COVID-19 lesions image segmentation method based on UniFormer
Vol 7, Issue 1, 2024
VIEWS - 0 (Abstract) 0 (PDF)
Abstract
In view of the fact that the convolution neural network segmentation method lacks to capture the global dependency of infected areas in COVID-19 images, which is not conducive to the complete segmentation of scattered lesion areas, this paper proposes a COVID-19 lesion segmentation method UniUNet based on UniFormer with its strong ability to capture global dependency. Firstly, a U-shaped encoder-decoder structure based on UniFormer is designed, which can enhance the cooperation ability of local and global relations. Secondly, Swin spatial pyramid pooling module is introduced to compensate the influence of spatial resolution reduction in the encoder process and generate multi-scale representation. Multi-scale attention gate is introduced at the skip connection to suppress redundant features and enhance important features. Experiment results show that, compared with the other four methods, the proposed model achieves better results in Dice, loU and Recall on COVID-19-CT-Seg and CC-CCIII dataset, and achieves a more complete segmentation of the lesion area.
Keywords
Full Text:
PDFReferences
1. Geng P, Tan Z, Wang Y, et al. STCNet: Alternating CNN and improved transformer network for COVID-19 CT image segmentation. Biomedical Signal Processing and Control. 2024; 93: 106205. doi: 10.1016/j.bspc.2024.106205
2. Niitsu H, Mizumoto M, Li Y, et al. Tumor Response on Diagnostic Imaging after Proton Beam Therapy for Hepatocellular Carcinoma. Cancers. 2024; 16(2): 357. doi: 10.3390/cancers16020357
3. Shimizu S, Nakai K, Li Y, et al. Boron Neutron Capture Therapy for Recurrent Glioblastoma Multiforme: Imaging Evaluation of a Case with Long-Term Local Control and Survival. Cureus. 2023. doi: 10.7759/cureus.33898
4. Li S, Mo Y, Li Z. Automated Pneumonia Detection in Chest X-Ray Images Using Deep Learning Model. Innovations in Applied Engineering and Technology. Published online December 12, 2022: 1–6. doi: 10.62836/iaet.vli1.002
5. Bueno C, Barker MD, Orphan VJ. X-Ray Detector Physics and Applications II. Society of Photo Optical; 1993. doi: 10.1117/12.164737
6. Zheng T, Lin F, Li X, et al. Deep learning-enabled fully automated pipeline system for segmentation and classification of single-mass breast lesions using contrast-enhanced mammography: a prospective, multicentre study. eClinicalMedicine. 2023; 58: 101913. doi: 10.1016/j.eclinm.2023.101913
7. Zhang J, Chen D, Ma D, et al. CdcSegNet: Automatic COVID-19 Infection Segmentation from CT Images. IEEE Transactions on Instrumentation and Measurement. 2023; 72: 1–13. doi: 10.1109/tim.2023.3267355
8. Shi F, Wang J, Shi J, et al. Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19. IEEE Reviews in Biomedical Engineering. 2021; 14: 4–15. doi: 10.1109/rbme.2020.2987975
9. Milletari F, Navab N, Ahmadi SA. V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. In: Proceedings of the 2016 Fourth International Conference on 3D Vision (3DV). 2016. doi: 10.1109/3dv.2016.79
10. Wang G, Liu X, Li C, et al. A Noise-Robust Framework for Automatic Segmentation of COVID-19 Pneumonia Lesions from CT Images. IEEE Transactions on Medical Imaging. 2020; 39(8): 2653–2663. doi: 10.1109/tmi.2020.3000314
11. Yu L, Hu Z, Zhang F, et al. Unmanned aerial vehicle image biological soil crust recognition based on UNet++. International Journal of Remote Sensing. 2022; 43(7): 2660–2676. doi: 10.1080/01431161.2022.2066486
12. Wang B, Jin S, Yan Q, et al. AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system. Applied Soft Computing. 2021; 98: 106897. doi: 10.1016/j.asoc.2020.106897
13. Cong R, Zhang Y, Yang N, et al. Boundary Guided Semantic Learning for Real-Time COVID-19 Lung Infection Segmentation System. IEEE Transactions on Consumer Electronics. 2022; 68(4): 376–386. doi: 10.1109/tce.2022.3205376
14. Ibtehaz N, Kihara D. Acc-unet: a completely convolutional unet model for the 2020s. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention. 2023: 692-702. doi: 10.48550/arXiv.2308.13680
15. Zhou T, Canu S, Ruan S. An automatic COVID-19 CT segmentation network using spatial and channel attention mechanism. ARXIV preprint arXiv:2004.06673, 2020.
