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Target area distillation and section attention segmentation network for accurate 3D medical image segmentation
3D medical image segmentation has an essential role in medical image analysis, while attention mechanism has improved the performance by a large margin. However, existing methods obtained the attention coefficient in a small receptive field, resulting in possible performance limitations. Radiologist...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9884720/ https://www.ncbi.nlm.nih.gov/pubmed/36721638 http://dx.doi.org/10.1007/s13755-022-00200-z |
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author | Xie, Ruiwei Pan, Dan Zeng, An Xu, Xiaowei Wang, Tianchen Ullah, Najeeb Ji, Yuzhu |
author_facet | Xie, Ruiwei Pan, Dan Zeng, An Xu, Xiaowei Wang, Tianchen Ullah, Najeeb Ji, Yuzhu |
author_sort | Xie, Ruiwei |
collection | PubMed |
description | 3D medical image segmentation has an essential role in medical image analysis, while attention mechanism has improved the performance by a large margin. However, existing methods obtained the attention coefficient in a small receptive field, resulting in possible performance limitations. Radiologists usually scan all the slices first to have an overall idea of the target, and then analyze regions of interest in multiple 2D views in clinic practice. We simulate radiologists’ recognition process and propose to exploit the 3D context information in a deeper manner for accurate 3D medical images segmentation. Due to the similarity of human body structure, medical images of different populations have highly similar shape and location information, so we use target region distillation to extract the common segmented region information. Particularly, we proposed two optimizations including Target Area Distillation and Section Attention. Target Area Distillation adds positions information to the original input to let the network has an initial attention of the target, while section attention performs attention extraction in three 2D sections thus with large range of receptive field. We compare our method against several popular networks in two public datasets including ImageCHD and COVID-19. Experimental results show that our proposed method improves the segmentation Dice score by 2–4% over the state-of-the-art methods. Our code has been released to the public (Anonymous link). |
format | Online Article Text |
id | pubmed-9884720 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-98847202023-01-30 Target area distillation and section attention segmentation network for accurate 3D medical image segmentation Xie, Ruiwei Pan, Dan Zeng, An Xu, Xiaowei Wang, Tianchen Ullah, Najeeb Ji, Yuzhu Health Inf Sci Syst Research 3D medical image segmentation has an essential role in medical image analysis, while attention mechanism has improved the performance by a large margin. However, existing methods obtained the attention coefficient in a small receptive field, resulting in possible performance limitations. Radiologists usually scan all the slices first to have an overall idea of the target, and then analyze regions of interest in multiple 2D views in clinic practice. We simulate radiologists’ recognition process and propose to exploit the 3D context information in a deeper manner for accurate 3D medical images segmentation. Due to the similarity of human body structure, medical images of different populations have highly similar shape and location information, so we use target region distillation to extract the common segmented region information. Particularly, we proposed two optimizations including Target Area Distillation and Section Attention. Target Area Distillation adds positions information to the original input to let the network has an initial attention of the target, while section attention performs attention extraction in three 2D sections thus with large range of receptive field. We compare our method against several popular networks in two public datasets including ImageCHD and COVID-19. Experimental results show that our proposed method improves the segmentation Dice score by 2–4% over the state-of-the-art methods. Our code has been released to the public (Anonymous link). Springer International Publishing 2023-01-30 /pmc/articles/PMC9884720/ /pubmed/36721638 http://dx.doi.org/10.1007/s13755-022-00200-z Text en © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
spellingShingle | Research Xie, Ruiwei Pan, Dan Zeng, An Xu, Xiaowei Wang, Tianchen Ullah, Najeeb Ji, Yuzhu Target area distillation and section attention segmentation network for accurate 3D medical image segmentation |
title | Target area distillation and section attention segmentation network for accurate 3D medical image segmentation |
title_full | Target area distillation and section attention segmentation network for accurate 3D medical image segmentation |
title_fullStr | Target area distillation and section attention segmentation network for accurate 3D medical image segmentation |
title_full_unstemmed | Target area distillation and section attention segmentation network for accurate 3D medical image segmentation |
title_short | Target area distillation and section attention segmentation network for accurate 3D medical image segmentation |
title_sort | target area distillation and section attention segmentation network for accurate 3d medical image segmentation |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9884720/ https://www.ncbi.nlm.nih.gov/pubmed/36721638 http://dx.doi.org/10.1007/s13755-022-00200-z |
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