Bhuvan Mittal
ABSTRACT
The Covid-19 pandemic has caused more then 193 million cases and 4.1 million deaths worldwide as of July 2021. The Fleischner Society reported that Computerized Tomography (CT) is a useful tool for the early identification of Covid-19. Covid-19 disease induces lung changes which can be observed in lung CT predominantly as ground-glass opacification (GGO) and occasional consolidation in the peripheries. Moreover, it was reported that the percentage of lung showing disease correlates with the severity of the disease. Therefore, segmentation of the disease areas in CT images is a logical first step to quantify disease severity. In this paper, we propose ‘CoviSegNet Enhanced’ based on a U-Net with an 813-layer EfficientNetB7 encoder having an attention mechanism to segment the Covid-19 disease area observed in CT images of Covid-19 patients. CoviSegNet Enhanced is an improvement of our previous work ‘CoviSegNet’. The experiments performed on three public CT datasets and a detailed comparison with recently published work confirms that the proposed CoviNet Enhanced using deep learning approaches is highly effective for Covid-19 segmentation.
- World Health Organization, “Weekly epidemiological update on Covid-19 – 25 July 2021,” Jul. 2021.Google Scholar
- Rubin GD, Ryerson CJ, Haramati LB, Sverzellati N, Kanne JP, Raoof S, The Role of Chest Imaging in Patient Management during the COVID-19 Pandemic: A Multinational Consensus Statement from the Fleischner Society. Radiology 2020 Jul;296 (1):172-180.Google Scholar
- The Radiology Assistant: An educational site of the Radiological Society of the Netherlands, https://radiologyassistant.nl/chest/covid-19/covid19-imaging-findingsGoogle Scholar
- Cai, W., Liu, T., Xue, X., Luo, G., Wang, X., Shen, Y., Fang, Q., Sheng, J., Chen, F., & Liang, T. (2020). CT Quantification and Machine-learning Models for Assessment of Disease Severity and Prognosis of Covid-19 Patients. Academic Radiology, 27(12), 1665–1678.Google ScholarCross Ref
- Mittal B., Oh J., (2021). CoviSegNet - Covid-19 Disease Area Segmentation using Machine Learning Analyses for Lung Imaging, Twelfth International Symposium on Image and Signal Processing and Analysis (ISPA 2021), 13-15 September 2021, Zagreb, Croatia.Google ScholarCross Ref
- Huang, G., Liu, Z., van der Maaten, L., & Weinberger, K. Q. (2017). Densely Connected Convolutional Networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Google Scholar
- Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. ArXiv:1505.04597.Google Scholar
- Tan, M., & Le, Q. (2019). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. International Conference on Machine Learning, 6105–6114.Google Scholar
- Dozat, T. (2016). “Incorporating Nesterov Momentum into Adam,” 6th International Conference on Learning Representations (ICLR 2016).Google Scholar
- 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.Google ScholarDigital Library
- Saeedizadeh, N., Minaee, S., Kafieh, R., Yazdani, S., & Sonka, M. (2020). Covid TV-UNet: Segmenting Covid-19 Chest CT Images Using Connectivity Imposed U-Net. ArXiv:2007.12303.Google Scholar
- Goncharov, M., Pisov, M., Shevtsov, A., Shirokikh, B., Kurmukov, A., Blokhin, I., Chernina, V., Solovev, A., Gombolevskiy, V., Morozov, S., & Belyaev, M. (2021). CT-based Covid-19 Triage: Deep Multitask Learning Improves Joint Identification and Severity Quantification. Medical Image Analysis, 102054.Google Scholar
- Zhou, Z., Siddiquee, M. M. R., Tajbakhsh, N., & Liang, J. (2018). UNet++: A Nested U-Net Architecture for Medical Image Segmentation. 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, Held in Conjunction with MICCAI 2018, Granada, Spain, S, 11045, 3–11.Google Scholar
- Fan, D.-P., Zhou, T., Ji, G.-P., Zhou, Y., Chen, G., Fu, H., Shen, J., & Shao, L. (2020). Inf-Net: Automatic Covid-19 Lung Infection Segmentation From CT Images, IEEE Transactions on Medical Imaging, vol. 39, no. 8, pp. 2626–2637, Aug. 2020, number: 8.Google ScholarCross Ref
- Safarov, S., & Whangbo, T. K. (2021). A-DenseUNet: Adaptive Densely Connected UNet for Polyp Segmentation in Colonoscopy Images with Atrous Convolution. Sensors, 21(4), 1441.Google ScholarCross Ref
- Jha, D., Smedsrud, P.H., Riegler, M., Halvorsen, P., de Lange, T., Johansen, D., Johansen, H. (2020). Kvasir-seg: A segmented polyp dataset. In Proceedings of the International Conference on Multimedia Modeling. 2020. Available online: https://datasets.simula.no/kvasir-seg/ (accessed on 10 February 2021).Google ScholarDigital Library
- Bernal, J.; Sanchez, F.J.; Fernandez-Esparrach, G.; Gil, D.; Rodrıguez, C.; Vilarino, F. Wm-dova maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians. Comput. Med Imaging Graph. 2015, 43, 99–111.Google Scholar
- Harris, C. R., Millman, K. J., van der Walt, Stéfan J, Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., Kern, R., Picus, M., Hoyer, S., van Kerkwijk, M. H., Brett, M., Haldane, A., Del Río, J. F., Wiebe, M., Peterson, P., . . . Oliphant, T. E. (2020). Array programming with NumPy. Nature (London), 585(7825), 357-362.Google ScholarCross Ref
- Glob: A python library, https://docs.python.org/2/library/glob.htmlGoogle Scholar
- Bernico, M. (2018). Deep learning quick reference: Useful hacks for training and optimizing deep neural networks with tensorflow and keras. Chapter 1, pp. 23.Google Scholar
- MedSeg Dataset, http://medicalsegmentation.com/covid19/Google Scholar
- Morozov S.P., Andreychenko A.E., Blokhin I.A., Gelezhe P.B., Gonchar A.P., Nikolaev A.E., Pavlov N.A., Chernina V.Y., Gombolevskiy V.A., (2020). MosMedData: Data set of 1110 chest CT scans performed during the Covid-19 epidemic. Digital Diagnostics, 1(1), 49–59.Google ScholarCross Ref
- Covid-Semi-Seg dataset: Fan, D.-P., Zhou, T., Ji, G.-P., Zhou, Y., Chen, G., Fu, H., Shen, J., & Shao, L. (2020). Inf-Net: Automatic Covid-19 Lung Infection Segmentation From CT Images. IEEE Transactions on Medical Imaging, 39(8), 2626–2637.Google ScholarCross Ref
- Simonyan, Karen, and Andrew Zisserman: Very deep convolutional networks for large-scale image recognition, 2014; Available: https://arxiv.org/pdf/1409.1556.pdf.Google Scholar
Recommendations
COVID-19 Lung CT image segmentation using localization and enhancement methods with U-Net
AbstractSegmentation of pneumonia lesions from Lung CT images has become vital for diagnosing the disease and evaluating the severity of the patients during the COVID-19 pandemic. Several AI-based systems have been proposed for this task. However, some ...
Lung segmentation and automatic detection of COVID-19 using radiomic features from chest CT images
Highlights- A new deep-learning-based method which integrates a 3D V-Net with shape priors is proposed to extract pulmonary parenchyma from chest CT images. Compared ...
AbstractThis paper aims to develop an automatic method to segment pulmonary parenchyma in chest CT images and analyze texture features from the segmented pulmonary parenchyma regions to assist radiologists in COVID-19 diagnosis. A new ...
Comments