Precise Segmentation of COVID-19 Infected Lung from CT Images Based on Adaptive First-Order Appearance Model with Morphological/Anatomical Constraints
Abstract
:1. Introduction
2. Methods
2.1. First-Order Visual Appearance Model
- E-step : estimate , , which maximize under the fixed conditional weights of Equation (5) at step .
- M-step : recalculate weights, which maximize L holding parameters and fixed.
Algorithm 1: Estimation of the proposed probabilistic model parameters |
Algorithm 2: The proposed clustering algorithm |
2.2. MGRF-Based Morphological Constraints
2.3. Joint MGRF Model and Lung Segmentation Algorithm
Algorithm 3: Lung Extraction Algorithm |
input: A test 3D CT image. output: Final 3D lung segmentation.
|
3. Evaluation Metrics
3.1. Dice Similarity Coefficient (DSC)
3.2. Overlap Coefficient
3.3. Absolute Lung Volume Difference (ALVD)
3.4. Bidirectional Hausdorff Distance (BHD)
4. Experimental Results
5. Discussion and Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Machhi, J.; Herskovitz, J.; Senan, A.M.; Dutta, D.; Nath, B.; Oleynikov, M.D.; Blomberg, W.R.; Meigs, D.D.; Hasan, M.; Patel, M.; et al. The Natural History, Pathobiology, and Clinical Manifestations of SARS-CoV-2 Infections. J. Neuroimmune Pharmacol. 2020, 15, 359–386. [Google Scholar] [CrossRef] [PubMed]
- Gupta, S.; Hayek, S.S.; Wang, W.; Chan, L.; Mathews, K.S.; Melamed, M.L.; Brenner, S.K.; Leonberg-Yoo, A.; Schenck, E.J.; Radbel, J.; et al. Factors Associated With Death in Critically Ill Patients With Coronavirus Disease 2019 in the US. JAMA Intern. Med. 2020, 180, 1436. [Google Scholar] [CrossRef]
- Vijayaraj, J. Various Segmentation Techniques for Lung Cancer Detection using CT Images: A Review. Turk. J. Comput. Math. Educ. 2021, 12, 918–928. [Google Scholar] [CrossRef]
- Silveira, M.; Nascimento, J.; Marques, J. Automatic segmentation of the lungs using robust level sets. In Proceedings of the 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Lyon, France, 22–26 August 2007. [Google Scholar] [CrossRef]
- Sun, X.; Zhang, H.; Duan, H. 3D Computerized Segmentation of Lung Volume With Computed Tomography. Acad. Radiol. 2006, 13, 670–677. [Google Scholar] [CrossRef] [PubMed]
- Leader, J.K.; Zheng, B.; Rogers, R.M.; Sciurba, F.C.; Perez, A.; Chapman, B.E.; Patel, S.; Fuhrman, C.R.; Gur, D. Automated lung segmentation in X-ray computed tomography. Acad. Radiol. 2003, 10, 1224–1236. [Google Scholar] [CrossRef]
- Hu, S.; Hoffman, E.; Reinhardt, J. Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images. IEEE Trans. Med. Imaging 2001, 20, 490–498. [Google Scholar] [CrossRef]
- Brown, M.S.; Goldin, J.G.; McNitt-Gray, M.F.; Greaser, L.E.; Sapra, A.; Li, K.T.; Sayre, J.W.; Martin, K.; Aberle, D.R. Knowledge-based segmentation of thoracic computed tomography images for assessment of split lung function. Med. Phys. 2000, 27, 592–598. [Google Scholar] [CrossRef]
- Brown, M.; McNitt-Gray, M.; Mankovich, N.; Goldin, J.; Hiller, J.; Wilson, L.; Aberie, D. Method for segmenting chest CT image data using an anatomical model: Preliminary results. IEEE Trans. Med. Imaging 1997, 16, 828–839. [Google Scholar] [CrossRef]
- Van Rikxoort, E.M.; de Hoop, B.; Viergever, M.A.; Prokop, M.; van Ginneken, B. Automatic lung segmentation from thoracic computed tomography scans using a hybrid approach with error detection. Med. Phys. 2009, 36, 2934–2947. [Google Scholar] [CrossRef]
- Tan, J.H.; Acharya, U.R. Active spline model: A shape based model—Interactive segmentation. Digit. Signal Process. 2014, 35, 64–74. [Google Scholar] [CrossRef] [Green Version]
- Gill, G.; Toews, M.; Beichel, R.R. Robust Initialization of Active Shape Models for Lung Segmentation in CT Scans: A Feature-Based Atlas Approach. Int. J. Biomed. Imaging 2014, 2014, 1–7. [Google Scholar] [CrossRef]
