Multi-scale U-like network with attention mechanism for automatic pancreas segmentation.
In recent years, the rapid development of deep neural networks has made great progress in automatic organ segmentation from abdominal CT scans. However, automatic segmentation for small organs (e.g., the pancreas) is still a challenging task. As an inconspicuous and small organ in the abdomen, the p...
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Public Library of Science (PLoS)
2021-01-01
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| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0252287&type=printable |
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| author | Yingjing Yan Defu Zhang |
| author_facet | Yingjing Yan Defu Zhang |
| author_sort | Yingjing Yan |
| collection | DOAJ |
| description | In recent years, the rapid development of deep neural networks has made great progress in automatic organ segmentation from abdominal CT scans. However, automatic segmentation for small organs (e.g., the pancreas) is still a challenging task. As an inconspicuous and small organ in the abdomen, the pancreas has a high degree of anatomical variability and is indistinguishable from the surrounding organs and tissues, which usually leads to a very vague boundary. Therefore, the accuracy of pancreatic segmentation is sometimes below satisfaction. In this paper, we propose a 2.5D U-net with an attention mechanism. The proposed network includes 2D convolutional layers and 3D convolutional layers, which means that it requires less computational resources than 3D segmentation models while it can capture more spatial information along the third dimension than 2D segmentation models. Then We use a cascaded framework to increase the accuracy of segmentation results. We evaluate our network on the NIH pancreas dataset and measure the segmentation accuracy by the Dice similarity coefficient (DSC). Experimental results demonstrate a better performance compared with state-of-the-art methods. |
| format | Article |
| id | doaj-art-5a603d6f3dc84edcb0601716aee734c3 |
| institution | OA Journals |
| issn | 1932-6203 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-5a603d6f3dc84edcb0601716aee734c32025-08-20T02:01:04ZengPublic Library of Science (PLoS)PLoS ONE1932-62032021-01-01165e025228710.1371/journal.pone.0252287Multi-scale U-like network with attention mechanism for automatic pancreas segmentation.Yingjing YanDefu ZhangIn recent years, the rapid development of deep neural networks has made great progress in automatic organ segmentation from abdominal CT scans. However, automatic segmentation for small organs (e.g., the pancreas) is still a challenging task. As an inconspicuous and small organ in the abdomen, the pancreas has a high degree of anatomical variability and is indistinguishable from the surrounding organs and tissues, which usually leads to a very vague boundary. Therefore, the accuracy of pancreatic segmentation is sometimes below satisfaction. In this paper, we propose a 2.5D U-net with an attention mechanism. The proposed network includes 2D convolutional layers and 3D convolutional layers, which means that it requires less computational resources than 3D segmentation models while it can capture more spatial information along the third dimension than 2D segmentation models. Then We use a cascaded framework to increase the accuracy of segmentation results. We evaluate our network on the NIH pancreas dataset and measure the segmentation accuracy by the Dice similarity coefficient (DSC). Experimental results demonstrate a better performance compared with state-of-the-art methods.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0252287&type=printable |
| spellingShingle | Yingjing Yan Defu Zhang Multi-scale U-like network with attention mechanism for automatic pancreas segmentation. PLoS ONE |
| title | Multi-scale U-like network with attention mechanism for automatic pancreas segmentation. |
| title_full | Multi-scale U-like network with attention mechanism for automatic pancreas segmentation. |
| title_fullStr | Multi-scale U-like network with attention mechanism for automatic pancreas segmentation. |
| title_full_unstemmed | Multi-scale U-like network with attention mechanism for automatic pancreas segmentation. |
| title_short | Multi-scale U-like network with attention mechanism for automatic pancreas segmentation. |
| title_sort | multi scale u like network with attention mechanism for automatic pancreas segmentation |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0252287&type=printable |
| work_keys_str_mv | AT yingjingyan multiscaleulikenetworkwithattentionmechanismforautomaticpancreassegmentation AT defuzhang multiscaleulikenetworkwithattentionmechanismforautomaticpancreassegmentation |