16. Li CF, Xu YD, Ding XH, et al. MultiR-Net: A Novel Joint Learning Network for COVID-19 segmentation and classification. Computers in Biology and Medicine. 2022; 144: 105340. doi: 10.1016/j.compbiomed.2022.105340
17. Xiao H, Ran Z, Mabu S, et al. SAUNet++: an automatic segmentation model of COVID-19 lesion from CT slices. The Visual Computer. 2022; 39(6): 2291–2304. doi: 10.1007/s00371-022-02414-4
18. Zhao S, Li Z, Chen Y, et al. SCOAT-Net: A novel network for segmenting COVID-19 lung opacification from CT images. Pattern Recognition. 2021; 119: 108109. doi: 10.1016/j.patcog.2021.108109
19. Jia W, Ma S, Geng P, et al. DT-Net: Joint Dual-Input Transformer and CNN for Retinal Vessel Segmentation. Computers, Materials & Continua. 2023; 76(3): 3393–3411. doi: 10.32604/cmc.2023.040091
20. Karlinsky L, Michaeli T, Nishino K, et al. Computer Vision – ECCV 2022 Workshops. Springer Nature Switzerland; 2023. doi: 10.1007/978-3-031-25066-8
21. Li K, Wang Y, Gao P, et al. Uniformer: unified transformer for efficient spatiotemporal representation learning. ARXIV preprint arXiv:2201.04676. 2022.
22. Bello IM, Zhang K, Su Y, et al. Densely multiscale framework for segmentation of high resolution remote sensing imagery. Computers & Geosciences. 2022; 167: 105196. doi: 10.1016/j.cageo.2022.105196
23. Azad R, Heidari M, Shariatnia M, et al. Transdeeplab: convolution-free transformer-based deeplab v3+ for medical image segmentation. Proceeding of the International Workshop on Predictive Intelligence in Medicine. 2022: 91-102. doi: 10.48550/arXiv.2208.00713
24. Tang F, Wang L, Ning C, et al. CMU-Net: A Strong ConvMixer-based Medical Ultrasound Image Segmentation Network. 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI). 2023. doi: 10.1109/isbi53787.2023.10230609
25. Zhang K, Liu X, Shen J, et al. Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography. Cell. 2020; 181(6): 1423–1433.e11. doi: 10.1016/j.cell.2020.04.045
26. Ma J, Wang Y, An X, et al. Toward data‐efficient learning: A benchmark for COVID‐19 CT lung and infection segmentation. Medical Physics. 2021; 48(3): 1197–1210. doi: 10.1002/mp.14676
27. Liu J, Zhao D, Shen J, et al. HRD-Net: High resolution segmentation network with adaptive learning ability of retinal vessel features. Computers in Biology and Medicine. 2024; 173: 108295. doi: 10.1016/j.compbiomed.2024.108295
28. Geng P, Lu J, Zhang Y, et al. TC-Fuse: A Transformers Fusing CNNs Network for Medical Image Segmentation. Computer Modeling in Engineering & Sciences. 2023; 137(2): 2001–2023. doi: 10.32604/cmes.2023.027127
29. Chen L, Bentley P, Mori K, et al. DRINet for Medical Image Segmentation. IEEE Transactions on Medical Imaging. 2018; 37(11): 2453–2462. doi: 10.1109/tmi.2018.2835303
30. Wang R, Lei T, Cui R, et al. Medical image segmentation using deep learning: A survey. IET Image Processing. 2022; 16(5): 1243–1267. doi: 10.1049/ipr2.12419
31. Fan DP, Zhou T, Ji GP, et al. Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images. IEEE Transactions on Medical Imaging. 2020; 39(8): 2626–2637. doi: 10.1109/tmi.2020.2996645
32. Cao H, Wang Y, Chen J, et al. Swin-unet: unet-like pure transformer for medical image segmentation. In: Proceedings of the European Conference on Computer Vision; 2022. doi: 10.48550/arXiv.2105.05537
DOI: https://doi.org/10.24294/irr7128
Refbacks
- There are currently no refbacks.
License URL: https://creativecommons.org/licenses/by/4.0/
This site is licensed under a Creative Commons Attribution 4.0 International License.