Light In The Black: An Evaluation of Data Augmentation Techniques for COVID-19 CT’s Semantic Segmentation

• Bruno A. Krinski UFPR
• Daniel V. Ruiz UFPR
• Eduardo Todt UFPR

DOI: https://doi.org/10.5753/sbcas.2022.222495

Abstract

With the COVID-19 global pandemic, computer-assisted diagnoses of medical images have gained much attention, and robust methods of Semantic Segmentation of Computed Tomography (CT) became highly desirable. Semantic Segmentation of CT is one of many research fields of automatic detection of COVID-19 and has been widely explored since the COVID-19 outbreak. In this work, we propose an extensive analysis of how different data augmentation techniques improve the training of encoder-decoder neural networks on this problem. Twenty different data augmentation techniques were evaluated on five different datasets. Each dataset was validated through a five-fold crossvalidation strategy, thus resulting in over 3,000 experiments. Our findings show that spatial level transformations are the most promising to improve the learning of neural networks on this problem.

References

Bertalmio, M., Bertozzi, A. L., and Sapiro, G. (2001). Navier-stokes, fluid dynamics, and image and video inpainting. In Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, volume 1, pages I-I.

Buslaev, A., Iglovikov, V. I., Khvedchenya, E., Parinov, A., Druzhinin, M., and Kalinin, A. A. (2020). Albumentations: Fast and flexible image augmentations. Information, 11(2).

Cao, F. and Bao, Q. (2020). A survey on image semantic segmentation methods with convolutional neural network. In 2020 International Conference on Communications, Information System and Computer Engineering (CISCE). IEEE.

Chen, M., Tu, C., Tan, C., Zheng, X., Wang, X., Wu, J., Huang, Y., Wang, Z., Yan, Y., Li, Z., Shan, H., Liu, J., and Huang, J. (2020a). Key to successful treatment of covid-19: accurate identification of severe risks and early intervention of disease progression. medRxiv, DOI:10.1101/2020.04.06.20054890.

Chen, P., Liu, S., Zhao, H., and Jia, J. (2020b). Gridmask data augmentation. arXiv preprint, arXiv:2001.04086.

Chen, X., Yao, L., and Zhang, Y. (2020c). Residual attention u-net for automated multiclass segmentation of covid-19 chest ct images. arXiv preprint, arXiv:2004.05645.

Dabouei, A., Soleymani, S., Taherkhani, F., and Nasrabadi, N. M. (2020). Supermix: Supervising the mixing data augmentation. arXiv preprint, arXiv:2003.05034.

Fang, H.-S., Sun, J., Wang, R., Gou, M., Li, Y.-L., and Lu, C. (2019). Instaboost: Boosting instance segmentation via probability map guided copy-pasting. arXiv preprint, arXiv:1908.07801.

Field, D. J., Hayes, A., and Hess, R. F. (1993). Contour integration by the human visual system: Evidence for a local "association field". Vision Research, 33(2):173-193.

He, K., Gkioxari, G., Dollar, P., and Girshick, R. (2017). Mask r-CNN. In 2017 IEEE International Conference on Computer Vision (ICCV). IEEE.

Hendrycks, D., Mu, N., Cubuk, E. D., Zoph, B., Gilmer, J., and Lakshminarayanan, B. (2020). AugMix: A simple data processing method to improve robustness and uncertainty. Proceedings of the International Conference on Learning Representations (ICLR).

Jun, M. et al. (2020). Covid-19 ct lung and infection segmentation dataset. Zenodo. Available at: https://zenodo.org/record/3757475. Accessed: 2022-02-22.

Kisantal, M., Wojna, Z., Murawski, J., Naruniec, J., and Cho, K. (2019). Augmentation for small object detection. In 9th International Conference on Advances in Computing and Information Technology (ACITY 2019). Aircc Publishing Corporation.

Krinski, B. A., Ruiz, D. V., and Todt, E. (2021). Spark in the dark: Evaluating encoderdecoder pairs for COVID-19 CT's semantic segmentation. In 2021 Latin American Robotics Symposium (LARS). IEEE.

Laroca, R., Araujo, A. B., Zanlorensi, L. A., De Almeida, E. C., and Menotti, D. (2021). Towards image-based automatic meter reading in unconstrained scenarios: A robust and efficient approach. IEEE Access, 9:67569-67584.

Laroca, R., Cardoso, E. V., Lucio, D. R., Estevam, V., and Menotti, D. (2022). On the cross-dataset generalization in license plate recognition. In International Conference on Computer Vision Theory and Applications (VISAPP), pages 166-178.

Liu, M.-Y., Huang, X., Mallya, A., Karras, T., Aila, T., Lehtinen, J., and Kautz, J. (2019). Few-shot unsupervised image-to-image translation. In 2019 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE.

