Enhanced imagistic methodologies augmenting radiological image processing in interstitial lung diseases

József Palatka; Levente Kovács; László Szilágyi

doi:10.2478/ausi-2023-0011

Abstract

Interstitial Lung Diseases (ILDs) represent a heterogeneous group of several rare diseases that are di cult to predict, diagnose and monitor. There are no predictive biomarkers for ILDs, clinical signs are similar to the ones for other lung diseases, the radiological features are not easy to recognize, and require manual radiologist review. Data-driven support for ILD prediction, diagnosis and disease-course monitoring are great unmet need. Numerous image processing techniques and computer-aided diagnostic and decision-making support methods have been developed over the recent years. The current review focuses on such solutions, discussing advancements on the fields of Quantitative CT, Complex Networks, and Convolutional Neural Networks.

References

S. Agarwala, A. Kumar, A.K. Dhara, S.B. Thakur, A. Sadhu, D. Nandi, Special Convolutional Neural Network for Identification and and Positioning of Interstitial Lung Disease Patterns in Computed Tomography Images, Pattern Recognition and Image Analysis 31, 4 (2021) 730-738. ⇒155, 161
Search in Google Scholar Back to article
M. Anthimopoulos, S. Christodoulidis, L. Ebner, A. Christe, S. Mougiakakou Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network, IEEE Transactions on Medical Imaging 35, 5 (2016) 1207-1216. ⇒155, 157
Search in Google Scholar Back to article
M. Anthimopoulos, S. Christodoulidis, L. Ebner, T. Geiser, A. Christe, S. Mougiakakou, Semantic Segmentation of Pathological Lung Tissue with Dilated Fully Convolutional Networks, IEEE Journal of Biomedical and Health Informatics 23, 2 (2018) 714-722. ⇒155, 161
Search in Google Scholar Back to article
S.Y. Ash, R. Harmouche, D.L. Lopez Vallejo, J.A. Villalba, K. Ostridge, R. Gunville, C.E. Come, J.O. Onieva, J.C. Ross, G.M. Hunninghake, S.Y. El-Chemaly, T.J. Doyle, P. Nardelli, G.V. Sanchez-Ferrero, H.J. Goldberg, I.O. Rosas, R. San Jose Estepar, G.R. Washko, Densitometric and local histogram based analysis of computed tomography images in patients with idiopathic pulmonary fibrosis, Respiratory Research 18 (2017) 45. ⇒152
Search in Google Scholar Back to article
S. Bakas, M. Reyes, A. Jakab, S. Bauer, M. Rempfler, A. Crimi, M. Prastawa, et al., Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge, arXiv (2019) 1181.02629v2. ⇒164
Search in Google Scholar Back to article
H. Barnes, S.M. Humphries, P.M. George, D. Assayag, I. Glaspole, J.A. Mackintosh, T.J. Corte, M. Glassberg, K.A. Johannson, L. Calandriello, F. Felder, A. Wells, S. Walsh, Digital Technology and the Future of Interstitial Lung Diseases, Lancet Digit Health 5 (2023) e41-50. ⇒149
Search in Google Scholar Back to article
B.J. Bartholmai, S. Raghunath, R.A. Karwoski, T. Moua, S. Rajagopalan, F. Maldonado, P.A. Decker, R.A. Robb, Quantitative CT Imaging of Interstitial Lung Diseases, Journal of Thoracic Imaging 28, 5 (2013) 298-307. ⇒153
Search in Google Scholar Back to article
K. Berger, R.J. Kaner, Diagnosis and Pharmacologic Management of Fibrotic Interstitial Lung Disease, Life (Basel) 13, 3 (2023) 599. ⇒149
Search in Google Scholar Back to article
D. Bermejo-Peláez, S.Y. Ash, G.R. Washko, R. San José Estépar, M.J. Ledesma-Carbayo, Classifcation of Interstitial Lung Abnormality Patterns with an Ensemble of Deep Convolutional Neural Networks, Scientific Reports 10 (2020) 338. ⇒155, 158, 159
Search in Google Scholar Back to article
J.D. Budzikowski, J.J. Foy, A.A. Rashid, J.H. Chung, I. Noth, S.G. Armato, Radiomics-based assessment of idiopathic pulmonary fibrosis is associated with genetic mutations and patient survival, Journal of Medical Imaging 8, 5 (2021) 031903. ⇒156
Search in Google Scholar Back to article
P. Camus, https://www.pneumotox.com, The drug-induced respiratory disease website, last accessed 15 June 2023. ⇒148
Search in Google Scholar Back to article
B. Choi, S.Y. Ash, Deep Learning–based Classification of Fibrotic Lung Disease: Can Computer Vision See the Future?, American Journal of Respiratory and Critical Care Medicine 206, 7 (2022) 812-814. ⇒156
Search in Google Scholar Back to article
W.I. Choi, S. Dauti, H.J. Kim, S.H. Park, J.S. Park, C.W. Lee, Risk factors for interstitial lung disease: a 9-year Nationwide population-based study, BMC Pulmonary Medicine 18 (2018) 96. ⇒150
Search in Google Scholar Back to article
A. Christe, A.A. Peters, D. Drakopoulos, J.T. Heverhagen, T. Geiser, T. Stathopoulou, S. Christodoulidis, M. Anthimopoulos, S. Mougiakakou, G. Stavroula, L. Ebner, Computer-Aided Diagnosis of Pulmonary Fibrosis Using Deep Learning and CT Images, Investigative Radiology 54, 10 (2019) 627-632. ⇒155
Search in Google Scholar Back to article
