SavedAdd your journal

Journals / Acta Infologica / 2026 / Volume: 10 Issue: 1

Robust COVID-19 Lung and Infection Segmentation in Computed Tomography Scans via U-Net with ResNet-SEResNet Encoders and Optimized Sliding Window Preprocessing

Furkan Atlan(BURDUR MEHMET AKİF ERSOY UNIVERSITY)0000-0003-1602-1941
İhsan Pençe(BURDUR MEHMET AKİF ERSOY UNIVERSITY)0000-0003-0734-3869
Pages
—
Publication Date
2026-06-30
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

This study contributes to the field by demonstrating that carefully designed data preparation pipelines can be as impactful as architectural innovations, paving the way for high-performance, resource-efficient segmentation systems applicable to broader medical imaging tasks.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Accurate segmentation of lung and infection regions in CT scans plays a vital role in the early diagnosis and clinical management of COVID-19.
📊
Key Findings & Data:This study proposes a robust and efficient DL approach utilizing a U-Net architecture equipped with ResNet and SEResNet encoder backbones, combined with optimized preprocessing strategies.
💡
Conclusion & Impact:This study contributes to the field by demonstrating that carefully designed data preparation pipelines can be as impactful as architectural innovations, paving the way for high-performance, resource-efficient segmentation systems applicable to broader medical imaging tasks.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:COVID-19 segmentation · Sliding window preprocessing · ResNet

Abstract

Accurate segmentation of lung and infection regions in CT scans plays a vital role in the early diagnosis and clinical management of COVID-19. This study proposes a robust and efficient DL approach utilizing a U-Net architecture equipped with ResNet and SEResNet encoder backbones, combined with optimized preprocessing strategies. The integration of flipping and sliding window techniques, which substantially increased the data volume and enhanced feature extraction at multiple scales, is a key innovation of this work. Unlike many prior studies that focused solely on infection segmentation, this study addresses both lung and infection region segmentation on the publicly available COVID-19 CT dataset. Among the various backbones tested, SEResNet18 was selected as the optimal choice due to its high accuracy and computational efficiency. The proposed model achieved outstanding segmentation performance, with Dice score, F1-score, and IoU reaching 0.9897, 0.9900, and 0.9796 for lung segmentation and 0.9339, 0.9357, and 0.8770 for infection segmentation, respectively. These results not only surpass those of previous studies using the same dataset but also highlight the significance of TP in improving model generalizability. This study contributes to the field by demonstrating that carefully designed data preparation pipelines can be as impactful as architectural innovations, paving the way for high-performance, resource-efficient segmentation systems applicable to broader medical imaging tasks.

🔖

Cite This Article

Atlan, F., Pençe, İ. (2026). Robust COVID-19 Lung and Infection Segmentation in Computed Tomography Scans via U-Net with ResNet-SEResNet Encoders and Optimized Sliding Window Preprocessing. Acta Infologica, Volume: 10 Issue: 1. https://doi.org/10.26650/acin.1666068

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (69)

