Abstract
Tumor region segmentation in liver pathological images has become a significant tool of routine diagnosis and it also plays an important role in the liver cancer surgical plannings. Liver lesions are usually manually marked by clinicians on ultra-high resolution pathological images, which is awfully time-consuming and laborious. Aiming at this problem, we propose a new deep neural network for automatically segment liver cancer pathological images in this paper to assist doctors in completing the diagnosis efficiently. The existing U-Net framework was mainly used in previous researches, but not all features extracted by the encoder module are suitable for pathological image segmentation. In order to solve this problem, the network in this paper designs an attention-guided connection module to obtain accurate segmentation of liver cancer, so that the useful context features can be selected adaptively from the low-level features which are guided by the high-level features. The pyramid sampling operation is applied to the global attention module to fuse different levels of features. The results of experiments show that the method has some certain advantages over the previous methods in liver cancer pathological image segmentation datasets, and its IOU is increase by 5.4% and 2.2% compared with U-Net and PSPNet networks. Simultaneously, in order to testify the basic concept preponderance, some related studies about the ablation are carried out.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Jemal, A., Bray, F., Center, M.M., Ferlay, J., Ward, E., Forman, D.: Global cancer statistics. CA Cancer J. Clin. 61(2), 69–90 (2011)
Longerich, T., Schirmacher, P.: Emerging role of the pathologist in precision medicine for HCC. Dig. Dis. Sci. 64(4), 928–933 (2019)
Roa-Peña, G., Romero, E.: An experimental study of pathologist’s navigation patterns in virtual microscopy. Diagn. Pathol. 5(1), 71 (2010)
Wang, S., Yang, D.M., et al.: Pathology image analysis using segmentation deep learning algorithms. Am. J. Pathol. 189(9), 1686–1698 (2019). 8(1): 1-9, 2018
Bejnordi, B.E., et al.: Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA 318(22), 2199–2210 (2017)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Zhang, H., Dana, K., Shi, J., Zhang, Z., et al.: Context encoding for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7151–7160 (2018)
Lin, D., et al.: RefineU-Net: improved U-Net with progressive global feedbacks and residual attention guided local refinement for medical image segmentation. Pattern Recogn. Lett. 138, 267–275 (2020)
Wang, X., Girshick, R.B., Gupta, A., He, K.: Non-local neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7794–7803 (2018)
Wang, Z., et al.: Non-local U-Nets for biomedical image segmentation. In: AAAI Conference on Artificial Intelligence, vol. 34, no. 04, pp. 6315–6322 (2020)
Chen, L.-C., et al.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2017)
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 833–851. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_49
Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6230–6239 (2017)
Shelhamer, E., Long, J., Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640–651 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Ethics declarations
Conflict of Interest
The authors did not receive any funding for the current work, and there is no conflict of interest. The authors thank the participants who volunteered to participate in this survey.
Ethics Approval
The local University Ethics Committee approved the study, which was performed in accordance with the principles of the latest version of the Declaration of Helsinki.
Consent to Participate
All the participants agreed after receiving an explanation of the purpose and design of the study. Participants can freely withdraw from the study at any time.
Consent for Publication
All the participants gave their consent for data and study publication.
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhai, Z., Wang, C., Sun, Z., Cheng, S., Wang, K. (2022). Deep Neural Network Guided by Attention Mechanism for Segmentation of Liver Pathology Image. In: Jia, Y., Zhang, W., Fu, Y., Yu, Z., Zheng, S. (eds) Proceedings of 2021 Chinese Intelligent Systems Conference. Lecture Notes in Electrical Engineering, vol 805. Springer, Singapore. https://doi.org/10.1007/978-981-16-6320-8_44
Download citation
DOI: https://doi.org/10.1007/978-981-16-6320-8_44
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-6319-2
Online ISBN: 978-981-16-6320-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)