- Lassen, B.; van Rikxoort, E.M.; Schmidt, M.; Kerkstra, S.; van Ginneken, B.; Kuhnigk, J.M. Automatic Segmentation of the Pulmonary Lobes From Chest CT Scans Based on Fissures, Vessels, and Bronchi. IEEE Trans. Med Imaging 2013, 32, 210–222. [Google Scholar] [CrossRef]
- Birkbeck, N.; Kohlberger, T.; Zhang, J.; Sofka, M.; Kaftan, J.; Comaniciu, D.; Zhou, S.K. Lung Segmentation from CT with Severe Pathologies Using Anatomical Constraints. In Medical Image Computing and Computer-Assisted Intervention–MICCAI 2014; Springer International Publishing: Berlin/Heidelberg, Germany, 2014; pp. 804–811. [Google Scholar] [CrossRef] [Green Version]
- Oulefki, A.; Agaian, S.; Trongtirakul, T.; Laouar, A.K. Automatic COVID-19 lung infected region segmentation and measurement using CT-scans images. Pattern Recognit. 2021, 114, 107747. [Google Scholar] [CrossRef]
- Korfiatis, P.; Kalogeropoulou, C.; Karahaliou, A.; Kazantzi, A.; Skiadopoulos, S.; Costaridou, L. Texture classification-based segmentation of lung affected by interstitial pneumonia in high-resolution CT. Med. Phys. 2008, 35, 5290–5302. [Google Scholar] [CrossRef] [PubMed]
- Wang, J.; Li, F.; Li, Q. Automated segmentation of lungs with severe interstitial lung disease in CT. Med. Phys. 2009, 36, 4592–4599. [Google Scholar] [CrossRef] [Green Version]
- Dehmeshki, J.; Ye, X.; Lin, X.; Valdivieso, M.; Amin, H. Automated detection of lung nodules in CT images using shape-based genetic algorithm. Comput. Med Imaging Graph. 2007, 31, 408–417. [Google Scholar] [CrossRef] [PubMed]
- Nakagomi, K.; Shimizu, A.; Kobatake, H.; Yakami, M.; Fujimoto, K.; Togashi, K. Multi-shape graph cuts with neighbor prior constraints and its application to lung segmentation from a chest CT volume. Med. Image Anal. 2013, 17, 62–77. [Google Scholar] [CrossRef] [PubMed]
- Mansoor, A.; Bagci, U.; Xu, Z.; Foster, B.; Olivier, K.N.; Elinoff, J.M.; Suffredini, A.F.; Udupa, J.K.; Mollura, D.J. A Generic Approach to Pathological Lung Segmentation. IEEE Trans. Med. Imaging 2014, 33, 2293–2310. [Google Scholar] [CrossRef] [Green Version]
- Houssein, E.H.; Emam, M.M.; Ali, A.A. Improved manta ray foraging optimization for multi-level thresholding using COVID-19 CT images. Neural Comput. Appl. 2021. [Google Scholar] [CrossRef]
- El-Baz, A.; Beache, G.M.; Gimel’farb, G.; Suzuki, K.; Okada, K.; Elnakib, A.; Soliman, A.; Abdollahi, B. Computer-Aided Diagnosis Systems for Lung Cancer: Challenges and Methodologies. Int. J. Biomed. Imaging 2013, 2013, 942353. [Google Scholar] [CrossRef] [Green Version]
- Saood, A.; Hatem, I. COVID-19 lung CT image segmentation using deep learning methods: U-Net versus SegNet. BMC Med. Imaging 2021, 21. [Google Scholar] [CrossRef]
- Badrinarayanan, V.; Kendall, A.; Cipolla, R. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 2017, 39, 2481–2495. [Google Scholar] [CrossRef]
- Ronneberger, O.; Fischer, P.; Brox, T. U-Net: Convolutional Networks for Biomedical Image Segmentation. In Lecture Notes in Computer Science; Springer International Publishing: Berlin/Heidelberg, Germany, 2015; pp. 234–241. [Google Scholar] [CrossRef] [Green Version]
- Yan, Q.; Wang, B.; Gong, D.; Luo, C.; Zhao, W.; Shen, J.; Shi, Q.; Jin, S.; Zhang, L.; You, Z. COVID-19 Chest CT Image Segmentation—A Deep Convolutional Neural Network Solution. arXiv 2020, arXiv:2004.10987. [Google Scholar]
- Amyar, A.; Modzelewski, R.; Li, H.; Ruan, S. Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation. Comput. Biol. Med. 2020, 126, 104037. [Google Scholar] [CrossRef]
- Fan, D.P.; Zhou, T.; Ji, G.P.; Zhou, Y.; Chen, G.; Fu, H.; Shen, J.; Shao, L. Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images. IEEE Trans. Med Imaging 2020, 39, 2626–2637. [Google Scholar] [CrossRef]