  1. Wang, C., Horby, P. W., Hayden, F. G., & Gao, G. F. (2020). A novel coronavirus outbreak of global health concern. The lancet, 395(10223), 470-473. https://doi.org/10.1016/S0140-6736(20)30185-9 google scholar
  2. WHO: World Health Organization, Deaths of Covid-19, https://covid19.who.int/ Access Date: 26/03/2025 google scholar
  3. Ojha, V., Mani, A., Pandey, N. N., Sharma, S., & Kumar, S. (2020). CT in coronavirus disease 2019 (COVID-19): a systematic review of chest CT findings in 4410 adult patients. European radiology, 30, 6129-6138. https://doi.org/10.1007/s00330-020-06975-7 google scholar
  4. Nino, G., Zember, J., Sanchez‐Jacob, R., Gutierrez, M. J., Sharma, K., & Linguraru, M. G. (2021). Pediatric lung imaging features of COVID‐19: a systematic review and meta‐analysis. Pediatric pulmonology, 56(1), 252-263. https://doi.org/10.1002/ppul.25070 google scholar
  5. Fang, Y., Zhang, H., Xie, J., Lin, M., Ying, L., Pang, P., & Ji, W. (2020). Sensitivity of chest CT for COVID-19: comparison to RT-PCR. Radiology, 296(2), 115-117. https://doi.org/10.1148/radiol.2020200432 google scholar
  6. ATLAN, F., & PENÇE, İ. (2021). An overview of artificial intelligence and medical imaging technologies. Acta Infologica, 5(1). https://doi.org/10.26650/acin.813736 google scholar
  7. Minaee, S., Kafieh, R., Sonka, M., Yazdani, S., & Soufi, G. J. (2020). Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning. Medical image analysis, 65, 101794. https://doi.org/10.1016/j.media.2020.101794 google scholar
  8. Kanne, J.P., Little, B.P., Chung, J.H., Elicker, B.M., Ketai, L.H.: Essentials for radiologists on COVID-19: An update—radiology scientific expert panel. Radiology. 296, E113–E114 (2020). https://doi.org/10.1148/radiol.2020200527 google scholar
  9. Rubin, G. D., Ryerson, C. J., Haramati, L. B., Sverzellati, N., Kanne, J. P., Raoof, S., … & Leung, A. N. (2020). The role of chest imaging in patient management during the COVID-19 pandemic: a multinational consensus statement from the Fleischner Society. Radiology, 296(1), 172-180. https://doi.org/10.1148/radiol.2020201365 google scholar
  10. Roth, H. R., Xu, Z., Tor-Díez, C., Jacob, R. S., Zember, J., Molto, J., … & Linguraru, M. G. (2022). Rapid artificial intelligence solutions in a pandemic—The COVID-19-20 Lung CT Lesion Segmentation Challenge. Medical image analysis, 82, 102605. https://doi.org/10.1016/j.media.2022.102605 google scholar
  11. Allioui, H., Mohammed, M. A., Benameur, N., Al-Khateeb, B., Abdulkareem, K. H., Garcia-Zapirain, B., … & Maskeliūnas, R. (2022). A multi-agent deep reinforcement learning approach for enhancement of COVID-19 CT image segmentation. Journal of personalized medicine, 12(2), 309. https://doi.org/10.3390/jpm12020309 google scholar
  12. Elharrouss, O., Subramanian, N., & Al-Maadeed, S. (2022). An encoder–decoder-based method for segmentation of COVID-19 lung infection in CT images. SN Computer Science, 3(1), 13. https://doi.org/10.1007/s42979-021-00874-4 google scholar
  13. Shiri, I., Arabi, H., Salimi, Y., Sanaat, A., Akhavanallaf, A., Hajianfar, G., … & Zaidi, H. (2022). COLI‐Net: deep learning‐assisted fully automated COVID‐19 lung and infection pneumonia lesion detection and segmentation from chest computed tomography images. International journal of imaging systems and technology, 32(1), 12-25. https://doi.org/10.1002/ima.22672 google scholar
  14. Zhou, T., Canu, S., & Ruan, S. (2021). Automatic COVID‐19 CT segmentation using U‐Net integrated spatial and channel attention mechanism. International Journal of Imaging Systems and Technology, 31(1), 16-27. https://doi.org/10.1002/ima.22527 google scholar
