Computer Engineering, Volume. 51, Issue 8, 270(2025)
Multi-Organ Semantic Segmentation Model Based on Multi-Scale Region Feature Fusion
[1] [1] EVERINGHAM M, ESLAMI S M A, VAN GOOL L, et al. The pascal visual object classes challenge: a retrospective[J]. International Journal of Computer Vision, 2015, 111: 98-136.
[5] [5] BATENBURG K J, SIJBERS J. Optimal threshold selection for tomogram segmentation by projection distance minimization[J]. IEEE Transactions on Medical Imaging, 2009, 28(5): 676-686.
[6] [6] HARIHARAN B, ARBELEZ P, GIRSHICK R, et al. Simultaneous detection and segmentation[C]//Proceedings of the 13th European Conference on Computer Vision. Berlin, Germany: Springer, 2014: 297-312.
[7] [7] SHRIVAKSHAN G T, CHANDRASEKAR C. A comparison of various edge detection techniques used in image processing[J]. International Journal of Computer Science Issues, 2012, 9(5): 269-276.
[8] [8] LITJENS G, KOOI T, BEJNORDI B E, et al. A survey on deep learning in medical image analysis[J]. Medical Image Analysis, 2017, 42: 60-88.
[9] [9] RONNEBERGER O, FISCHER P, BROX T. U-Net: convolutional networks for biomedical image segmentation[C]//Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention. Berlin, Germany: Springer, 2015: 234-241.
[10] [10] ZHOU Z W, SIDDIQUEE M M R, NIMA T, et al. UNet++: a nested U-Net architecture for medical image segmentation[C]//Proceedings of Workshop on Deep Learning in Medical Image Analysis. Berlin, Germany: Springer, 2018: 3-11.
[11] [11] HUANG H M, LIN L F, TONG R F, et al. UNet 3+: a full-scale connected UNet for medical image segmentation[C]//Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2020). Washington D.C., USA: IEEE Press, 2020: 1055-1059.
[12] [12] HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Washington D.C., USA: IEEE Press, 2016: 770-778.
[13] [13] PENG D L, XIONG S Y, PENG W J, et al. LCP-Net: a local context-perception deep neural network for medical image segmentation[J]. Expert Systems with Applications, 2021, 168: 114234.
[14] [14] CHEN R, WANG X, JIN B, et al. CLD-Net: complement local detail for medical small-object segmentation[C]//Proceedings of 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Washington D.C., USA: IEEE Press, 2022: 942-947.
[15] [15] HUANG Q, SU J, PRZYSTUPA K, et al. BSANet: highperformance 3D medical image segmentation[J]. IEEE Access, 2023, 11: 79213-79223.
[17] [17] WANG J, ZHAO H Y, LIANG W, et al. Cross-convolutional transformer for automated multi-organs segmentation in a variety of medical images[J]. Physics in Medicine & Biology, 2023, 68(3): 035008.
[18] [18] KANG S, YANG M, QI X S, et al. Bridging feature gaps to improve multi-organ segmentation on abdominal magnetic resonance image[J]. IEEE Journal of Biomedical and Health Informatics, 2023, 27(3): 1477-1487.
[19] [19] SHEN N, WANG Z, LI J, et al. Multi-organ segmentation network for abdominal CT images based on spatial attention and deformable convolution[J]. Expert Systems with Applications, 2023, 211: 118625.
[22] [22] HU J, SHEN L, ALBANIE S, et al. Squeeze-and-excitation networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 42: 2011-2023.
[23] [23] ROY A G, NAVAB N, WACHINGER C. Concurrent spatial and channel ‘squeeze & excitation’ in fully convolutional networks[C]//Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention. Berlin, Germany: Springer, 2018: 421-429.
[24] [24] FU J, LIU J, TIAN H J, et al. Dual attention network for scene segmentation[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington D.C., USA: IEEE Press, 2019: 3146-3154.
[25] [25] CHEN J N, LU Y Y, YU Q H, et al. TransUNet: Transformers make strong encoders for medical image segmentation[EB/OL]. [2023-12-15]. https://arxiv.org/abs/2102.04306?context=cs.
[26] [26] SCHLEMPER J, OKTAY O, SCHAAP M, et al. Attention gated networks: learning to leverage salient regions in medical images[J]. Medical Image Analysis, 2019, 53: 197-207.
[27] [27] GU Z W, CHENG J, FU H Z, et al. CE-Net: context encoder network for 2D medical image segmentation[J]. IEEE Transactions on Medical Imaging, 2019, 38(10): 2281-2292.
[28] [28] SINHA A, DOLZ J. Multi-scale self-guided attention for medical image segmentation[J]. IEEE Journal of Biomedical and Health Informatics, 2021, 25(1): 121-130.
[29] [29] HUANG Z L, WANG X G, WEI Y C, et al. CCNet: criss-cross attention for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45(6): 6896-6908.
[30] [30] SONG J H, CHEN X J, ZHU Q L, et al. Global and local feature reconstruction for medical image segmentation[J]. IEEE Transactions on Medical Imaging, 2022, 41(9): 2273-2284.
[31] [31] CAO H, WANG Y Y, CHEN J, et al. Swin-Unet: Unet-like pure transformer for medical image segmentation[EB/OL]. [2023-12-15]. https://arxiv.org/abs/2105.05537?context=eess.IV.
[32] [32] WANG H Y, XIE S, LIN L F, et al. Mixed transformer U-Net for medical image segmentation[C]//Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Washington D.C., USA: IEEE Press, 2022: 2390-2394.
[33] [33] LIN G, CHEN L. A multi-scale fusion network with transformer for medical image segmentation[C]//Proceedings of the 3rd International Conference on Neural Networks, Information and Communication Engineering. Washington D.C., USA: IEEE Press, 2023: 224-228.
[34] [34] YU J, HE X, QIN J, et al. Trans-UNeter: a new decoder of Trans-UNet for medical image segmentation[C]//Proceedings of IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Washington D.C., USA: IEEE Press, 2023: 2338-2341.
[35] [35] ZHAO L, TIAN X, LIU Y. Transformer based position information enhancement for medical image segmentation[C]//Proceedings of the 4th Asia Conference on Information Engineering (ACIE). Washington D.C., USA: IEEE Press, 2024: 92-96.
Get Citation
Copy Citation Text
HAO Hongda, LUO Jianxu. Multi-Organ Semantic Segmentation Model Based on Multi-Scale Region Feature Fusion[J]. Computer Engineering, 2025, 51(8): 270
Category:
Received: Jan. 21, 2024
Accepted: Aug. 26, 2025
Published Online: Aug. 26, 2025
The Author Email: LUO Jianxu (jxluo@ecust.edu.cn)