DFU-Net：基于多尺度特征融合的肝脏肿瘤分割网络
DFU-Net: A Liver Tumor Segmentation Network Based on Multi-Scale Feature Fusion
DOI: 10.12677/jisp.2025.141006, PDF,   
作者: 佘海州, 高 凝：沈阳航空航天大学电子信息工程学院，辽宁 沈阳；李 鹤*：沈阳工学院信息与控制学院，辽宁 抚顺
关键词: CT图像分割；3D U-Net；多尺度特征融合；密集连接；CT Image Segmentation； 3D U-Net； Multi-Scale Feature Fusion； Dense Connection
摘要: 肝脏以及肝脏肿瘤的有效分割是肝部疾病在临床诊断的关键步骤。文章针对肝脏结构复杂、肝脏与相邻器官像素强度差异小、肝脏边界模糊等特点，提出了一种可以进行多尺度特征融合的肝脏肿瘤分割网络。该方法根据肝脏CT图像特点，在3D U-Net的基础上进行改进，提升了网络提取特征的感受野，减少了传递过程中信息的丢失。同时，在网络中引入密集融合模块，该模块可对不同尺度下的特征图进行特征融合，通过边缘信息和差异信息的融合来提升网络信息提取的性能，避免传递过程中肿瘤部分等小目标特征的丢失。在LiTS17数据集上的实验结果表明，该模型对肝脏分割的Dice系数达到了0.9504，对肿瘤分割的Dice系数达到了0.7046，实验结果证明了该方法的出色分割性能和有效性。
Abstract: Effective segmentation of the liver and liver tumors is a key step in the clinical diagnosis of liver diseases. This paper addresses the complexity of liver structure, the small difference in pixel intensity between the liver and adjacent organs, and the vagueness of liver boundaries, proposing a liver tumor segmentation network capable of multi-scale feature fusion. Based on the characteristics of liver CT images, this method improves upon the 3D U-Net, enhancing the network’s receptive field for feature extraction and reducing information loss during transmission. At the same time, a dense fusion module is introduced into the network, which can fuse feature maps at different scales, enhancing the network’s performance in information extraction through the integration of edge and difference information and preventing the loss of small target features such as tumor parts during transmission. Experimental results on the LiTS17 dataset show that the model achieved a Dice coefficient of 0.9504 for liver segmentation and 0.7046 for tumor segmentation, demonstrating the excellent segmentation performance and effectiveness of this method.
文章引用：佘海州, 高凝, 李鹤. DFU-Net：基于多尺度特征融合的肝脏肿瘤分割网络[J]. 图像与信号处理, 2025, 14(1): 62-73. https://doi.org/10.12677/jisp.2025.141006

参考文献

[1] Gotra, A., Sivakumaran, L., Chartrand, G., Vu, K., Vandenbroucke-Menu, F., Kauffmann, C., et al. (2017) Liver Segmentation: Indications, Techniques and Future Directions. Insights into Imaging, 8, 377-392. [Google Scholar] [CrossRef] [PubMed]
[2] Esteva, A., Chou, K., Yeung, S., Naik, N., Madani, A., Mottaghi, A., et al. (2021) Deep Learning-Enabled Medical Computer Vision. NPJ Digital Medicine, 4, Article No. 5. [Google Scholar] [CrossRef] [PubMed]
[3] Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S. and Lew, M.S. (2016) Deep Learning for Visual Understanding: A Review. Neurocomputing, 187, 27-48. [Google Scholar] [CrossRef] 
[4] Ronneberger, O., Fischer, P. and Brox, T. (2015) U-Net: Convolutional Networks for Biomedical Image Segmentation. In: Navab, N., Hornegger, J., Wells, W. and Frangi, A., Eds., Medical Image Computing and Computer-Assisted Intervention—MICCAI 2015, Springer, 234-241. [Google Scholar] [CrossRef] 
[5] Ker, J., Wang, L., Rao, J. and Lim, T. (2018) Deep Learning Applications in Medical Image Analysis. IEEE Access, 6, 9375-9389. [Google Scholar] [CrossRef] 
[6] Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N. and Liang, J. (2018) UNet++: A Nested U-Net Architecture for Medical Image Segmentation. In: Stoyanov, D., et al., Eds., Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Springer, 3-11. [Google Scholar] [CrossRef] [PubMed]
[7] Huang, H., Lin, L., Tong, R., Hu, H., Zhang, Q., Iwamoto, Y., et al. (2020) UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation. ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, 4-8 May 2020, 1055-1059. [Google Scholar] [CrossRef] 
[8] Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T. and Ronneberger, O. (2016) 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M., Unal, G. and Wells, W., Eds., Medical Image Computing and Computer-Assisted Intervention—MICCAI 2016, Springer, 424-432. [Google Scholar] [CrossRef] 
[9] Milletari, F., Navab, N. and Ahmadi, S. (2016) V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. 2016 Fourth International Conference on 3D Vision (3DV), Stanford, 25-28 October 2016, 565-571. [Google Scholar] [CrossRef] 
[10] Xiao, X., Lian, S., Luo, Z. and Li, S. (2018) Weighted Res-UNet for High-Quality Retina Vessel Segmentation. 2018 9th International Conference on Information Technology in Medicine and Education (ITME), Hangzhou, 19-21 October 2018, 327-331. [Google Scholar] [CrossRef] 
[11] Li, X., Chen, H., Qi, X., Dou, Q., Fu, C. and Heng, P. (2018) H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes. IEEE Transactions on Medical Imaging, 37, 2663-2674. [Google Scholar] [CrossRef] [PubMed]
[12] Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L. and Zhou, Y. (2021) TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation. arXiv: 2102.04306.
