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A Lightweight Segmentation Network Based on Weak Supervision for COVID-19 Detection

  • Conference paper
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Digital Multimedia Communications (IFTC 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1766))

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Abstract

The Coronavirus Disease 2019 (COVID-19) outbreak in late 2019 threatens global health security. Computed tomography (CT) can provide richer information for the diagnosis and treatment of COVID-19. Unfortunately, labeling of COVID-19 lesion chest CT images is an expensive affair. We solved the challenge of chest CT labeling by simply marking point annotations to the lesion areas, i.e., by marking individual pixels for each lesion area in the chest CT scan. It takes only a few seconds to complete the labeling using this labeling strategy. We also designed a lightweight segmentation model with approximately 10% of the number of model parameters of the conventional model. So, the proposed model segmented the lesions of a single image in only 0.05 s. In order to obtain the shape and size of lesions from point labels, the convex-hull based segmentation (CHS) loss function is proposed in this paper, which enables the model to obtain an approximate fully supervised performance on point labels. The experiments were compared with the current state-of-the-art (SOTA) point label segmentation methods on the COVID-19-CT-Seg dataset, and our model showed a large improvement: IoU improved by 28.85%, DSC improved by 28.91%, Sens improved by 13.75%, Spes improved by 1.18%, and MAE decreased by 1.10%. Experiments on the dataset show that the proposed model combines the advantages of lightweight and weak supervision, resulting in more accurate COVID-19 lesion segmentation results while having only a 10% performance difference with the fully supervised approach.

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References

  1. Huang, C., et al.: Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 395(10223), 497–506 (2020)

    Article  Google Scholar 

  2. Dong, E., Du, H., Gardner, L.: An interactive web-based dashboard to track COVID-19 in real time. Lancet. Infect. Dis 20(5), 533–534 (2020)

    Article  Google Scholar 

  3. Ai, T., et al.: Correlation of chest CT and RT-PCR testing in coronavirus disease 2019 (COVID-19) in china: a report of 1014 cases. Radiology 296, E32–E40 (2020)

    Article  Google Scholar 

  4. Zu, Z.Y., et al.: Coronavirus disease 2019 (COVID-19): a perspective from china. Radiology 296(2), E15–E25 (2020)

    Article  Google Scholar 

  5. Iqbal, A., et al.: The COVID-19 sequelae: a cross-sectional evaluation of post-recovery symptoms and the need for rehabilitation of COVID-19 survivors. Cureus 13(2), e13080 (2021)

    Google Scholar 

  6. Froidure, A., et al.: Integrative respiratory follow-up of severe COVID-19 reveals common functional and lung imaging sequelae. Respir. Med. 181, 106383 (2021)

    Article  Google Scholar 

  7. Gao, R.: Rethink dilated convolution for real-time semantic segmentation. arXiv preprint arXiv:2111.09957 (2021)

  8. Ma, J., et al.: Towards efficient COVID-19 CT annotation: a benchmark for lung and infection segmentation (2020). https://arxiv.org/abs/2004.12537v1

  9. Abualigah, L., Diabat, A., Sumari, P., Gandomi, A.H.: A novel evolutionary arithmetic optimization algorithm for multilevel thresholding segmentation of COVID-19 CT images. Processes 9(7), 1155 (2021)

    Article  Google Scholar 

  10. Shen, C., et al.: Quantitative computed tomography analysis for stratifying the severity of coronavirus disease 2019. J. Pharm. Anal. 10(2), 123–129 (2020)

    Article  Google Scholar 

  11. Oulefki, A., Agaian, S., Trongtirakul, T., Laouar, A.K.: Automatic COVID-19 lung infected region segmentation and measurement using CT-scans images. Pattern Recogn. 114, 107747 (2021)

    Article  Google Scholar 

  12. Joshi, A., Khan, M.S., Soomro, S., Niaz, A., Han, B.S., Choi, K.N.: SRIS: saliency-based region detection and image segmentation of COVID-19 infected cases. IEEE Access 8, 190487–190503 (2020)

    Article  Google Scholar 

  13. 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

    Chapter  Google Scholar 

  14. Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: redesigning skip connections to exploit multiscale features in image segmentation. IEEE Trans. Med. Imaging 39(6), 1856–1867 (2019)

