Treffer: MF-ResUnet: A 3D Liver Image Segmentation Method Based on Multi-Scale Feature Fusion.
Title:
MF-ResUnet: A 3D Liver Image Segmentation Method Based on Multi-Scale Feature Fusion.
Authors:
Qin J; College of Computer Science and Technology, Changchun University of Science and Technology, Changchun, China., Li Y; College of Computer Science and Technology, Changchun University of Science and Technology, Changchun, China., Qin G; College of Computer Science and Technology, Jilin University, Changchun, China.
Source:
The international journal of medical robotics + computer assisted surgery : MRCAS [Int J Med Robot] 2025 Jun; Vol. 21 (3), pp. e70068.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Wiley Country of Publication: England NLM ID: 101250764 Publication Model: Print Cited Medium: Internet ISSN: 1478-596X (Electronic) Linking ISSN: 14785951 NLM ISO Abbreviation: Int J Med Robot Subsets: MEDLINE
Imprint Name(s):
Publication: 2006- : West Sussex, England : Wiley
Original Publication: Ilkley, UK : Robotic Publications, c2004-
Original Publication: Ilkley, UK : Robotic Publications, c2004-
MeSH Terms:
References:
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M. Moghbel, S. Mashohor, R. Mahmud, and M. I. B. Saripan, “Review of Liver Segmentation and Computer Assisted Detection/Diagnosis Methods in Computed Tomography,” Artificial Intelligence Review 50, no. 4 (2018): 497–537, https://doi.org/10.1007/s10462‐017‐9550‐x.
H. Masoumi, A. Behrad, M. A. Pourmina, and A. Roosta, “Automatic Liver Segmentation in MRI Images Using an Iterative Watershed Algorithm and Artificial Neural Network,” Biomedical Signal Processing and Control 7, no. 5 (2012): 429–437, https://doi.org/10.1016/j.bspc.2012.01.002.
M. Liao, Yq Zhao, Xy Liu, et al., “Automatic Liver Segmentation From Abdominal CT Volumes Using Graph Cuts and Border Marching,” Computer Methods and Programs in Biomedicine 143 (2017): 1–12, https://doi.org/10.1016/j.cmpb.2017.02.015.
X. Li, H. Chen, X. Qi, Q. Dou, C. W. Fu, and P. A. Heng, “H‐DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes,” IEEE Transactions on Medical Imaging 37, no. 12 (2018): 2663–2674, https://doi.org/10.1109/tmi.2018.2845918.
J. Wu, Z. Cui, F. Ye, et al., “Interpolation Based on Contour Shape for CT Faulted Images,” Jisuanji Yingyong yu Ruanjian 25, no. 11 (2008): 63–65.
X. Lu, J. Wu, X. Ren, B. Zhang, and Y. Li, “The Study and Application of the Improved Region Growing Algorithm for Liver Segmentation,” Optik 125, no. 9 (2014): 2142–2147, https://doi.org/10.1016/j.ijleo.2013.10.049.
Y. Zhao, G. Yan, X. Xu, et al., “Automatic Segmentation of Livers From CT Series Based on Level Set Method With Prior Knowledge,” Journal of Central South University 46, no. 4 (2015): 1310–1317.
E. Vorontsov, N. Abi‐Jaoudeh, and S. Kadoury, “Metastatic Liver Tumor Segmentation Using Texture‐Based Omni‐Directional Deformable Surface Models,” in Abdominal Imaging. Computational and Clinical Applications: 6th International Workshop, ABDI 2014, Held in Conjunction With MICCAI 2014, Cambridge, MA, USA, September 14, 2014. 6 (Springer International Publishing, 2014), 74–83.
R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Süsstrunk, “SLIC Superpixels Compared to State‐of‐the‐Art Superpixel Methods,” IEEE Transactions on Pattern Analysis and Machine Intelligence 34, no. 11 (2012): 2274–2282, https://doi.org/10.1109/tpami.2012.120.
Y. Li, S. Hara, and K. Shimura, “A Machine Learning Approach for Locating Boundaries of Liver Tumors in CT Images,” in 18th International Conference on Pattern Recognition (ICPR'06), Vol. 1 (IEEE, 2006), 400–403.