- Chen, X.; Yao, L.; Zhang, Y. Residual Attention U-Net for Automated Multi-Class Segmentation of COVID-19 Chest CT Images. arXiv 2020, arXiv:2004.05645. [Google Scholar]
- Abdel-Basset, M.; Chang, V.; Hawash, H.; Chakrabortty, R.K.; Ryan, M. FSS-2019-nCov: A deep learning architecture for semi-supervised few-shot segmentation of COVID-19 infection. Knowl. Based Syst. 2021, 212, 106647. [Google Scholar] [CrossRef] [PubMed]
- Gao, S.H.; Cheng, M.M.; Zhao, K.; Zhang, X.Y.; Yang, M.H.; Torr, P. Res2Net: A New Multi-Scale Backbone Architecture. IEEE Trans. Pattern Anal. Mach. Intell. 2021, 43, 652–662. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Zhao, C.; Xu, Y.; He, Z.; Tang, J.; Zhang, Y.; Han, J.; Shi, Y.; Zhou, W. Lung segmentation and automatic detection of COVID-19 using radiomic features from chest CT images. Pattern Recognit. 2021, 119, 108071. [Google Scholar] [CrossRef]
- Milletari, F.; Navab, N.; Ahmadi, S.A. V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. In Proceedings of the 2016 Fourth International Conference on 3D Vision (3DV), Stanford, CA, USA, 25–28 October 2016. [Google Scholar] [CrossRef] [Green Version]
- Li, L.; Qin, L.; Xu, Z.; Yin, Y.; Wang, X.; Kong, B.; Bai, J.; Lu, Y.; Fang, Z.; Song, Q.; et al. Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy. Radiology 2020, 296, E65–E71. [Google Scholar] [CrossRef]
- He, K.; Zhang, X.; Ren, S.; Sun, J. Deep Residual Learning for Image Recognition. In Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 27–30 June 2016. [Google Scholar] [CrossRef] [Green Version]
- Singh, V.K.; Abdel-Nasser, M.; Pandey, N.; Puig, D. LungINFseg: Segmenting COVID-19 Infected Regions in Lung CT Images Based on a Receptive-Field-Aware Deep Learning Framework. Diagnostics 2021, 11, 158. [Google Scholar] [CrossRef] [PubMed]
- Shan, F.; Gao, Y.; Wang, J.; Shi, W.; Shi, N.; Han, M.; Xue, Z.; Shen, D.; Shi, Y. Abnormal lung quantification in chest CT images of COVID-19 patients with deep learning and its application to severity prediction. Med. Phys. 2021, 48, 1633–1645. [Google Scholar] [CrossRef] [PubMed]
- Gerard, S.E.; Herrmann, J.; Xin, Y.; Martin, K.T.; Rezoagli, E.; Ippolito, D.; Bellani, G.; Cereda, M.; Guo, J.; Hoffman, E.A.; et al. CT image segmentation for inflamed and fibrotic lungs using a multi-resolution convolutional neural network. Sci. Rep. 2021, 11. [Google Scholar] [CrossRef]
- Pan, F.; Li, L.; Liu, B.; Ye, T.; Li, L.; Liu, D.; Ding, Z.; Chen, G.; Liang, B.; Yang, L.; et al. A novel deep learning-based quantification of serial chest computed tomography in Coronavirus Disease 2019 (COVID-19). Sci. Rep. 2021, 11, 1–11. [Google Scholar] [CrossRef]
- Ma, J.; Wang, Y.; An, X.; Ge, C.; Yu, Z.; Chen, J.; Zhu, Q.; Dong, G.; He, J.; He, Z.; et al. Toward data-efficient learning: A benchmark for COVID-19 CT lung and infection segmentation. Med. Phys. 2021, 48, 1197–1210. [Google Scholar] [CrossRef]
- Elharrouss, O.; Subramanian, N.; Al-Maadeed, S. An encoder-decoder-based method for COVID-19 lung infection segmentation. arXiv 2020, arXiv:2007.00861. [Google Scholar]
- Müller, D.; Rey, I.S.; Kramer, F. Automated Chest CT Image Segmentation of COVID-19 Lung Infection based on 3D U-Net. arXiv 2020, arXiv:2007.04774. [Google Scholar]
- Tilborghs, S.; Dirks, I.; Fidon, L.; Willems, S.; Eelbode, T.; Bertels, J.; Ilsen, B.; Brys, A.; Dubbeldam, A.; Buls, N.; et al. Comparative study of deep learning methods for the automatic segmentation of lung, lesion and lesion type in CT scans of COVID-19 patients. arXiv 2020, arXiv:2007.15546. [Google Scholar]
