Special Convolutional Neural Network for Identification and Positioning of Interstitial Lung Disease Patterns in Computed Tomography Images

M. Anthimopoulos, S. Christodoulidis, L. Ebner, T. Geiser, A. Christe, and S. Mougiakakou, “Semantic segmentation of pathological lung tissue with dilated fully convolutional networks,” IEEE J. Biomed. Health Inf. 23, 714–722 (2018). https://doi.org/10.1109/JBHI.2018.2818620 M. Anthimopoulos, S. Christodoulidis, L. Ebner, A. Christe, and S. Mougiakakou, “Lung pattern classification for interstitial lung diseases using a deep convolutional neural network,” IEEE Trans. Med. Imaging 35, 1207–1216 (2016). https://doi.org/10.1109/TMI.2016.2535865 U. Bagci, J. Yao, A. Wu, J. Caban, T. N. Palmore, A. F. Suffredini, O. Aras, and D. J. Mollura, “Automatic detection and quantification of tree-in-bud (TIB) opacities from CT scans,” IEEE Trans. Biomed. Eng. 59, 1620–1632 (2012). https://doi.org/10.1109/TBME.2012.2190984 G. Chassagnon, M. Vakalopoulou, E. Battistella, S. Christodoulidis, T. N. Hoang-Thi, S. Dangeard, E. Deutsch, et al., “AI-driven quantification, staging, and outcome prediction of COVID-19 pneumonia,” Med. Image Anal. 67, 101860 (2021). https://doi.org/10.1016/j.media.2020.101860 A. Depeursinge, D. V. Ville, A. Platon, A. Geissbuhler, P. A. Poletti, and H. Muller, “Near-affine-invariant texture learning for lung tissue analysis using isotropic wavelet frames,” IEEE Trans. Inf. Technol. Biomed. 16, 665–675 (2012). https://doi.org/10.1109/TITB.2012.2198829 S. Christian, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proc. IEEE Conf. on Computer Vision and Pattern Recognition, Boston, Mass., 2015 (IEEE, 2015). https://doi.org/10.1109/CVPR.2015.7298594 A. Depeursinge, A. Foncubierta-Rodriguez, D. V. Ville, and H. Müller, “Lung texture classification using locally-oriented Riesz components,” in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2011, Ed. by G. Fichtinger, A. Martel, and T. Peters, Lecture Notes in Computer Sciences, vol. 6893 (Springer, Berlin, 2011), pp. 231–238. https://doi.org/10.1007/978-3-642-23626-6_29 A. Depeursinge, A. Vargas, A. Platon, A. Geissbuhler, P. A. Poletti, and H. Müller, “Building a reference multimedia database for interstitial lung diseases,” Comput. Med. Imaging Graphics 36, 227–238 (2012). https://doi.org/10.1016/j.compmedimag.2011.07.003 A. El-Baz, A. Soliman, P. McClure, G. Gimel’farb, M. A. El-Ghar, and R. Falk, “Early assessment of malignant lung nodules based on the spatial analysisof detected lung nodules,” in 9th IEEE Int. Symp. on Biomedical Imaging, Barcelona, 2012 (IEEE, 2012), pp. 1463–1466. https://doi.org/10.1109/ISBI.2012.6235847 M. J. Gangeh, L. Sørensen, S. B. Shaker, M. S. Kamel, M. D. Bruijne, and M. Loog, “A texton-based approach for the classification of lung parenchyma in CT images,” in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010, Ed. by T. Jiang, N. Navab, J. P. W. Pluim, and M. A. Viergever, Lecture Notes in Computer Sciences, vol. 6363 (Springer, Berlin, 2010), pp. 595–602. https://doi.org/10.1007/978-3-642-15711-0_74 M. Gao, U. Bagci, L. Lu, A. Wu, M. Buty, H. C. Shin, H. Roth, G. Z. Papadakis, A. Depeursinge, R. M. Summers, Z. Xu, and D. J. Mollura, “Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks,” Comput. Methods Biomech. Biomed. Eng.: Imaging Visualization 6, 1–6 (2016). https://doi.org/10.1080/21681163.2015.1124249 C. Goutte, and E. Gaussier, “A probabilistic interpretation of precision, recall and F-score, with implication for evaluation,” in Advances in Information Retrieval, Ed. by D. E. Losada and J. M. Fernández-Luna, Lecture Notes in Computer Sciences, vol. 3408 (Springer, Berlin, 2005), pp. 345–359. https://doi.org/10.1007/978-3-540-31865-1_25 K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 2016 (IEEE, 2016), pp. 770–778. S. Huang, F. Lee, R. Miao, Q. Si, C. Lu, and Q. Chen, “A deep convolutional neural network architecture for interstitial lung disease pattern classification,” Med. Biol. Eng. Comput. 58, 725–737 (2020). https://doi.org/10.1007/s11517-019-02111-w C. Jacobs, C. I. Sánchez, S. C. Saur, T. Twellmann, P. A. de Jong, and B. van Ginneken, “Computer-aided detection of ground glass nodules in thoracic CT images using shape, intensity and context features,” in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2011, Ed. by G. Fichtinger, A. Martel, and T. Peters, Lecture Notes in Computer Sciences, vol. 6893 (Springer, Berlin, 2011), pp. 207–214. https://doi.org/10.1007/978-3-642-23626-6_26 B. G. Jankharia and B. A. Angirish, “Computer-aided quantitative analysis in interstitial lung diseases–A pictorial review using CALIPER,” Lung India 38, 161–167 (2021). https://doi.org/10.4103/lungindia.lungindia_244_20 D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization.” arXiv:1412.6980 [cs.LG] P. D. Korfiatis, A. N. Karahaliou, A. D. Kazantzi, C. Kalogeropoulou, and L. I. Costaridou, “Texture-based identification and characterization of interstitial pneumonia patterns in lung multidetector CT,” IEEE Trans. Inf. Technol. Biomed. 