[13] Sahiner, B., Pezeshk, A., Hadjiiski, L.M., Wang, X., Drukker, K., Cha, K.H., et al. (2018) Deep Learning in Medical Imaging and Radiation Therapy. Medical Physics, 46, e1-e36. [Google Scholar] [CrossRef] [PubMed]
[14] Schlemper, J., Oktay, O., Schaap, M., Heinrich, M., Kainz, B., Glocker, B., et al. (2019) Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images. Medical Image Analysis, 53, 197-207. [Google Scholar] [CrossRef] [PubMed]
[15] Valanarasu, J.M.J., Sindagi, V.A., Hacihaliloglu, I. and Patel, V.M. (2022) KiU-Net: Overcomplete Convolutional Architectures for Biomedical Image and Volumetric Segmentation. IEEE Transactions on Medical Imaging, 41, 965-976. [Google Scholar] [CrossRef] [PubMed]
[16] Valanarasu, J.M.J., Sindagi, V.A., Hacihaliloglu, I. and Patel, V.M. (2020) KiU-Net: Towards Accurate Segmentation of Biomedical Images Using Over-Complete Representations. In: Martel, A.L., et al., Eds., Medical Image Computing and Computer Assisted Intervention—MICCAI 2020, Springer, 363-373. [Google Scholar] [CrossRef] 
[17] Kushnure, D.T., Tyagi, S. and Talbar, S.N. (2023) LiM-Net: Lightweight Multi-Level Multiscale Network with Deep Residual Learning for Automatic Liver Segmentation in CT Images. Biomedical Signal Processing and Control, 80, Article ID: 104305. [Google Scholar] [CrossRef] 
[18] Szegedy, C., Liu, W., Jia, Y.Q., Sermanet, P., Reed, S., Anguelov, D., et al. (2015) Going Deeper with Convolutions. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, 7-12 June 2015, 1-9. [Google Scholar] [CrossRef] 
[19] Huang, G., Liu, Z., Van Der Maaten, L. and Weinberger, K.Q. (2017) Densely Connected Convolutional Networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, 21-26 July 2017, 2261-2269. [Google Scholar] [CrossRef] 
[20] Lee, C., Xie, S., Gallagher, P., Zhang, Z. and Tu, Z. (2015) Deeply-Supervised Nets. Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, San Diego, 9-12 May 2015, 562-570.
[21] Yu, F. and Koltun, V. (2016) Multi-Scale Context Aggregation by Dilated Convolutions. Proceedings of the International Conference on Learning Representations (ICLR), 25-29 April 2022.
[22] Chen, P., Zhang, B., Hong, D., Chen, Z., Yang, X. and Li, B. (2022) FCCDN: Feature Constraint Network for VHR Image Change Detection. ISPRS Journal of Photogrammetry and Remote Sensing, 187, 101-119. [Google Scholar] [CrossRef] 
[23] Antonelli, M., Reinke, A., Bakas, S., Farahani, K., Kopp-Schneider, A., Landman, B.A., et al. (2022) The Medical Segmentation Decathlon. Nature Communications, 13, Article No. 4128. [Google Scholar] [CrossRef] [PubMed]
[24] Salehi, S.S.M., Erdogmus, D. and Gholipour, A. (2017) Tversky Loss Function for Image Segmentation Using 3D Fully Convolutional Deep Networks. In: Wang, Q., Shi, Y., Suk, H.I. and Suzuki, K., Eds., Machine Learning in Medical Imaging, Springer, 379-387. [Google Scholar] [CrossRef] 
[25] Bilic, P., et al. (2004) The Liver Tumor Segmentation Benchmark (LiTS). arXiv: 1901.04056.