Liver Tumor Computed Tomography Image Segmentation Based on an Improved U-Net Model
An automated segmentation method for computed tomography (CT) images of liver tumors is an urgent clinical need. Tumor areas within liver cancer images are easily missed as they are small and have unclear borders. To address these issues, an improved liver tumor segmentation method based on U-Net is...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/20/11283 |
| _version_ | 1797447032758075392 |
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| author | Hefu Li Binmei Liang |
| author_facet | Hefu Li Binmei Liang |
| author_sort | Hefu Li |
| collection | DOAJ |
| description | An automated segmentation method for computed tomography (CT) images of liver tumors is an urgent clinical need. Tumor areas within liver cancer images are easily missed as they are small and have unclear borders. To address these issues, an improved liver tumor segmentation method based on U-Net is proposed. This involves incorporating attention mechanisms into the U-Net’s skip connections, giving higher weights to important regions. Through dynamically adjusting the attention recognition characteristics, the method achieves accurate localization that is focused on and discriminates target regions. Testing using the LiTS (liver tumor segmentation) public dataset resulted in a Dice similarity coefficient of 0.69. The experiments demonstrated that this method can accurately segment liver tumors. |
| first_indexed | 2024-03-09T13:50:02Z |
| format | Article |
| id | doaj.art-547c253a9c8e4102b193d989cbf6176d |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T13:50:02Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-547c253a9c8e4102b193d989cbf6176d2023-11-30T20:51:54ZengMDPI AGApplied Sciences2076-34172023-10-0113201128310.3390/app132011283Liver Tumor Computed Tomography Image Segmentation Based on an Improved U-Net ModelHefu Li0Binmei Liang1College of Computer and Electronic Information, Guangxi University, Nanning 530004, ChinaCollege of Computer and Electronic Information, Guangxi University, Nanning 530004, ChinaAn automated segmentation method for computed tomography (CT) images of liver tumors is an urgent clinical need. Tumor areas within liver cancer images are easily missed as they are small and have unclear borders. To address these issues, an improved liver tumor segmentation method based on U-Net is proposed. This involves incorporating attention mechanisms into the U-Net’s skip connections, giving higher weights to important regions. Through dynamically adjusting the attention recognition characteristics, the method achieves accurate localization that is focused on and discriminates target regions. Testing using the LiTS (liver tumor segmentation) public dataset resulted in a Dice similarity coefficient of 0.69. The experiments demonstrated that this method can accurately segment liver tumors.https://www.mdpi.com/2076-3417/13/20/11283CT imagesU-Net networkattention mechanismliver tumorsdeep learning |
| spellingShingle | Hefu Li Binmei Liang Liver Tumor Computed Tomography Image Segmentation Based on an Improved U-Net Model Applied Sciences CT images U-Net network attention mechanism liver tumors deep learning |
| title | Liver Tumor Computed Tomography Image Segmentation Based on an Improved U-Net Model |
| title_full | Liver Tumor Computed Tomography Image Segmentation Based on an Improved U-Net Model |
| title_fullStr | Liver Tumor Computed Tomography Image Segmentation Based on an Improved U-Net Model |
| title_full_unstemmed | Liver Tumor Computed Tomography Image Segmentation Based on an Improved U-Net Model |
| title_short | Liver Tumor Computed Tomography Image Segmentation Based on an Improved U-Net Model |
| title_sort | liver tumor computed tomography image segmentation based on an improved u net model |
| topic | CT images U-Net network attention mechanism liver tumors deep learning |
| url | https://www.mdpi.com/2076-3417/13/20/11283 |
| work_keys_str_mv | AT hefuli livertumorcomputedtomographyimagesegmentationbasedonanimprovedunetmodel AT binmeiliang livertumorcomputedtomographyimagesegmentationbasedonanimprovedunetmodel |