Opto-Electronic Engineering, Volume. 52, Issue 3, 240279(2025)
Colorectal polyp segmentation via Transformer-based adaptive feature selection
Figures & Tables(13)
Fig. 1. A Transformer-based adaptive feature selection algorithm for colorectal polyp segmentation
Fig. 6. Visualization of segmentation results of different networks on CVC-ClinicDB and Kvasir datasets
Fig. 7. Visualization of sgmentation results of different networks on CVC-ColonDB and ETIS datasets
Table 1. Dataset details and division
View tableView in ArticleTable 1. Dataset details and division
Dataset Image resolution Train data Test data Image type CVC-ClinicDB 384×288 550 62 Image Kvasir-SEG Size variation 900 100 Image and mask CVC-ColonDB 574×500 0 380 Image ETIS 1226×996 0 196 Image
Table 2. Segmentation results of different networks on Kvasir and CVC-ClinicDB datasets
View tableView in ArticleTable 2. Segmentation results of different networks on Kvasir and CVC-ClinicDB datasets
Dataset Method Dice MIoU SE PC F2 MAE Kvasir U-Net[ 4 ]0.818 0.746 0.856 0.857 0.827 0.055 DCRNet[ 6 ]0.888 0.825 0.902 0.904 0.891 0.035 CaraNet[ 7 ]0.922 0.872 0.915 0.941 0.921 0.019 MSEG[ 8 ]0.899 0.842 0.900 0.923 0.896 0.028 SSFormer-S[ 9 ]0.925 0.877 0.914 0.944 0.917 0.018 MSRAFormer[ 10 ]0.923 0.873 0.915 0.952 0.917 0.024 Polyp-PVT[ 21 ]0.917 0.864 0.913 0.947 0.914 0.023 Ours 0.926 0.879 0.917 0.955 0.919 0.023 CVC-ClinicDB U-Net[ 4 ]0.823 0.755 0.834 0.839 0.827 0.019 DCRNet[ 6 ]0.899 0.846 0.913 0.893 0.906 0.010 CaraNet[ 7 ]0.934 0.890 0.944 0.940 0.939 0.006 MSEG[ 8 ]0.912 0.866 0.924 0.935 0.918 0.007 SSFormer-S[ 9 ]0.918 0.875 0.905 0.939 0.910 0.007 MSRAFormer[ 10 ]0.924 0.874 0.945 0.920 0.932 0.008 Polyp-PVT[ 21 ]0.937 0.889 0.949 0.936 0.945 0.006 Ours 0.941 0.896 0.957 0.934 0.949 0.006
Table 3. Segmentation results of different networks on CVC-ColonDB and ETIS datasets
View tableView in ArticleTable 3. Segmentation results of different networks on CVC-ColonDB and ETIS datasets
Dataset Method Dice MIoU SE PC F2 MAE CVC-ConlonDB U-Net[ 4 ]0.512 0.444 0.523 0.621 0.510 0.061 DCRNet[ 6 ]0.707 0.632 0.776 0.719 0.723 0.052 CaraNet[ 7 ]0.748 0.683 0.753 0.893 0.746 0.035 MSEG[ 8 ]0.738 0.669 0.752 0.806 0.739 0.038 SSFormer-S[ 9 ]0.774 0.698 0.777 0.837 0.766 0.036 MSRAFormer[ 10 ]0.782 0.707 0.803 0.874 0.181 0.028 Polyp-PVT[ 21 ]0.808 0.727 0.821 0.849 0.809 0.031 Ours 0.814 0.732 0.849 0.824 0.825 0.028 ETIS U-Net[ 4 ]0.398 0.335 0.482 0.439 0.429 0.036 DCRNet[ 6 ]0.550 0.486 0.746 0.504 0.600 0.095 CaraNet[ 7 ]0.728 0.661 0.775 0.814 0.750 0.017 MSEG[ 8 ]0.703 0.632 0.739 0.710 0.720 0.015 SSFormer-S[ 9 ]0.769 0.698 0.856 0.743 0.800 0.016 MSRAFormer[ 10 ]0.750 0.679 0.811 0.745 0.777 0.013 Polyp-PVT[ 21 ]0.787 0.706 0.867 0.774 0.820 0.013 Ours 0.797 0.716 0.889 0.761 0.834 0.018
Table 4. Performance comparison of different networks (CVC-ClinicDB)
View tableView in ArticleTable 4. Performance comparison of different networks (CVC-ClinicDB)
Method Parameters/M GFLOPs Train/( ) U-Net 34.53 65.52 309 DCRNet 28.70 53.00 285 CaraNet 44.54 11.45 256 SSFormer-S 29.31 10.11 220 MSRAFormer 68.96 21.29 199 Polyp-PVT 25.12 5.30 233 Ours 26.05 11.00 183
Table 5. Ablation results of each module on the CVC-ClinicDB dataset
View tableView in ArticleTable 5. Ablation results of each module on the CVC-ClinicDB dataset
Method DFA HFF DFS Dice MIoU SE PC F2 G1 √ √ 0.929 0.882 0.935 0.941 0.931 G2 √ √ 0.930 0.884 0.950 0.924 0.938 G3 √ √ 0.921 0.866 0.922 0.935 0.921 G4 √ √ √ 0.941 0.896 0.957 0.934 0.949
Table 6. Ablation results of each module on the ETIS dataset
View tableView in ArticleTable 6. Ablation results of each module on the ETIS dataset
Method DFA HFF DFS Dice MIoU SE PC F2 G1 √ √ 0.782 0.705 0.856 0.748 0.816 G2 √ √ 0.787 0.705 0.875 0.755 0.825 G3 √ √ 0.780 0.698 0.839 0.771 0.806 G4 √ √ √ 0.797 0.716 0.889 0.761 0.834
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Liming Liang, Ting Kang, Chengbin Wang, Kangquan Chen, Yulin Li. Colorectal polyp segmentation via Transformer-based adaptive feature selection[J]. Opto-Electronic Engineering, 2025, 52(3): 240279
Paper Information
Category: Article
Received: Nov. 29, 2024
Accepted: Feb. 6, 2025
Published Online: May. 22, 2025
The Author Email: Ting Kang (康婷)
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