Skip to main content

Advertisement

SpringerLink
Log in

A high-level feature channel attention UNet network for cholangiocarcinoma segmentation from microscopy hyperspectral images

  • Original Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

Pathological diagnosis is the gold standard for the diagnosis of cholangiocarcinoma. The manual segmentation of pathology sections is time-consuming. Automatic segmentation has become a clinical requirement. Recently, the UNet network has been widely used in automatic segmentation; however, due to the complex structure and diverse shapes of pathological slices of cholangiocarcinoma, the segmentation ability of UNet is insufficient. In addition, traditional RGB images cannot reflect the spectral characteristics of cancerous tissue. Therefore, in this paper, a high-level feature channel attention UNet (HLCA-UNet) network is proposed to segment cholangiocarcinoma using microscopy hyperspectral images. Compared with the original UNet, HLCA-UNet has the following improvements: (1) a new path consisting of hierarchical feature extraction and channel attention mechanism designed to deliver encoder features to decoder, in which the hierarchical feature extraction module is able to extract high-resolution high-level features and reduce semantic gaps while channel attention mechanism establishes a deep correlation between the low-level and high-level features, (2) we introduce bilinear interpolation technology to replace the transpose convolution so as to achieve a smooth segmentation boundary. We have evaluated the performance of the proposed HLCA-UNet model and compared it with other excellent models using cholangiocarcinoma microscopy hyperspectral images. Experiment results show that HLCA-UNet gains a superior performance, with accuracy, precision, and recall of 82.84%, 69.60%, and 77.99%, respectively. In general, our study provides new ideas for future pathological diagnosis of cholangiocarcinoma and makes recommendations for future researchers.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Khan, A.S., Dageforde, L.A.: Cholangiocarcinoma. Surg. Clin. 99(2), 315–335 (2019)

    Google Scholar 

  2. Sung, H., Ferlay, J., Siegel, R.L., Laversanne, M., Soerjomataram, I., Jemal, A., Bray, F.: Global cancer statistics 2020: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: Cancer J. Clin. 71(3), 209–249 (2021)

  3. Doherty, B., Nambudiri, V.E., Palmer, W.C.: Update on the diagnosis and treatment of cholangiocarcinoma. Curr. Gastroenterol. Rep. 19(1), 1–8 (2017)

  4. Guo, H.-L., Zheng, X., Cheng, M.-Q., Zeng, D., Huang, H., Xie, X.-Y., Lu, M.-D., Kuang, M., Wang, W., Xian, M.-F.: Contrast-enhanced ultrasound for differentiation between poorly differentiated hepatocellular carcinoma and intrahepatic cholangiocarcinoma. J. Ultrasound Med. 41(5), 1213–1225 (2022)

    Article  Google Scholar 

  5. XianJun, Y., NianAn, H., Mei, W.: Value of conventional ultrasonography and contrast-enhanced ultrasound in the differential diagnosis of intrahepatic cholangiocellular carcinoma and hepatocellular carcinoma: An analysis based on the logistic regression model. J. Clin. Hepatol. 35(11), 2502–2507 (2019)

    Google Scholar 

  6. Intuyod, K., Armartmuntree, N., Jusakul, A., Sakonsinsiri, C., Thanan, R., Pinlaor, S.: Current omics-based biomarkers for cholangiocarcinoma. Expert Rev. Mol. Diagn. 19(11), 997–1005 (2019)

    Article  Google Scholar 

  7. Li, X., Li, C., Rahaman, M.M., Sun, H., Li, X., Wu, J., Yao, Y., Grzegorzek, M.: A comprehensive review of computer-aided whole-slide image analysis: from datasets to feature extraction, segmentation, classification and detection approaches. Artif. Intell. Rev. 55(6), 4809–4878 (2022)

    Article  Google Scholar 

  8. Chen, H., Li, C., Wang, G., Li, X., Rahaman, M.M., Sun, H., Hu, W., Li, Y., Liu, W., Sun, C.: Gashis-transformer: a multi-scale visual transformer approach for gastric histopathological image detection. Pattern Recogn. 130, 108827 (2022)

    Article  Google Scholar 

  9. Liu, Z., Jin, L., Chen, J., Fang, Q., Ablameyko, S., Yin, Z., Xu, Y.: A survey on applications of deep learning in microscopy image analysis. Comput. Biol. Med. 134, 104523 (2021)