J. Deng, W. Dong, R. Socher, L.J. Li, K. Li, L. Fei-Fei, ImageNet: A large-scale hierarchical image database, IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2009) pp. 248-255. ⇒154
Search in Google Scholar Back to article
A. Depeursinge, D. Van de Ville, A. Platon, A. Geissbuhler, P.A. Poletti, H. Müller, Near-a ne-invariant texture learning for lung tissue analysis using isotropic wavelet frames, IEEE Transactions on Information Technology in Biomedicine 16, 4 (2012) 665-675. ⇒157
Search in Google Scholar Back to article
N. Gordillo, E. Montseny, P. Sobrevilla, State of the art survey on MRI brain tumor segmentation, Magnetic Resonance Imaging 31 (2013) 1426-1438. ⇒164
Search in Google Scholar Back to article
Á. Győrfi, L. Szilágyi, L. Kovács, A fully automatic procedure for brain tumor segmentation from multi-spectral MRI records using ensemble learning and atlas-based data enhancement, Applied Sciences 11 (2021) 564. ⇒165
Search in Google Scholar Back to article
S. Huang, F.F. Lee, R. Miao, Q. Si, C. Lu, Q. Chen, A deep convolutional neural network architecture for interstitial lung disease pattern classification, Medical & Biological Engineering & Computing 58 (2020) 725-737. ⇒154, 155, 158
Search in Google Scholar Back to article
B. Kaul, V. Cottin, H.K. Collard, C. Valenzuela, Variability in Global Prevalence of Interstitial Lung Disease, Frontiers in Medicine 8 (2021) 751181. ⇒147
Search in Google Scholar Back to article
G.B. Kim, K.H. Jung, Y. Lee, H.J. Kim, N. Kim, S. Jun, J.B. Seo, D.A. Lynch, Comparison of Shallow and Deep Learning Methods on Classifying the Regional Pattern of Di use Lung Disease, Journal of Digital Imaging 31 (2018) 415-424. ⇒155
Search in Google Scholar Back to article
B.H. Menze, A. Jakab, S. Bauer, J. Kalpathy-Cramer, K. Farahani, J. Kirby, et al., The multimodal brain tumor image segmentation benchmark (BRATS), IEEE Transactions on Medical Imaging 34, 10 (2015) 1993-2024. ⇒164
Search in Google Scholar Back to article
H.J. Park, S.M. Lee, J.W. Song, S.M. Lee, S.Y. Oh, N. Kim, J.B. Seo, Texture-Based Automated Quantitative Assessment of Regional Patterns on Initial CT in Patients With Idiopathic Pulmonary Fibrosis: Relationship to Decline in Forced Vital Capacity, American Journal of Roentgenology 207, 5 (2016) 976-983. ⇒156
Search in Google Scholar Back to article
S.C. Park, J. Tan, X.W. Wang, D. Lederman, J.K. Leader, S.H. Kim, B. Zheng, Computer-aided detection of early interstitial lung diseases using low-dose CT images, Physics in Medicine & Biology 56 (2011) 1139-1153. ⇒154
Search in Google Scholar Back to article
C.A. Powell, S. Modi, H. Iwala, S. Takahashi, E.F. Smit, S. Siena, D.Y. Chang, E. Macpherson, A. Qin, J. Singh, C. Taitt, N. Shire, D. Ross Camidge, Pooled analysis of drug-related interstitial lung disease and/or pneumonitis in nine trastuzumab deruxtecan monotherapy studies, ESMO Open 7, 4 (2022) 100544. ⇒148
Search in Google Scholar Back to article
G. Raghu, M. Remy-Jardin, J.L. Myers, L. Richeldi, C.J. Ryerson, D.J. Lederer, et al., Diagnosis of idiopathic pulmonary fibrosis an o cial ATS/ERS/JRS/ALAT clinical practice guideline, American Journal of Respiratory and Critical Care Medicine 198, 5 (2018) e44-e68. ⇒147, 150
Search in Google Scholar Back to article
H.C. Shin, H.R. Roth, M.C Gao, L. Lu, Z.T. Xu, I. Nogues, J.H. Yao, D. Mollura, R.M. Summers, Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning, IEEE Transactions on Medical Imaging 35, 5 (2016) 1285-1298. ⇒158, 160
Search in Google Scholar Back to article
Y. Song, W.D. Cai, Y. Zhou, D.D. Feng, Feature-based image patch approximation for lung tissue classification, IEEE Transactions on Medical Imaging 32, 4 (2013) 797-808. ⇒157
Search in Google Scholar Back to article
C. Szegedy, W.Liu, Y.Q. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, A. Rabinovich, Going deeper with convolutions, IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015) pp. 1-9. ⇒154
Search in Google Scholar Back to article
A.A. Truşculescu, D. Manolescu, E. Tudorache, C. Oancea, Deep learning in interstitial lung disease – how long until daily practise, European Radiology 30, (2020) 6285-6292. ⇒150
Search in Google Scholar Back to article
A.A. Truşculescu, D.L. Manolescu, L. BroascȈa, V.M. Ancuşa, H. Ciocârlie, C.C. Pescaru, E. Vaştag, C.I. Oancea, Enhancing Imagistic Interstitial Lung Disease Diagnosis by Using Complex Networks, Medicina (Kaunas) 58, 9 (2022) 1288. ⇒153, 154
Search in Google Scholar Back to article
A. Wallis, K. Spinks, The diagnosis and management of interstitial lung diseases, BMJ 2015 (2015) 350:h2072. ⇒149
Search in Google Scholar Back to article

Enhanced imagistic methodologies augmenting radiological image processing in interstitial lung diseases

Abstract

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