Effective Algorithm for Biomedical Image Segmentation
https://doi.org/10.35596/1729-7648-2024-22-3-84-92
Abstract
Biomedical image segmentation plays an important role in quantitative analysis, clinical diagnosis, and medical manipulation. Objects in medical images have different scales, types, complex backgrounds, and similar tissue appearances, making information extraction challenging. To solve this problem, a module is proposed that takes into account the features of images, which will improve the biomedical image segmentation network FE-Net. An integral part of the FE-Net algorithm is the connection skipping mechanism, which ensures the connection and fusion of feature maps from different layers in the encoder and decoder. Features at the encoder level are combined with high-level semantic knowledge at the decoder level. The algorithm establishes connections between feature maps, which is used in medicine for image processing. The proposed method is tested on three public datasets: Kvasir-SEG, CVC-ClinicDB and 2018 Data Science Bowl. Based on the results of the study, it was found that FE-Net demonstrates better performance compared to other methods in terms of Intersection over Union and F1-score. The network under consideration copes more effectively with segmentation details and object boundaries, while maintaining high accuracy. The study was conducted jointly with the Department of Magnetic Resonance Imaging of the N. N. Alexandrov National Oncology Center. Access to the source code of the algorithm and additional technical details is available at https://github.com/tyjcbzd/FE-Net.
Keywords
biomedical image segmentation,
convolution neural network,
feature aware module,
attention mechanism,
U-shaped network
Supporting agencies: This work is funded by the China Scholarship Council.
About the Authors
Zhao Di
Belarusian State University of Informatics and Radioelectronics
Belarus
Belarus
Postgraduate at the Department of Information Technologies in Automated Systems
220013, Minsk, Brovki St., 6
Tang Yi
Belarusian State University of Informatics and Radioelectronics
Belarus
Belarus
Master’s Student at the Department of Information Technologies in Automated Systems
220013, Minsk, Brovki St., 6
A. B. Gourinovitch
Belarusian State University of Informatics and Radioelectronics
Belarus
Belarus
Gourinovitch Alevtina Borisovna, Assistant Professor at the Department of Information Technologies in Automated Systems
220013, Minsk, Brovki St., 6
Tel.: +375 29 622-69-34
References
1. Li tjens G., Kooi T., Bejnordi B. E., Adiyoso Setio А. А., Ciompi F., Ghafoorian M., et al. (2017) A Survey on Deep Learning in Medical Image Analysis. Medical Image Analysis. 60–88.
2. Ronneberger O., Fischer P., Brox T. (2015) U-Net: Convolutional Networks for Biomedical Image Segmentation. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, 18th International Conference . 234–241.
3. Zhou Z., Rahman Siddiquee M. M., Tajbakhsh N., Liang J. (2018) Unet++: A Nested U-Net Architecture for Medical Image Segmentation. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, 4th International Workshop DLMIA 2018, and 8th International Workshop ML-CDS 2018 . 3–11.
4. Jha D., Smedsrud P. H., Riegler M. A., Johansen D. (2019) Resunet++: An Advanced Architecture for Medical Image Segmentation. In 2019 IEEE International Symposium on Multimedia . 225–255.
5. Long J., Shelhamer E., Darrell T. (2015) Fully Convolutional Networks for Semantic Segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 3431–3440.
6. Seo H., Huang C., Bassenne M., Xiao R., Xing L. (2019) Modified U-Net (mU-Net) with Incorporation of Object-Dependent High Level Features for Improved Liver and Liver-Tumor Segmentation in CT Images. IEEE Transactions on Medical Imaging . 39 (5), 1316–1325.
7. He K., Zhang X., Ren S., Sun J. (2016) Deep Residual Learning for Image Recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 770–778.
8. Hu J., Shen L., Sun G. (2018) Squeeze-and-Excitation Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 7132–7141.
9. Zhang Z., Liu Q., Wang Y. (2018) Road Extraction by Deep Residual U-Net. IEEE Geoscience and Remote Sensing Letters. 15 (5), 749–753.
10. Tomar N. K., Jha D., Riegler M. A., Johansen H. D., Johansen D., Rittscher J., et al. (2022) Fanet: A Feedback Attention Network for Improved Biomedical Image Segmentation. IEEE Transactions on Neural Networks and Learning Systems .
11. Yang C., Guo X., Zhu M., Ibragimov B., Yuan Y. (2021) Mutual-Prototype Adaptation for Cross-Domain Polyp Segmentation. IEEE Journal of Biomedical and Health Informatics . 25 (10), 3886–3897. DOI: 10.1109/JBHI.2021.3077271.
12. Chlap P., Min H., Vandenberg N., Dowling J., Holloway L., Haworth A. (2021) A Review of Medical Image Data Augmentation Techniques for Deep Learning Applications. Journal of Medical Imaging and Radiation Oncology . 65 (5), 545–563. DOI: 10.1111/1754-9485.13261.
13. Oktay O., Schlemper J., Folgoc L. L., Lee M., Heinrich M., Misawa K., et al. (2018) Attention U-Net: Learning Where to Look for the Pancreas. 1st Conference on Medical Imaging with Deep Learning (MIDL 2018), Amsterdam, the Netherlands.
14. Jha D., Riegler M. A., Johansen D., Halvorsen P., Johansen H. D. (2020) Doubleu-Net: A Deep Convolutional Neural Network for Medical Image Segmentation. In 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems. 558–564.
15. Iqbal A., Sharif M., Khan M. A., Nisar W., Alhaisoni M. (2022) FF-UNet: A U-Shaped Deep Convolutional Neural Network for Multimodal Biomedical Image Segmentation. Cognitive Computation. 14 (4), 1287–1302.
Review
For citations:
Di Zh., Yi T., Gourinovitch A.B. Effective Algorithm for Biomedical Image Segmentation. Doklady BGUIR. 2024;22(3):84-92. https://doi.org/10.35596/1729-7648-2024-22-3-84-92
Views: 68
Email this article 