  15. Javaheri, T., Homayounfar, M., Amoozgar, Z., Reiazi, R., Homayounieh, F., Abbas, E., … & Rawassizadeh, R. (2021). CovidCTNet: an open-source deep learning approach to diagnose covid-19 using small cohort of CT images. NPJ digital medicine, 4(1), 29. https://doi.org/10.1038/s41746-021-00399-3 google scholar
  16. Liu, J., Dong, B., Wang, S., Cui, H., Fan, D. P., Ma, J., & Chen, G. (2021). COVID-19 lung infection segmentation with a novel two-stage cross-domain transfer learning framework. Medical image analysis, 74, 102205. https://doi.org/10.1016/j.media.2021.102205 google scholar
  17. Yan, Q., Wang, B., Gong, D., Luo, C., Zhao, W., Shen, J., … & You, Z. (2021). COVID-19 chest CT image segmentation network by multi-scale fusion and enhancement operations. IEEE transactions on big data, 7(1), 13-24. https://doi.org/10.1109/TBDATA.2021.3056564 google scholar
  18. Zheng, R., Zheng, Y., & Dong-Ye, C. (2021). Improved 3D U‐Net for COVID‐19 chest CT image segmentation. Scientific Programming, 2021(1), 9999368. https://doi.org/10.1155/2021/9999368 google scholar
  19. Khalifa, N. E. M., Manogaran, G., Taha, M. H. N., & Loey, M. (2022). A deep learning semantic segmentation architecture for COVID‐19 lesions discovery in limited chest CT datasets. Expert Systems, 39(6), e12742. https://doi.org/10.1111/exsy.12742 google scholar
  20. Ma, J., Wang, Y., An, X., Ge, C., Yu, Z., Chen, J., … & Yang, X. (2021). Toward data‐efficient learning: A benchmark for COVID‐19 CT lung and infection segmentation. Medical physics, 48(3), 1197-1210. https://doi.org/10.5281/zenodo.3757476 google scholar
  21. Xu, X., Wen, Y., Zhao, L., Zhang, Y., Zhao, Y., Tang, Z., … & Chen, C. Y. C. (2021). CARes‐UNet: Content‐aware residual UNet for lesion segmentation of COVID‐19 from chest CT images. Medical physics, 48(11), 7127-7140. https://doi.org/10.1002/mp.15231 google scholar
  22. Amyar, A., Modzelewski, R., Li, H., & Ruan, S. (2020). Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation. Computers in biology and medicine, 126, 104037. https://doi.org/10.1016/j.compbiomed.2020.104037 google scholar
  23. Oulefki, A., Agaian, S., Trongtirakul, T., & Laouar, A. K. (2021). Automatic COVID-19 lung infected region segmentation and measurement using CT-scans images. Pattern recognition, 114, 107747. https://doi.org/10.1016/j.patcog.2020.107747 google scholar
  24. Zhang, J., Yu, L., Chen, D., Pan, W., Shi, C., Niu, Y., … & Cheng, Y. (2021). Dense GAN and multi-layer attention based lesion segmentation method for COVID-19 CT images. Biomedical signal processing and control, 69, 102901. https://doi.org/10.1016/j.bspc.2021.102901 google scholar
  25. Agarwal, M., Agarwal, S., Saba, L., Chabert, G. L., Gupta, S., Carriero, A., … & Suri, J. S. (2022). Eight pruning deep learning models for low storage and high-speed COVID-19 computed tomography lung segmentation and heatmap-based lesion localization: A multicenter study using COVLIAS 2.0. Computers in biology and medicine, 146, 105571. https://doi.org/10.1016/j.compbiomed.2022.105571 google scholar
  26. Amara, K., Aouf, A., Kennouche, H., Djekoune, A. O., Zenati, N., Kerdjidj, O., & Ferguene, F. (2022). COVIR: A virtual rendering of a novel NN architecture O-Net for COVID-19 Ct-scan automatic lung lesions segmentation. Computers & Graphics, 104, 11-23. https://doi.org/10.1016/j.cag.2022.03.003 google scholar
  27. Qi, A., Zhao, D., Yu, F., Heidari, A. A., Wu, Z., Cai, Z., … & Chen, M. (2022). Directional mutation and crossover boosted ant colony optimization with application to COVID-19 X-ray image segmentation. Computers in biology and medicine, 148, 105810. https://doi.org/10.1016/j.compbiomed.2022.105810 google scholar