M. Fu, P. Xu, X. D. Li, Q. Liu, M. Ye, and C. Zhu, “Fast Crowd Density Estimation With Convolutional Neural Networks,” Engineering Applications of Artificial Intelligence 43 (2015): 81–88, https://doi.org/10.1016/j.engappai.2015.04.006.
Yi Shen, V. S. Sheng, L. Wang, et al., “Empirical Comparisons of Deep Learning Networks on Liver Segmentation,” Computers, Materials & Continua 62, no. 3 (2020): 1233–1247, https://doi.org/10.32604/cmc.2020.07450.
J. Long, E. Shelhamer, and T. Darrell, “Fully Convolutional Networks for Semantic Segmentation,” in Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2015), 3431–3440.
O. Ronneberger, P. Fischer, and T. Brox, “U‐Net: Convolutional Networks for Biomedical Image Segmentation,” in Medical Image Computing and Computer‐Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5–9, 2015, Proceedings, Part III 18 (Springer International Publishing, 2015), 234–241.
Z. W. Zhou, M. M. Rahman Siddiquee, N. Tajbakhsh, et al., “UNet++: A Nested U‐Net Architecture for Medical Image Segmentation,” in Proceedings of the 2018 International Workshop on Deep Learning in Medical Image Analysis/International Workshop on Multimodal Learning for Clinical Decision Support, LNCS 11045 (Springer, 2018), 3–11.
R. A. Khan, Y. Luo, and F. X. Wu, “RMS‐UNet: Residual Multi‐Scale UNet for Liver and Lesion Segmentation,” Artificial Intelligence in Medicine 124 (2022): 102231, https://doi.org/10.1016/j.artmed.2021.102231.
S. Feng, H. Zhao, F. Shi, et al., “CPFNet: Context Pyramid Fusion Network for Medical Image Segmentation,” IEEE Transactions on Medical Imaging 39, no. 10 (2020): 3008–3018, https://doi.org/10.1109/tmi.2020.2983721.
Z. Liu, L. Zheng, S. Yang, Z. Zhong, and G. Zhang, “MFF‐Net: Multiscale Feature Fusion Semantic Segmentation Network for Intracranial Surgical Instruments,” International Journal of Medical Robotics and Computer Assisted Surgery 20, no. 1 (2024): e2595, https://doi.org/10.1002/rcs.2595.
A. Vaswani, “Attention Is All You Need,” Advances in Neural Information Processing Systems 30 (2017).
K. Han, Y. Wang, H. Chen, et al., “A Survey on Vision Transformer,” IEEE Transactions on Pattern Analysis and Machine Intelligence 45, no. 1 (2022): 87–110, https://doi.org/10.1109/tpami.2022.3152247.
J. Chen, Y. Lu, Q. Yu, et al., “Transunet: Transformers Make Strong Encoders for Medical Image Segmentation,” arxiv preprint arxiv:2102.04306 (2021).
Z. Liu, Y. Lin, Y. Cao, et al., “Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (IEEE, 2021).
A. Hatamizadeh, Y. Tang, V. Nath, et al., “UNETR: Transformers for 3D Medical Image Segmentation,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (2022).
A. M. Shaker, M. Maaz, H. Rasheed, S. Khan, M. H. Yang, and F. Shahbaz Khan, “UNETR++: Delving Into Efficient and Accurate 3D Medical Image Segmentation,” IEEE Transactions on Medical Imaging 43, no. 9 (2024): 3377–3390, https://doi.org/10.1109/tmi.2024.3398728.
J. Hu, L. Shen, and G. Sun, “Squeeze‐and‐Excitation Networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018), 7132–7141.
J. Ruan, M. Xie, J. Gao, T. Liu, and Y. Fu, “EGE‐UNet: An Efficient Group Enhanced UNet for Skin Lesion Segmentation,” in Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, Vol. 14223, ed. H. Greenspan, et al. (Springer, 2023), 481–490, https://doi.org/10.1007/978‐3‐031‐43901‐8_46.
L. C. Chen, G. Papandreou, F. Schroff, et al., “Rethinking Atrous Convolution for Semantic Image Segmentation,” ar**v preprint ar**v:1706.05587 (2017).
Y. Zhou and J. Zong, “Automatic Liver Segmentation Method From CT Images Based on Improved 3D U‐Net,” in 2022 IEEE 10th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), Vol. 10 (IEEE, 2022), 250–258, https://doi.org/10.1109/itaic54216.2022.9836869.