- Tajbakhsh, N.; Jeyaseelan, L.; Li, Q.; Chiang, J.N.; Wu, Z.; Ding, X. Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation. Med. Image Anal. 2020, 63, 101693. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- El-Baz, A.S.; Gimel’farb, G.L.; Suri, J.S. Stochastic Modeling for Medical Image Analysis; OCLC: 1086143882; CRC Press: Boca Raton, FL, USA, 2016. [Google Scholar]
- Fuglede, B.; Topsoe, F. Jensen-Shannon divergence and Hilbert space embedding. In Proceedings of the International Symposium onInformation Theory 2004, Chicago, IL, USA, 27 June–2 July 2004. [Google Scholar] [CrossRef]
- MacKay, D.J.C. Information Theory, Inference, and Learning Algorithms; Cambridge University Press: New York, NY, USA, 2003; p. 34. [Google Scholar]
- Gerig, G.; Jomier, M.; Chakos, M. Valmet: A New Validation Tool for Assessing and Improving 3D Object Segmentation. In Medical Image Computing and Computer-Assisted Intervention–MICCAI 2001; Springer: Berlin/Heidelberg, Germany, 2001; pp. 516–523. [Google Scholar] [CrossRef] [Green Version]
- Soliman, A.; Khalifa, F.; Alansary, A.; Gimel’farb, G.; El-Baz, A. Performance Evaluation of an Automatic MGRF-Based Lung Segmentation Approach; AIP: College Park, MA, USA, 2013. [Google Scholar] [CrossRef] [Green Version]
- An, P.; Xu, S.; Harmon, S.A.; Turkbey, E.B.; Sanford, T.H.; Amalou, A.; Kassin, M.; Varble, N.; Blain, M.; Anderson, V.; et al. CT Images in COVID-19. Available online: https://wiki.cancerimagingarchive.net/display/Public/CT+Images+in+COVID-19 (accessed on 5 June 2021).
- Kasper, J.; Decker, J.; Wiesenreiter, K.; Römmele, C.; Ebigbo, A.; Braun, G.; Häckel, T.; Schwarz, F.; Wehler, M.; Messmann, H.; et al. Typical Imaging Patterns in COVID-19 Infections of the Lung on Plain Chest Radiographs to Aid Early Triage. In RöFo-Fortschritte auf dem Gebiet der RöNtgenstrahlen und der Bildgebenden Verfahren; Georg Thieme Verlag KG: New York, NY, USA, 2021. [Google Scholar] [CrossRef]
- Chen, L.C.; Zhu, Y.; Papandreou, G.; Schroff, F.; Adam, H. Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation. In Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany, 8–4 September 2018. [Google Scholar]
- Çiçek., Ö.; Abdulkadir, A.; Lienkamp, S.S.; Brox, T.; Ronneberger, O. 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation. In Medical Image Computing and Computer-Assisted Intervention–MICCAI 2016; Springer International Publishing: Berlin/Heidelberg, Germany, 2016; pp. 424–432. [Google Scholar] [CrossRef] [Green Version]
Class | Resolution | #Slices | #Patients | Total | |
---|---|---|---|---|---|
Training | Healthy/Mild | 2 | 34 | ||
Moderate | 35–397 | 20 | |||
Severe | 12 | ||||
Testing | Healthy/Mild | 4 | 32 | ||
Moderate | 17 | ||||
Severe | 11 |
DSC | Overlap | BHD | ALVD | ||
---|---|---|---|---|---|
LCG-model | Healthy/Mild | ||||
Moderate | |||||
Severe | |||||
Overall | |||||
Final System | Healthy/Mild | ||||
Moderate | |||||
Severe | |||||
Overall |
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Sharafeldeen, A.; Elsharkawy, M.; Alghamdi, N.S.; Soliman, A.; El-Baz, A. Precise Segmentation of COVID-19 Infected Lung from CT Images Based on Adaptive First-Order Appearance Model with Morphological/Anatomical Constraints. Sensors 2021, 21, 5482. https://doi.org/10.3390/s21165482
Sharafeldeen A, Elsharkawy M, Alghamdi NS, Soliman A, El-Baz A. Precise Segmentation of COVID-19 Infected Lung from CT Images Based on Adaptive First-Order Appearance Model with Morphological/Anatomical Constraints. Sensors. 2021; 21(16):5482. https://doi.org/10.3390/s21165482
Chicago/Turabian StyleSharafeldeen, Ahmed, Mohamed Elsharkawy, Norah Saleh Alghamdi, Ahmed Soliman, and Ayman El-Baz. 2021. "Precise Segmentation of COVID-19 Infected Lung from CT Images Based on Adaptive First-Order Appearance Model with Morphological/Anatomical Constraints" Sensors 21, no. 16: 5482. https://doi.org/10.3390/s21165482