  28. Li, M., Li, X., Jiang, Y., Zhang, J., Luo, H., & Yin, S. (2022). Explainable multi-instance and multi-task learning for COVID-19 diagnosis and lesion segmentation in CT images. Knowledge-Based Systems, 252, 109278. https://doi.org/10.1016/j.knosys.2022.109278 google scholar
  29. Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017). Grad-CAM: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE International Conference on Computer Vision (pp. 618–626). https://doi.org/10.1109/ICCV.2017.74 google scholar
  30. Li, Z., Wang, S., & Zhao, L. (2021). A visualization method based on the Grad-CAM for medical image segmentation model. In 2021 IEEE International Conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology (CEI) (pp. 242–247). https://doi.org/10.1109/CEI52496.2021.9587953 google scholar
  31. Hasany, S. N., Werghi, N., & Nandakumar, K. (2024). Seg-HiRes-Grad CAM: Leveraging CAM algorithms for explaining medical semantic segmentation. Machine Learning for Biomedical Imaging (MELBA), 2024:023. https://doi.org/10.59275/j.melba.2024-b781 google scholar
  32. Draelos, R. L., & Carin, L. (2021). Use HiResCAM instead of Grad-CAM for faithful explanations of convolutional neural networks. arXiv preprint arXiv:2011.08891. google scholar
  33. Connell, M., Xin, Y., Gerard, S. E., Herrmann, J., Shah, P. K., Martin, K. T., … & Cereda, M. (2022). Unsupervised segmentation and quantification of COVID-19 lesions on computed tomography scans using CycleGAN. Methods, 205, 200-209. https://doi.org/10.1016/j.ymeth.2022.07.007 google scholar
  34. Chen, Y., Zhou, T., Chen, Y., Feng, L., Zheng, C., Liu, L., … & Pan, B. (2022). HADCNet: Automatic segmentation of COVID-19 infection based on a hybrid attention dense connected network with dilated convolution. Computers in Biology and Medicine, 149, 105981. https://doi.org/10.1016/j.compbiomed.2022.105981 google scholar
  35. Li, C. F., Xu, Y. D., Ding, X. H., Zhao, J. J., Du, R. Q., Wu, L. Z., & Sun, W. P. (2022). MultiR-net: a novel joint learning network for COVID-19 segmentation and classification. Computers in Biology and Medicine, 144, 105340. https://doi.org/10.1016/j.compbiomed.2022.105340 google scholar
  36. He, Z., Wong, A. N. N., & Yoo, J. S. (2023). Co-ERA-Net: Co-Supervision and Enhanced Region Attention for Accurate Segmentation in COVID-19 Chest Infection Images. Bioengineering, 10(8), 928. https://doi.org/10.3390/bioengineering10080928 google scholar
  37. Fang, C., Liu, Y., Liu, Y., Liu, M., Qiu, X., Li, Y., … & Yang, Y. (2022). Label‐free coronavirus disease 2019 lesion segmentation based on synthetic healthy lung image subtraction. Medical Physics, 49(7), 4632-4641. https://doi.org/10.1002/mp.15661 google scholar
  38. Li, Y., Song, S., Sun, Y., Bao, N., Yang, B., & Xu, L. (2022). Segmentation and volume quantification of epicardial adipose tissue in computed tomography images. Medical Physics, 49(10), 6477-6490. https://doi.org/10.1002/mp.15965 google scholar
  39. Fan, C., Zeng, Z., Xiao, L., & Qu, X. (2022). GFNet: automatic segmentation of COVID-19 lung infection regions using CT images based on boundary features. Pattern recognition, 132, 108963. https://doi.org/10.1016/j.patcog.2022.108963 google scholar
  40. Polat, H. (2022). Multi-task semantic segmentation of CT images for COVID-19 infections using DeepLabV3+ based on dilated residual network. Physical and Engineering Sciences in Medicine, 45(2), 443-455. https://doi.org/10.1007/s13246-022-01110-w google scholar
  41. Chiu, T. W., Tsai, Y. L., & Su, S. F. (2021). Automatic detect lung node with deep learning in segmentation and imbalance data labeling. Scientific Reports, 11(1), 11174. https://doi.org/10.1038/s41598-021-90599-4 google scholar