Codelab, “LiTS Database: Liver Tumor Segmentation Challenge,” (2017), 9, accessed June 2020, http://www.lits‐challenge.com/.
Ö Çiçek, A. Abdulkadir, S. S. Lienkamp, et al., “3D U‐Net: Learning Dense Volumetric Segmentation From Sparse Annotation,” in Medical Image Computing and Computer‐Assisted Intervention–MICCAI 2016: 19th International Conference, Athens, Greece, October 17–21, 2016, Proceedings, Part II 19 (Springer International Publishing, 2016), 424–432.
F. Isensee, P. F. Jaeger, S. A. A. Kohl, J. Petersen, and K. H. Maier‐Hein, “nnU‐Net: A Self‐Configuring Method for Deep Learning‐Based Biomedical Image Segmentation,” Nature Methods 18, no. 2 (2021): 203–211, https://doi.org/10.1038/s41592‐020‐01008‐z.
H. Liu, Y. Fu, S. Zhang, et al., “GCHA‐Net: Global Context and Hybrid Attention Network for Automatic Liver Segmentation,” Computers in Biology and Medicine 152 (2023): 106352, https://doi.org/10.1016/j.compbiomed.2022.106352.
A. Hatamizadeh, V. Nath, Y. Tang, D. Yang, H. R. Roth, and D. Xu, “Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images,” in International MICCAI Brainlesion Workshop (Springer International Publishing, 2021).
Z. Xing, T. Ye, Y. Yang, G. Liu, and L. Zhu, “Segmamba: Long‐Range Sequential Modeling Mamba for 3D Medical Image Segmentation,” Lecture Notes in Computer Science (2024): 578–588, arXiv preprint arXiv:2401.13560, https://doi.org/10.1007/978‐3‐031‐72111‐3_54.
J. C. Ozougwu, “Physiology of the Liver,” International Journal of Research in Pharmacy and Biosciences 4, no. 8 (2017): 13–24.
X. Xu, Q. Lu, L. Yang, et al., “Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018), 8300–8308.
S. Zheng, B. Fang, L. Li, M. Gao, and Y. Wang, “A Variational Approach to Liver Segmentation Using Statistics From Multiple Sources,” Physics in Medicine and Biology 63, no. 2 (2018): 025024, https://doi.org/10.1088/1361‐6560/aaa360.
M. Moghbel, S. Mashohor, R. Mahmud, and M. I. B. Saripan, “Review of Liver Segmentation and Computer Assisted Detection/Diagnosis Methods in Computed Tomography,” Artificial Intelligence Review 50, no. 4 (2018): 497–537, https://doi.org/10.1007/s10462‐017‐9550‐x.
H. Masoumi, A. Behrad, M. A. Pourmina, and A. Roosta, “Automatic Liver Segmentation in MRI Images Using an Iterative Watershed Algorithm and Artificial Neural Network,” Biomedical Signal Processing and Control 7, no. 5 (2012): 429–437, https://doi.org/10.1016/j.bspc.2012.01.002.
M. Liao, Yq Zhao, Xy Liu, et al., “Automatic Liver Segmentation From Abdominal CT Volumes Using Graph Cuts and Border Marching,” Computer Methods and Programs in Biomedicine 143 (2017): 1–12, https://doi.org/10.1016/j.cmpb.2017.02.015.
X. Li, H. Chen, X. Qi, Q. Dou, C. W. Fu, and P. A. Heng, “H‐DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes,” IEEE Transactions on Medical Imaging 37, no. 12 (2018): 2663–2674, https://doi.org/10.1109/tmi.2018.2845918.
J. Wu, Z. Cui, F. Ye, et al., “Interpolation Based on Contour Shape for CT Faulted Images,” Jisuanji Yingyong yu Ruanjian 25, no. 11 (2008): 63–65.
X. Lu, J. Wu, X. Ren, B. Zhang, and Y. Li, “The Study and Application of the Improved Region Growing Algorithm for Liver Segmentation,” Optik 125, no. 9 (2014): 2142–2147, https://doi.org/10.1016/j.ijleo.2013.10.049.
Y. Zhao, G. Yan, X. Xu, et al., “Automatic Segmentation of Livers From CT Series Based on Level Set Method With Prior Knowledge,” Journal of Central South University 46, no. 4 (2015): 1310–1317.