    Article  Google Scholar 

  10. Liu, Z., Wang, H., Li, Q.: Tongue tumor detection in medical hyperspectral images. Sensors 12(1), 162–174 (2011)

    Article  Google Scholar 

  11. Goto, A., Nishikawa, J., Kiyotoki, S., Nakamura, M., Nishimura, J., Okamoto, T., Ogihara, H., Fujita, Y., Hamamoto, Y., Sakaida, I.: Use of hyperspectral imaging technology to develop a diagnostic support system for gastric cancer. J. Biomed. Opt. 20(1), 016017 (2015)

    Article  Google Scholar 

  12. Li, J., Condello, S., Thomes-Pepin, J., Ma, X., Xia, Y., Hurley, T.D., Matei, D., Cheng, J.-X.: Lipid desaturation is a metabolic marker and therapeutic target of ovarian cancer stem cells. Cell Stem Cell 20(3), 303–314 (2017)

    Article  Google Scholar 

  13. Kho, E., de Boer, L.L., Van de Vijver, K.K., van Duijnhoven, F., Vrancken Peeters, M.-J.T., Sterenborg, H.J., Ruers, T.J.: Hyperspectral imaging for resection margin assessment during cancer surgeryhyperspectral imaging for resection margin assessment. Clin. Cancer Res. 25(12), 3572–3580 (2019)

  14. Wang, Q., Wang, J., Zhou, M., Li, Q., Wang, Y.: Spectral-spatial feature-based neural network method for acute lymphoblastic leukemia cell identification via microscopic hyperspectral imaging technology. Biomed. Opt. Express 8(6), 3017–3028 (2017)

    Article  Google Scholar 

  15. Dong, H., Yang, G., Liu, F., Mo, Y., Guo, Y.: Automatic brain tumor detection and segmentation using u-net based fully convolutional networks. In: Annual Conference on Medical Image Understanding and Analysis, pp. 506–517 (2017). Springer

  16. Soulami, K.B., Kaabouch, N., Saidi, M.N., Tamtaoui, A.: Breast cancer: one-stage automated detection, segmentation, and classification of digital mammograms using unet model based-semantic segmentation. Biomed. Signal Process. Control 66, 102481 (2021)

    Article  Google Scholar 

  17. Xiao, Z., Liu, B., Geng, L., Zhang, F., Liu, Y.: Segmentation of lung nodules using improved 3d-unet neural network. Symmetry 12(11), 1787 (2020)

    Article  Google Scholar 

  18. Lei, T., Zhou, W., Zhang, Y., Wang, R., Meng, H., Nandi, A.K.: Lightweight v-net for liver segmentation. In: ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1379–1383 (2020). IEEE

  19. Shi, L., Li, X., Hua, W., Chen, H., Chen, J., Fan, Z., Gao, M., Jing, Y., Lu, G., Ma, D., et al.: Ebhi-seg: A novel enteroscope biopsy histopathological haematoxylin and eosin image dataset for image segmentation tasks. arXiv preprint arXiv:2212.00532 (2022)

  20. Al-Masni, M.A., Al-Antari, M.A., Choi, M.-T., Han, S.-M., Kim, T.-S.: Skin lesion segmentation in dermoscopy images via deep full resolution convolutional networks. Comput. Methods Programs Biomed. 162, 221–231 (2018)

    Article  Google Scholar 

  21. Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer

  22. Hu, H., Zheng, Y., Zhou, Q., Xiao, J., Chen, S., Guan, Q.: Mc-unet: Multi-scale convolution unet for bladder cancer cell segmentation in phase-contrast microscopy images. In: 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1197–1199 (2019). IEEE

  23. Devalla, S.K., Renukanand, P.K., Sreedhar, B.K., Subramanian, G., Zhang, L., Perera, S., Mari, J.-M., Chin, K.S., Tun, T.A., Strouthidis, N.G.: Drunet: a dilated-residual u-net deep learning network to segment optic nerve head tissues in optical coherence tomography images. Biomed. Opt. Express 9(7), 3244–3265 (2018)

    Article  Google Scholar 

  24. Han, Y., Ye, J.C.: Framing u-net via deep convolutional framelets: Application to sparse-view ct. IEEE Trans. Med. Imaging 37(6), 1418–1429 (2018)

    Article  Google Scholar 

  25. Wang, Z., Zou, Y., Liu, P.X.: Hybrid dilation and attention residual u-net for medical image segmentation. Comput. Biol. Med. 134, 104449 (2021)