MedSeg (2021). Covid-19 ct segmentation dataset. Available at: http://medicalsegmentation.com/covid19/. Accessed: 2021-05-03.

Morozov, S., Andreychenko, A., Pavlov, N., Vladzymyrskyy, A., Ledikhova, N., Gombolevskiy, V., Blokhin, I., Gelezhe, P., Gonchar, A., and Chernina, V. (2020). Mosmeddata: Chest ct scans with covid-19 related findings dataset. medRxiv, DOI:10.1101/2020.05.20.20100362.

Müller, D., Rey, I. S., and Kramer, F. (2020). Automated chest ct image segmentation of covid-19 lung infection based on 3d u-net. arXiv preprint, arXiv:2007.04774.

of Medicine, J. H. U. (2022). Coronavirus resource center. Available at: https://coronavirus.jhu.edu/. Accessed: 2022-02-22.

Qiblawey, Y., Tahir, A., Chowdhury, M. E. H., Khandakar, A., Kiranyaz, S., Rahman, T., Ibtehaz, N., Mahmud, S., Al-Madeed, S., and Musharavati, F. (2021). Detection and severity classification of covid-19 in ct images using deep learning. arXiv preprint, arXiv:2102.07726.

Raj, A. N. J., Zhu, H., Khan, A., Zhuang, Z., Yang, Z., Mahesh, V. G. V., and Karthik, G. (2021). ADID-UNET-a segmentation model for COVID-19 infection from lung CT scans. PeerJ Computer Science, 7:e349.

Ruiz, D. V., Krinski, B. A., and Todt, E. (2019). ANDA: A novel data augmentation technique applied to salient object detection. In 2019 19th International Conference on Advanced Robotics (ICAR), pages 487-492.

Ruiz, D. V., Krinski, B. A., and Todt, E. (2020a). IDA: Improved data augmentation applied to salient object detection. In 2020 33rd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), pages 210-217.

Ruiz, D. V., Salomon, G., and Todt, E. (2020b). Can giraffes become birds? an evaluation of image-to-image translation for data generation. Anais do Computer on the Beach, 11(1):176-182. DOI: 10.14210/cotb.v11n1.p176-182, also available as arXiv preprint, arXiv:2001.03637.

Shi, F., Wang, J., Shi, J., Wu, Z., Wang, Q., Tang, Z., He, K., Shi, Y., and Shen, D. (2021). Review of artificial intelligence techniques in imaging data acquisition, segmentation, and diagnosis for COVID-19. IEEE Reviews in Biomedical Engineering, 14:4-15.

Summers, C. and Dinneen, M. J. (2019). Improved mixed-example data augmentation. In 2019 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE.

Tsai, E. et al. (2020). Medical imaging data resource center rsna international covid radiology database release 1a chest ct covid+ (midrc-ricord-1a). DOI: 10.7937/VTW4X588.

Wang, C., Horby, P. W., Hayden, F. G., and Gao, G. F. (2020). A novel coronavirus outbreak of global health concern. The Lancet, 395(10223):470-473.

Xu, J., Pan, Y., Pan, X., Hoi, S., Yi, Z., and Xu, Z. (2021). Regnet: Self-regulated network for image classification. arXiv preprint, arXiv:2101.00590.

Xu, Z., Cao, Y., Jin, C., Shao, G., Liu, X., Zhou, J., Shi, H., and Feng, J. (2020). Gasnet: Weakly-supervised framework for covid-19 lesion segmentation. arXiv preprint, arXiv:2010.09456.

Yun, S., Han, D., Chun, S., Oh, S. J., Yoo, Y., and Choe, J. (2019). CutMix: Regularization strategy to train strong classifiers with localizable features. In 2019 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE.

Zhang, K. et al. (2020). Clinically applicable AI system for accurate diagnosis, quantitative measurements, and prognosis of COVID-19 pneumonia using computed tomography. Cell, 181(6):1423-1433.e11.

Zhao, X., Zhang, P., Song, F., Fan, G., Sun, Y., Wang, Y., Tian, Z., Zhang, L., and Zhang, G. (2021). D2a u-net: Automatic segmentation of covid-19 lesions from ct slices with dilated convolution and dual attention mechanism. arXiv preprint, arXiv:2102.05210.

Zhong, Z., Zheng, L., Kang, G., Li, S., and Yang, Y. (2020). Random erasing data augmentation. Proceedings of the AAAI Conference on Artificial Intelligence, 34(07):13001-13008.

Zhou, Z., Siddiquee, M. M. R., Tajbakhsh, N., and 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, pages 3-11. Springer International Publishing.