    Article  Google Scholar 

  15. Milletari, F., Navab, N., Ahmadi, S.A.: V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565–571. IEEE (2016)

    Google Scholar 

  16. Oktay, O., et al.: Attention U-Net: learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018)

  17. Han, M., et al.: Segmentation of Ct thoracic organs by multi-resolution VB-Nets. In: SegTHOR@ ISBI (2019)

    Google Scholar 

  18. Wu, Y.H., et al.: JCS: an explainable COVID-19 diagnosis system by joint classification and segmentation. IEEE Trans. Image Process. 30, 3113–3126 (2021)

    Article  Google Scholar 

  19. Paluru, N., et al.: ANAM-Net: anamorphic depth embedding-based lightweight CNN for segmentation of anomalies in COVID-19 chest CT images. IEEE Trans. Neural Netw. Learn. Syst. 32(3), 932–946 (2021)

    Article  Google Scholar 

  20. Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: 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)

    Article  Google Scholar 

  21. Xiao, H., Ran, Z., Mabu, S., Li, Y., Li, L.: SauNet++: an automatic segmentation model of COVID-19 lesion from CT slices. Vis. Comput., 1–14 (2022). https://doi.org/10.1007/s00371-022-02414-4

  22. Enshaei, N., et al.: COVID-rate: an automated framework for segmentation of COVID-19 lesions from chest CT images. Sci. Rep. 12(1), 1–18 (2022)

    Article  Google Scholar 

  23. Raj, A.N.J., et al.: ADID-UNET-a segmentation model for COVID-19 infection from lung CT scans. PeerJ Comput. Sci. 7, e349 (2021)

    Article  Google Scholar 

  24. Ouyang, X., et al.: Dual-sampling attention network for diagnosis of COVID-19 from community acquired pneumonia. IEEE Trans. Med. Imaging 39(8), 2595–2605 (2020)

    Article  Google Scholar 

  25. Shi, T., Cheng, F., Li, Z., Zheng, C., Xu, Y., Bai, X.: Automatic segmentation of COVID-19 infected regions in chest CT images based on 2D/3D model ensembling. Acta Automatica Sinica 47(AAS-CN-2021-0400), 1 (2021). https://doi.org/10.16383/j.aas.c210400, https://www.aas.net.cn/cn/article/doi/10.16383/j.aas.c210400

  26. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: MobileNetV2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510–4520 (2018)

    Google Scholar 

  27. Ma, N., Zhang, X., Zheng, H.T., Sun, J.: ShuffleNetV2: practical guidelines for efficient CNN architecture design. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 116–131 (2018)

    Google Scholar 

  28. Mehta, S., Rastegari, M., Shapiro, L., Hajishirzi, H.: EspNetv2: a light-weight, power efficient, and general purpose convolutional neural network. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9190–9200 (2019)

    Google Scholar 

  29. Wu, B., et al.: Shift: A Zero FLOP, zero parameter alternative to spatial convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9127–9135 (2018)

    Google Scholar 

  30. Jeon, Y., Kim, J.: Constructing fast network through deconstruction of convolution. In: Advances in Neural Information Processing Systems, vol. 31 (2018)

    Google Scholar 

  31. Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)

    Google Scholar 

  32. Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1251–1258 (2017)

    Google Scholar 

  33. Howard, A.G., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)

  34. Qiu, Y., Liu, Y., Li, S., Xu, J.: MiniSeg: an extremely minimum network for efficient COVID-19 segmentation. In: Proceedings of the AAAI Conference on Artificial Intelligence vol. 35(6), pp. 4846–4854 (2021). https://doi.org/10.1609/aaai.v35i6.16617

  35. Zhou, Y., Zhu, Y., Ye, Q., Qiu, Q., Jiao, J.: Weakly supervised instance segmentation using class peak response. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3791–3800 (2018)

    Google Scholar 

  36. Tang, W., et al.: M-SEAM-NAM: multi-instance self-supervised equivalent attention mechanism with neighborhood affinity module for double weakly supervised segmentation of COVID-19. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12907, pp. 262–272. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87234-2_25

    Chapter  Google Scholar 

  37. Liu, X., et al.: Weakly supervised segmentation of COVID-19 infection with scribble annotation on CT images. Pattern Recogn. 122, 108341 (2022)

    Article  Google Scholar 

  38. Khoreva, A., Benenson, R., Hosang, J., Hein, M., Schiele, B.: Simple does it: weakly supervised instance and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 876–885 (2017)

