RDCTrans U-Net: A Hybrid Variable Architecture for Liver CT Image Segmentation
Segmenting medical images is a necessary prerequisite for disease diagnosis and treatment planning. Among various medical image segmentation tasks, U-Net-based variants have been widely used in liver tumor segmentation tasks. In view of the highly variable shape and size of tumors, in order to impro...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/7/2452 |
| _version_ | 1797437781105967104 |
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| author | Lingyun Li Hongbing Ma |
| author_facet | Lingyun Li Hongbing Ma |
| author_sort | Lingyun Li |
| collection | DOAJ |
| description | Segmenting medical images is a necessary prerequisite for disease diagnosis and treatment planning. Among various medical image segmentation tasks, U-Net-based variants have been widely used in liver tumor segmentation tasks. In view of the highly variable shape and size of tumors, in order to improve the accuracy of segmentation, this paper proposes a U-Net-based hybrid variable structure—RDCTrans U-Net for liver tumor segmentation in computed tomography (CT) examinations. We design a backbone network dominated by ResNeXt50 and supplemented by dilated convolution to increase the network depth, expand the perceptual field, and improve the efficiency of feature extraction without increasing the parameters. At the same time, Transformer is introduced in down-sampling to increase the network’s overall perception and global understanding of the image and to improve the accuracy of liver tumor segmentation. The method proposed in this paper tests the segmentation performance of liver tumors on the LiTS (Liver Tumor Segmentation) dataset. It obtained 89.22% mIoU and 98.91% Acc, for liver and tumor segmentation. The proposed model also achieved 93.38% Dice and 89.87% Dice, respectively. Compared with the original U-Net and the U-Net model that introduces dense connection, attention mechanism, and Transformer, respectively, the method proposed in this paper achieves SOTA (state of art) results. |
| first_indexed | 2024-03-09T11:27:34Z |
| format | Article |
| id | doaj.art-710a1db9b6c64255acc2e7197b31e960 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T11:27:34Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-710a1db9b6c64255acc2e7197b31e9602023-11-30T23:59:05ZengMDPI AGSensors1424-82202022-03-01227245210.3390/s22072452RDCTrans U-Net: A Hybrid Variable Architecture for Liver CT Image SegmentationLingyun Li0Hongbing Ma1College of Information Science and Engineering, Xinjiang University, Urumqi 830046, ChinaCollege of Information Science and Engineering, Xinjiang University, Urumqi 830046, ChinaSegmenting medical images is a necessary prerequisite for disease diagnosis and treatment planning. Among various medical image segmentation tasks, U-Net-based variants have been widely used in liver tumor segmentation tasks. In view of the highly variable shape and size of tumors, in order to improve the accuracy of segmentation, this paper proposes a U-Net-based hybrid variable structure—RDCTrans U-Net for liver tumor segmentation in computed tomography (CT) examinations. We design a backbone network dominated by ResNeXt50 and supplemented by dilated convolution to increase the network depth, expand the perceptual field, and improve the efficiency of feature extraction without increasing the parameters. At the same time, Transformer is introduced in down-sampling to increase the network’s overall perception and global understanding of the image and to improve the accuracy of liver tumor segmentation. The method proposed in this paper tests the segmentation performance of liver tumors on the LiTS (Liver Tumor Segmentation) dataset. It obtained 89.22% mIoU and 98.91% Acc, for liver and tumor segmentation. The proposed model also achieved 93.38% Dice and 89.87% Dice, respectively. Compared with the original U-Net and the U-Net model that introduces dense connection, attention mechanism, and Transformer, respectively, the method proposed in this paper achieves SOTA (state of art) results.https://www.mdpi.com/1424-8220/22/7/2452liver tumor segmentationU-NetResNeXt50dilated convolutiontransformer |
| spellingShingle | Lingyun Li Hongbing Ma RDCTrans U-Net: A Hybrid Variable Architecture for Liver CT Image Segmentation Sensors liver tumor segmentation U-Net ResNeXt50 dilated convolution transformer |
| title | RDCTrans U-Net: A Hybrid Variable Architecture for Liver CT Image Segmentation |
| title_full | RDCTrans U-Net: A Hybrid Variable Architecture for Liver CT Image Segmentation |
| title_fullStr | RDCTrans U-Net: A Hybrid Variable Architecture for Liver CT Image Segmentation |
| title_full_unstemmed | RDCTrans U-Net: A Hybrid Variable Architecture for Liver CT Image Segmentation |
| title_short | RDCTrans U-Net: A Hybrid Variable Architecture for Liver CT Image Segmentation |
| title_sort | rdctrans u net a hybrid variable architecture for liver ct image segmentation |
| topic | liver tumor segmentation U-Net ResNeXt50 dilated convolution transformer |
| url | https://www.mdpi.com/1424-8220/22/7/2452 |
| work_keys_str_mv | AT lingyunli rdctransunetahybridvariablearchitectureforliverctimagesegmentation AT hongbingma rdctransunetahybridvariablearchitectureforliverctimagesegmentation |