14, 675–680 (2010). https://doi.org/10.1109/TITB.2009.2036166 G. M. Mallow, Z. K. Siyaji, F. Galbusera, A. A. Espinoza-Orías, M. Giers, H. Lundberg, C. Ames, J. Karppinen, P. K. Louie, F. M. Phillips, R. Pourzal, J. Schwab, D. M. Sciubba, J. C. Wang, H.-J. Wilke, F. M. K. Williams, S. A. Mohiuddin, M. C. Makhni, N. A. Shepard, H. S. An, and D. Samartzis, “Intelligence-based spine care model: A new era of research and clinical decision-making,” Glob. Spine J. 11, 135–145 (2021). https://doi.org/10.1177/2192568220973984 P. Marentakis, P. Karaiskos, V. Kouloulias, N. Kelekis, S. Argentos, N. Oikonomopoulos, and C. Loukas, “Lung cancer histology classification from CT images based on radiomics and deep learning models,” Med. Biol. Eng. Comput. 59, 215–226 (2021). https://doi.org/10.1007/s11517-020-02302-w S. C. Park, J. Tan, X. Wang, D. Lederman, J. K. Leader, S. H. Kim, and B. Zheng, “Computer-aided detection of early interstitial lung diseases using low-dose CT images,” Phys. Med. Biol. 56, 1139–1153 (2011). https://doi.org/10.1088/0031-9155/56/4/016 D. Bermejo-Peláez, S. Y. Ash, G. R. Washko, R. S. J. Estépar, and M. J. Ledesma-Carbayo, “Classification of interstitial lung abnormality patterns with an ensemble of deep convolutional neural networks,” Sci. Rep. 10, 338 (2020). https://doi.org/10.1038/s41598-019-56989-5 D. R. Sarvamangala and R. V. Kulkarni, “Convolutional neural networks in medical image understanding: a survey,” Evol. Intell. (2021). https://doi.org/10.1007/s12065-020-00540-3 H. C. Shin, H. R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, J. Yao, D. Mollura, and R. M. Summers, “Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning,” IEEE Trans. Med. Imaging 35, 1285–1298 (2016). https://doi.org/10.1109/TMI.2016.2528162 K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition.” arXiv:1409.1556 [cs.CV] Y. Song, W. Cai, Y. Zhou, and D. D. Feng, “Feature-based image patch approximation for lung tissue classification,” IEEE Trans. Med. Imaging 32, 797–808 (2013). https://doi.org/10.1109/TMI.2013.2241448 Y. Song, W. Cai, H. Huang, Y. Zhou, D. D. Feng, Y. Wang, M. J Fulham, and M. Chen, “Large margin local estimate with applications to medical image classification,” IEEE Trans. Med. Imaging 34, 1362–1377 (2015). https://doi.org/10.1109/TMI.2015.2393954 Y. Song, W. Cai, J. Kim, and D. D. Feng, “A multistage discriminative model for tumor and lymph node detection in thoracic images,” IEEE Trans. Med. Imaging 31, 1061–1075 (2012). https://doi.org/10.1109/TMI.2012.2185057 Y. Song, W. Cai, S. Eberl, M. J. Fulham, and D. Feng, “Discriminative pathological context detection in thoracic images based on multi-level inference,” in Int. Conf. on Medical Image Computing and Computer-Assisted Intervention – MICCAI 2011, Ed. by G. Fichtinger, A. Martel, and T. Peters, Lecture Notes in Computer Science, vol. 6893 (Springer, Berlin, 2011), pp. 191–198. https://doi.org/10.1007/978-3-642-23626-6_24 Y. Wang, L. Zhou, M. Wang, C. Shao, L. Shi, S. Yang, Z. Zhang, M. Feng, F. Shan, and L. Liu, “Combination of generative adversarial network and convolutional neural network for automatic subcentimeter pulmonary adenocarcinoma classification,” Quant. Imaging Med. Surg. 10, 1249 (2020). https://doi.org/10.21037/qims-19-982 Y. Wang, Y. Zhang, Y. Liu, J. Tian, C. Zhong, Z. Shi, Y. Zhang, and Z. He, “Does non-COVID-19 lung lesion help? investigating transferability in COVID-19 CT image segmentation,” Comput. Methods Programs Biomed. 202, 106004 (2021). https://doi.org/10.1016/j.cmpb.2021.106004 W. R. Webb, N. L. Muller, and D. P. Naidich, High-Resolution CT of the Lung, (Lippincott Williams & Wilkins, 2014). J. Wright, Y. Ma, J. Mairal, G. Sapiro, T.S. Huang, and S. Yan, “Sparse representation for computer vision and pattern recognition,” Proc. IEEE 98, 1031–1044 (2010). https://doi.org/10.1109/JPROC.2010.2044470 R. Xu, Y. Hirano, R. Tachibana, and S. Kido, “Classification of diffuse lung disease patterns on high-resolution computed tomography by a bag of words approach,” Int. Conf. on Medical Image Computing and Computer-Assisted Intervention – MICCAI 2011, Ed. by G. Fichtinger, A. Martel, and T. Peters, Lecture Notes in Computer Science, vol. 6893 (Springer, Berlin, 2011), pp. 183–190. https://doi.org/10.1007/978-3-642-23626-6_23 J. Yao, A. Dwyer, R. M. Summers, and D. J. Mollura, “Computer aided diagnosis of pulmonary infections using texture analysis and support vector machine classification,” Acad. Radiol. 18, 306–314 (2011). https://doi.org/10.1016/j.acra.2010.11.013