E. Vorontsov, N. Abi‐Jaoudeh, and S. Kadoury, “Metastatic Liver Tumor Segmentation Using Texture‐Based Omni‐Directional Deformable Surface Models,” in Abdominal Imaging. Computational and Clinical Applications: 6th International Workshop, ABDI 2014, Held in Conjunction With MICCAI 2014, Cambridge, MA, USA, September 14, 2014. 6 (Springer International Publishing, 2014), 74–83.
R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Süsstrunk, “SLIC Superpixels Compared to State‐of‐the‐Art Superpixel Methods,” IEEE Transactions on Pattern Analysis and Machine Intelligence 34, no. 11 (2012): 2274–2282, https://doi.org/10.1109/tpami.2012.120.
Y. Li, S. Hara, and K. Shimura, “A Machine Learning Approach for Locating Boundaries of Liver Tumors in CT Images,” in 18th International Conference on Pattern Recognition (ICPR'06), Vol. 1 (IEEE, 2006), 400–403.
M. Fu, P. Xu, X. D. Li, Q. Liu, M. Ye, and C. Zhu, “Fast Crowd Density Estimation With Convolutional Neural Networks,” Engineering Applications of Artificial Intelligence 43 (2015): 81–88, https://doi.org/10.1016/j.engappai.2015.04.006.
Yi Shen, V. S. Sheng, L. Wang, et al., “Empirical Comparisons of Deep Learning Networks on Liver Segmentation,” Computers, Materials & Continua 62, no. 3 (2020): 1233–1247, https://doi.org/10.32604/cmc.2020.07450.
J. Long, E. Shelhamer, and T. Darrell, “Fully Convolutional Networks for Semantic Segmentation,” in Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2015), 3431–3440.
O. Ronneberger, P. Fischer, and T. Brox, “U‐Net: Convolutional Networks for Biomedical Image Segmentation,” in Medical Image Computing and Computer‐Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5–9, 2015, Proceedings, Part III 18 (Springer International Publishing, 2015), 234–241.
Z. W. Zhou, M. M. Rahman Siddiquee, N. Tajbakhsh, et al., “UNet++: A Nested U‐Net Architecture for Medical Image Segmentation,” in Proceedings of the 2018 International Workshop on Deep Learning in Medical Image Analysis/International Workshop on Multimodal Learning for Clinical Decision Support, LNCS 11045 (Springer, 2018), 3–11.
R. A. Khan, Y. Luo, and F. X. Wu, “RMS‐UNet: Residual Multi‐Scale UNet for Liver and Lesion Segmentation,” Artificial Intelligence in Medicine 124 (2022): 102231, https://doi.org/10.1016/j.artmed.2021.102231.
S. Feng, H. Zhao, F. Shi, et al., “CPFNet: Context Pyramid Fusion Network for Medical Image Segmentation,” IEEE Transactions on Medical Imaging 39, no. 10 (2020): 3008–3018, https://doi.org/10.1109/tmi.2020.2983721.
Z. Liu, L. Zheng, S. Yang, Z. Zhong, and G. Zhang, “MFF‐Net: Multiscale Feature Fusion Semantic Segmentation Network for Intracranial Surgical Instruments,” International Journal of Medical Robotics and Computer Assisted Surgery 20, no. 1 (2024): e2595, https://doi.org/10.1002/rcs.2595.
A. Vaswani, “Attention Is All You Need,” Advances in Neural Information Processing Systems 30 (2017).
K. Han, Y. Wang, H. Chen, et al., “A Survey on Vision Transformer,” IEEE Transactions on Pattern Analysis and Machine Intelligence 45, no. 1 (2022): 87–110, https://doi.org/10.1109/tpami.2022.3152247.
J. Chen, Y. Lu, Q. Yu, et al., “Transunet: Transformers Make Strong Encoders for Medical Image Segmentation,” arxiv preprint arxiv:2102.04306 (2021).
Z. Liu, Y. Lin, Y. Cao, et al., “Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (IEEE, 2021).
A. Hatamizadeh, Y. Tang, V. Nath, et al., “UNETR: Transformers for 3D Medical Image Segmentation,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (2022).