    Google Scholar 

  39. Hsu, C.C., Hsu, K.J., Tsai, C.C., Lin, Y.Y., Chuang, Y.Y.: Weakly supervised instance segmentation using the bounding box tightness prior. In: Advances in Neural Information Processing Systems, vol. 32 (2019)

    Google Scholar 

  40. Laradji, I., et al.: 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, pp. 2453–2462 (2021)

    Google Scholar 

  41. Laradji, I.H., Saleh, A., Rodriguez, P., Nowrouzezahrai, D., Azghadi, M.R., Vazquez, D.: Weakly supervised underwater fish segmentation using affinity LCFCN. Sci. Rep. 11(1), 1–10 (2021)

    Article  Google Scholar 

  42. Qian, R., Wei, Y., Shi, H., Li, J., Liu, J., Huang, T.: Weakly supervised scene parsing with point-based distance metric learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33(01), pp. 8843–8850 (2019). https://doi.org/10.1609/aaai.v33i01.33018843

  43. MedSeg, Håvard, Bjørke, J., Tomas, S.: MedSeg COVID dataset 1 (2021). https://figshare.com/articles/dataset/MedSeg_Covid_Dataset_1/13521488

  44. MedSeg, Håvard, Bjørke, J., Tomas, S.: Medseg COVID dataset 2 (2021). https://figshare.com/articles/dataset/Covid_Dataset_2/13521509

  45. Ma, J., et al.: COVID-19 CT lung and infection segmentation dataset (2020). https://doi.org/10.5281/zenodo.3757476

  46. Morozov, S.P., et al.: MosMedData: chest CT scans with COVID-19 related findings dataset. arXiv preprint arXiv:2005.06465 (2020)

  47. Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018)

    Google Scholar 

  48. Cheng, B., Parkhi, O., Kirillov, A.: Pointly-supervised instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2617–2626 (2022)

    Google Scholar 

  49. Mettes, P., van Gemert, J.C., Snoek, C.G.M.: Spot on: action localization from pointly-supervised proposals. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9909, pp. 437–453. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46454-1_27

    Chapter  Google Scholar 

  50. Papadopoulos, D.P., Uijlings, J.R., Keller, F., Ferrari, V.: Training object class detectors with click supervision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6374–6383 (2017)

    Google Scholar 

  51. Bearman, A., Russakovsky, O., Ferrari, V., Fei-Fei, L.: What’s the point: semantic segmentation with point supervision. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 549–565. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46478-7_34

    Chapter  Google Scholar 

  52. Radosavovic, I., Kosaraju, R.P., Girshick, R., He, K., Dollár, P.: Designing network design spaces. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10428–10436 (2020)

    Google Scholar 

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Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Shanghai University of Electric Power, Shanghai, China

    Fangfang Lu, Tianxiang Liu, Chi Tang & Zhihao Zhang

  2. Institute of Image Communication and Network Engineering, Shanghai Jiao Tong University, Shanghai, China

    Guangtao Zhai, Xiongkuo Min & Wei Sun

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  1. Fangfang Lu
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  2. Tianxiang Liu
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  3. Chi Tang
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  4. Zhihao Zhang
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  5. Guangtao Zhai
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  6. Xiongkuo Min
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  7. Wei Sun
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Correspondence to Tianxiang Liu .

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Editors and Affiliations

  1. Shanghai Jiao Tong University, Shanghai, China

    Guangtao Zhai

  2. Shanghai Jiao Tong University, Shanghai, China

    Jun Zhou

  3. Shanghai Jiao Tong University, Shanghai, China

    Hua Yang

  4. Shanghai Jiao Tong University, Shanghai, China

    Xiaokang Yang

  5. Shanghai University, Shanghai, China

    Ping An

  6. Shanghai Jiao Tong University, Shanghai, China

    Jia Wang

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Lu, F. et al. (2023). A Lightweight Segmentation Network Based on Weak Supervision for COVID-19 Detection. In: Zhai, G., Zhou, J., Yang, H., Yang, X., An, P., Wang, J. (eds) Digital Multimedia Communications. IFTC 2022. Communications in Computer and Information Science, vol 1766. Springer, Singapore. https://doi.org/10.1007/978-981-99-0856-1_7

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  • DOI: https://doi.org/10.1007/978-981-99-0856-1_7

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