  42. Zanddizari, H., Nguyen, N., Zeinali, B., & Chang, J. M. (2021). A new preprocessing approach to improve the performance of CNN-based skin lesion classification. Medical & Biological Engineering & Computing, 59(5), 1123-1131. https://doi.org/10.1007/s11517-021-02355-5 google scholar
  43. Roshankhah, R., Karbalaeisadegh, Y., Greer, H., Mento, F., Soldati, G., Smargiassi, A., … & Muller, M. (2021). Investigating training-test data splitting strategies for automated segmentation and scoring of COVID-19 lung ultrasound images. The Journal of the Acoustical Society of America, 150(6), 4118-4127. https://doi.org/10.1121/10.0007272 google scholar
  44. Ter-Sarkisov, A. (2022). Covid-ct-mask-net: Prediction of covid-19 from ct scans using regional features. Applied Intelligence, 1-12. https://doi.org/10.1007/s10489-021-02731-6 google scholar
  45. Ronneberger, O., Fischer, P., & Brox, T. (2015). 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, 234-241. Springer international publishing. https://doi.org/10.1007/978-3-319-24574-4_28 google scholar
  46. 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. google scholar
  47. Hu, J., Shen, L., & Sun, G. (2018). Squeeze-and-excitation networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, 7132-7141. google scholar
  48. Shan, F., Gao, Y., Wang, J., Shi, W., Shi, N., Han, M., … & Shi, Y. (2021). Abnormal lung quantification in chest CT images of COVID‐19 patients with deep learning and its application to severity prediction. Medical physics, 48(4), 1633-1645. https://doi.org/10.1002/mp.14609 google scholar
  49. TRUBA: Turkish National Science e-Infrastructure, https://www.truba.gov.tr/ google scholar
  50. Nishio, M., Fujimoto, K., Matsuo, H., Muramatsu, C., Sakamoto, R., & Fujita, H. (2021). Lung cancer segmentation with transfer learning: usefulness of a pretrained model constructed from an artificial dataset generated using a generative adversarial network. Frontiers in artificial intelligence, 4, 694815. https://doi.org/10.3389/frai.2021.694815 google scholar
  51. Kumar Singh, V., Abdel-Nasser, M., Pandey, N., & Puig, D. (2021). Lunginfseg: Segmenting covid-19 infected regions in lung ct images based on a receptive-field-aware deep learning framework. Diagnostics, 11(2), 158. https://doi.org/10.3390/diagnostics11020158 google scholar
  52. Landim, J. A. G. S., De Araújo, F. H. D., Mathew, M. J., & De Carvalho Filho, A. O. (2021, September). An automated method for segmentation of COVID-19 lesions based on Computed Tomography using deep learning methods. In 2021 IEEE Symposium on Computers and Communications (ISCC), 1-6. https://doi.org/10.1109/ISCC53001.2021.9631534 google scholar
  53. Zhang, B., Qi, S., Wu, Y., Pan, X., Yao, Y., Qian, W., & Guan, Y. (2022). Multi-scale segmentation squeeze-and-excitation UNet with conditional random field for segmenting lung tumor from CT images. Computer Methods and Programs in Biomedicine, 222, 106946. https://doi.org/10.1016/j.cmpb.2022.106946 google scholar
  54. Müller, D., Soto-Rey, I., & Kramer, F. (2021). Robust chest CT image segmentation of COVID-19 lung infection based on limited data. Informatics in medicine unlocked, 25, 100681. https://doi.org/10.1016/j.imu.2021.100681 google scholar
  55. Saeedizadeh, N., Minaee, S., Kafieh, R., Yazdani, S., & Sonka, M. (2021). COVID TV-Unet: Segmenting COVID-19 chest CT images using connectivity imposed Unet. Computer methods and programs in biomedicine update, 1, 100007. https://doi.org/10.1016/j.cmpbup.2021.100007 google scholar