A. M. Shaker, M. Maaz, H. Rasheed, S. Khan, M. H. Yang, and F. Shahbaz Khan, “UNETR++: Delving Into Efficient and Accurate 3D Medical Image Segmentation,” IEEE Transactions on Medical Imaging 43, no. 9 (2024): 3377–3390, https://doi.org/10.1109/tmi.2024.3398728.
J. Hu, L. Shen, and G. Sun, “Squeeze‐and‐Excitation Networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018), 7132–7141.
J. Ruan, M. Xie, J. Gao, T. Liu, and Y. Fu, “EGE‐UNet: An Efficient Group Enhanced UNet for Skin Lesion Segmentation,” in Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, Vol. 14223, ed. H. Greenspan, et al. (Springer, 2023), 481–490, https://doi.org/10.1007/978‐3‐031‐43901‐8_46.
L. C. Chen, G. Papandreou, F. Schroff, et al., “Rethinking Atrous Convolution for Semantic Image Segmentation,” ar**v preprint ar**v:1706.05587 (2017).
Y. Zhou and J. Zong, “Automatic Liver Segmentation Method From CT Images Based on Improved 3D U‐Net,” in 2022 IEEE 10th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), Vol. 10 (IEEE, 2022), 250–258, https://doi.org/10.1109/itaic54216.2022.9836869.
Codelab, “LiTS Database: Liver Tumor Segmentation Challenge,” (2017), 9, accessed June 2020, http://www.lits‐challenge.com/.
Ö Çiçek, A. Abdulkadir, S. S. Lienkamp, et al., “3D U‐Net: Learning Dense Volumetric Segmentation From Sparse Annotation,” in Medical Image Computing and Computer‐Assisted Intervention–MICCAI 2016: 19th International Conference, Athens, Greece, October 17–21, 2016, Proceedings, Part II 19 (Springer International Publishing, 2016), 424–432.
F. Isensee, P. F. Jaeger, S. A. A. Kohl, J. Petersen, and K. H. Maier‐Hein, “nnU‐Net: A Self‐Configuring Method for Deep Learning‐Based Biomedical Image Segmentation,” Nature Methods 18, no. 2 (2021): 203–211, https://doi.org/10.1038/s41592‐020‐01008‐z.
H. Liu, Y. Fu, S. Zhang, et al., “GCHA‐Net: Global Context and Hybrid Attention Network for Automatic Liver Segmentation,” Computers in Biology and Medicine 152 (2023): 106352, https://doi.org/10.1016/j.compbiomed.2022.106352.
A. Hatamizadeh, V. Nath, Y. Tang, D. Yang, H. R. Roth, and D. Xu, “Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images,” in International MICCAI Brainlesion Workshop (Springer International Publishing, 2021).
Z. Xing, T. Ye, Y. Yang, G. Liu, and L. Zhu, “Segmamba: Long‐Range Sequential Modeling Mamba for 3D Medical Image Segmentation,” Lecture Notes in Computer Science (2024): 578–588, arXiv preprint arXiv:2401.13560, https://doi.org/10.1007/978‐3‐031‐72111‐3_54.
Grant Information:
20210101167JC Natural Science Foundation of Jilin Province of China
Contributed Indexing:
Keywords: CT volumes; attention mechanism; liver segmentation; multi‐scale feature fusion
Entry Date(s):
Date Created: 20250521 Date Completed: 20250521 Latest Revision: 20250521
Update Code:
20250521
DOI:
10.1002/rcs.70068
PMID:
40397360
Database:
MEDLINE
Weitere Informationen
Background: Due to the variable shapes of the liver parenchyma, minimal voxel intensity differences with adjacent organs, and discontinuous liver boundaries, automatic liver segmentation from computerised tomography images poses significant challenges.
Methods: In this study, we propose a 3D liver segmentation method based on multiscale feature fusion. This network employs SE channel attention to recalibrate liver features. Additionally, it utilises an AMF module for multiscale feature fusion to obtain rich spatial information. Furthermore, we introduce the NGAB module to address the deteriorating effects of dilated convolutions as the dilation rate increases, contributing to enhanced feature representation and improving accuracy in liver segmentation.
Results: Experimental results on the publicly available LiTS2017 dataset and 3DIRCADb dataset show that our proposed framework achieves a DSC of 0.977 and 0.967 in liver segmentation, respectively.
Conclusions: The proposed method can adequately capture multiscale characteristics, showing promising prospects for automatic liver segmentation.
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