into a single representative tensor to be further processed by the decoder block. An experimental study demonstrates that our design can provide rich and generic representation features which are highly efficient for a fine-grained semantic segmentation task.
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spelling pubmed-93193842022-07-27 Medical Image Segmentation with Learning Semantic and Global Contextual Representation Alahmadi, Mohammad D. Diagnostics (Basel) Article Automatic medical image segmentation is an essential step toward accurate diseases diagnosis and designing a follow-up treatment. This assistive method facilitates the cancer detection process and provides a benchmark to highlight the affected area. The U-Net model has become the standard design choice. Although the symmetrical structure of the U-Net model enables this network to encode rich semantic representation, the intrinsic locality of the CNN layers limits this network’s capability in modeling long-range contextual dependency. On the other hand, sequence to sequence Transformer models with a multi-head attention mechanism can enable them to effectively model global contextual dependency. However, the lack of low-level information stemming from the Transformer architecture limits its performance for capturing local representation. In this paper, we propose a two parallel encoder model, where in the first path the CNN module captures the local semantic representation whereas the second path deploys a Transformer module to extract the long-range contextual representation. Next, by adaptively fusing these two feature maps, we encode both representations into a single representative tensor to be further processed by the decoder block. An experimental study demonstrates that our design can provide rich and generic representation features which are highly efficient for a fine-grained semantic segmentation task. MDPI 2022-06-25 /pmc/articles/PMC9319384/ /pubmed/35885454 http://dx.doi.org/10.3390/diagnostics12071548 Text en © 2022 by the author. 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
Alahmadi, Mohammad D.
Medical Image Segmentation with Learning Semantic and Global Contextual Representation
title Medical Image Segmentation with Learning Semantic and Global Contextual Representation
title_full Medical Image Segmentation with Learning Semantic and Global Contextual Representation
title_fullStr Medical Image Segmentation with Learning Semantic and Global Contextual Representation
title_full_unstemmed Medical Image Segmentation with Learning Semantic and Global Contextual Representation
title_short Medical Image Segmentation with Learning Semantic and Global Contextual Representation
title_sort medical image segmentation with learning semantic and global contextual representation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9319384/
https://www.ncbi.nlm.nih.gov/pubmed/35885454
http://dx.doi.org/10.3390/diagnostics12071548
work_keys_str_mv AT alahmadimohammadd medicalimagesegmentationwithlearningsemanticandglobalcontextualrepresentation