  56. Laradji, I., Rodriguez, P., Manas, O., Lensink, K., Law, M., Kurzman, L., … & Nowrouzezahrai, D. (2021). A weakly supervised consistency-based learning method for COVID-19 segmentation in CT images. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2453-2462. google scholar
  57. Wang, Y., Zhang, Y., Liu, Y., Tian, J., Zhong, C., Shi, Z., … & He, Z. (2021). Does non-COVID-19 lung lesion help? investigating transferability in COVID-19 CT image segmentation. Computer Methods and Programs in Biomedicine, 202, 106004. https://doi.org/10.1016/j.cmpb.2021.106004 google scholar
  58. Enshaei, N., Oikonomou, A., Rafiee, M. J., Afshar, P., Heidarian, S., Mohammadi, A., … & Naderkhani, F. (2022). COVID-rate: an automated framework for segmentation of COVID-19 lesions from chest CT images. Scientific Reports, 12(1), 3212. https://doi.org/10.1038/s41598-022-06854-9 google scholar
  59. Bdair, T., Wiestler, B., Navab, N., & Albarqouni, S. (2022). ROAM: Random layer mixup for semi‐supervised learning in medical images. IET Image Processing, 16(10), 2593-2608. https://doi.org/10.1049/ipr2.12511 google scholar
  60. Bougourzi, F., Distante, C., Dornaika, F., & Taleb-Ahmed, A. (2023). PDAtt-Unet: Pyramid dual-decoder attention Unet for Covid-19 infection segmentation from CT-scans. Medical Image Analysis, 86, 102797. https://doi.org/10.1016/j.media.2023.102797 google scholar
  61. Xiao, H., Ran, Z., Mabu, S., Li, Y., & Li, L. (2023). SAUNet++: an automatic segmentation model of COVID-19 lesion from CT slices. The Visual Computer, 39(6), 2291-2304. https://doi.org/10.1007/s00371-022-02414-4 google scholar
  62. Lu, F., Tang, C., Liu, T., Zhang, Z., & Li, L. (2023). Multi-attention segmentation networks combined with the sobel operator for medical images. Sensors, 23(5), 2546. https://doi.org/10.3390/s23052546 google scholar
  63. Yang, S., Zhang, X., He, Y., Chen, Y., & Zhou, Y. (2024). TBE-Net: A Deep Network Based on Tree-like Branch Encoder for Medical Image Segmentation. IEEE Journal of Biomedical and Health Informatics. https://doi.org/10.1109/JBHI.2024.3468904 google scholar
  64. Yang, J., Li, H., Wang, H., & Han, M. (2024). 3D medical image segmentation based on semi-supervised learning using deep co-training. Applied Soft Computing, 159, 111641. https://doi.org/10.1016/j.asoc.2024.111641 google scholar
  65. Nakamoto, I., Zhuang, W., Chen, H., & Guo, Y. (2024). PDSMNet: parallel pyramid dual-stream modeling for automatic lung COVID-19 infection segmentations. Engineering Applications of Artificial Intelligence, 133, 108541. https://doi.org/10.1016/j.engappai.2024.108541 google scholar
  66. Ağralı, M., & Kılıç, V. (2024). U-TranSvision: transformer-based deep supervision approach for COVID-19 lesion segmentation on computed tomography images. Biomedical Signal Processing and Control, 93, 106167. https://doi.org/10.1016/j.bspc.2024.106167 google scholar
  67. Chu, Y., Wang, J., Xiong, Y., Gao, Y., Liu, X., Luo, G., … & Meng, X. (2025). Point-annotation supervision for robust 3D pulmonary infection segmentation by CT-based cascading deep learning. Computers in Biology and Medicine, 187, 109760. https://doi.org/10.1016/j.compbiomed.2025.109760 google scholar
  68. Qiao, Z., Wu, L., Heidari, A. A., Zhao, X., & Chen, H. (2025). An enhanced tree-seed algorithm for global optimization and neural architecture search optimization in medical image segmentation. Biomedical Signal Processing and Control, 104, 107457. https://doi.org/10.1016/j.bspc.2024.107457 google scholar
  69. Choi, J., Condori, M. A. T., Miranda, P. A. V., & Tsuzuki, M. S. G. (2026). Lung automatic seeding and segmentation: A robust method based on relaxed oriented image foresting transform. Biomedical Signal Processing and Control, 117, 109537. doi:10.1016/j.bspc.2026.109537. google scholar