    Article  Google Scholar 

  26. Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)

  27. Chen, H., Li, C., Li, X., Rahaman, M.M., Hu, W., Li, Y., Liu, W., Sun, C., Sun, H., Huang, X.: Il-mcam: an interactive learning and multi-channel attention mechanism-based weakly supervised colorectal histopathology image classification approach. Comput. Biol. Med. 143, 105265 (2022)

    Article  Google Scholar 

  28. Mnih, V., Heess, N., Graves, A., et al.: Recurrent models of visual attention. Adv. Neural Inform. Process. Syst. 27 (2014)

  29. Hou, X., Zhao, Y., Liu, H., Guo, H., Yu, X., Ding, M.: Optic disk segmentation by combining unet and residual attention mechanism. J. Image Graph. 25, 1915–1929 (2020)

    Google Scholar 

  30. Wang, M., Chen, Y., Qi, B.: Residual unet with spatial and channel attention for automatic magnetic resonance image segmentation of rectal cancer. Multimed. Tools Appl. 81(30), 43821–43835 (2022)

    Article  Google Scholar 

  31. Guo, C., Szemenyei, M., Yi, Y., Wang, W., Chen, B., Fan, C.: Sa-unet: Spatial attention u-net for retinal vessel segmentation. In: 2020 25th International Conference on Pattern Recognition (ICPR), pp. 1236–1242 (2021). IEEE

  32. Chen, M., Zhao, C., Tian, X., Liu, Y., Wang, T., Lei, B.: Placental super micro-vessels segmentation based on resnext with convolutional block attention and u-net. In: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4015–4018 (2021). IEEE

  33. Glasbey, C.A., Mardia, K.V.: A review of image-warping methods. J. Appl. Stat. 25(2), 155–171 (1998)

    Article  MATH  Google Scholar 

  34. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)

  35. Gao, Y., Zhou, M., Li, Q., Liu, H., Zhang, Y.: Aotf based molecular hyperspectral imaging system and its image pre-processing method. In: 2015 8th International Conference on Biomedical Engineering and Informatics (BMEI), pp. 14–18 (2015). IEEE

  36. Zhang, Q., Li, Q., Yu, G., Sun, L., Zhou, M., Chu, J.: A multidimensional choledoch database and benchmarks for cholangiocarcinoma diagnosis. IEEE access 7, 149414–149421 (2019)

    Article  Google Scholar 

  37. Vidal, R., Ma, Y., Sastry, S.S., Vidal, R., Ma, Y., Sastry, S.S.: Principal Component Analysis. Springer, Cham (2016)

    Book  MATH  Google Scholar 

  38. Quan, T.M., Hildebrand, D.G.C., Jeong, W.-K.: Fusionnet: a deep fully residual convolutional neural network for image segmentation in connectomics. Front. Comput. Sci. 34, 613981 (2021)

    Article  Google Scholar 

  39. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017)

    Article  Google Scholar 

  40. Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017)

  41. Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018)

Download references

Acknowledgements

This work was supported by the Nanjing Health Science and Technology Development Special Fund Project under Grant YKK22087.

Funding

Partial financial support was received from the Nanjing Health Science and Technology Development Special Fund Project under Grant YKK22087.

Author information

Authors and Affiliations

  1. College of Computer and Information Engineering, Hohai University, Nanjing, 211100, China

    Hongmin Gao, Mengran Yang & Xueying Cao

  2. Department of Hematology, Drum Tower Hospital, School of Medicine, Nanjing University, Nanjing, 211108, China

    Peipei Xu

  3. Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai, 200241, China

    Hongmin Gao, Mengran Yang & Xueying Cao

  4. Department of Oncology, Drum Tower Hospital, School of Medicine, Nanjing University, Nanjing, 211108, China

    Qin Liu

Authors
  1. Hongmin Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mengran Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xueying Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Qin Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Peipei Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Peipei Xu.

Ethics declarations

Conflict of interest

The authors declare that there is no conflict of interest regarding the publication of this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gao, H., Yang, M., Cao, X. et al. A high-level feature channel attention UNet network for cholangiocarcinoma segmentation from microscopy hyperspectral images. Machine Vision and Applications 34, 72 (2023). https://doi.org/10.1007/s00138-023-01418-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00138-023-01418-x

Keywords

Search

Navigation