Zusammenfassung
Chronic obstructive pulmonary disease (COPD) is a lung disease that is not fully reversible and one of the leading causes of morbidity and mortality in the world. Early detection and diagnosis of COPD can increase the survival rate and reduce the risk of COPD progression in patients. Currently, the primary examination tool to diagnose COPD is spirometry. However, computed tomography (CT) is used for detecting symptoms and sub-type classification of COPD. Using different imaging modalities is a diffcult and tedious task even for physicians and is subjective to inter-and intra-observer variations. Hence, developing methods that can automatically classify COPD versus healthy patients is of great interest. In this paper, we propose a 3D deep learning approach to classify COPD and emphysema using volume-wise annotations only. We also demonstrate the impact of transfer learning on the classification of emphysema using knowledge transfer from a pre-trained COPD classification model.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Literatur
Organization WH. Fact Sheet on Chronic Obstructive Pulmonary Disease (COPD);. http://www.who.int/en/news-room/fact-sheets.
Mathers CD, Loncar D. Projections of Global Mortality and Burden of Disease from 2002 to 2030. PLOS Medicine. 2006 11;3(11):1–20.
Bellamy D, Smith J. Role of primary care in early diagnosis and effective management of COPD. International Journal of Clinical Practice. 2007;61(8):1380–1389.
Hatt C, Galban C, Labaki W, et al. Convolutional neural network based COPD and emphysema classifications are predictive of lung cancer diagnosis. In: Lecture Notes in Computer Science; 2018. .
Gonzalez G, Ash SY, Vegas-Sanchez-Ferrero G, et al.. Disease staging and prognosis in smokers using deep learning in chest computed tomography; 2018.
Karabulut EM, Ibrikci T. Emphysema discrimination from raw HRCT images by convolutional neural networks. In: ELECO; 2015. p. 705–708.
Ying J, Dutta J, Guo N, et al. Gold classification of COPDGene cohort based on deep learning. In: ICASSP; 2016. p. 2474–2478.
Chen H, Dou Q, Yu L, et al. VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images. NeuroImage. 2018;170:446–455.
MeVis Medical Solutions AG. MeVisLab;. https://www.mevislab.de/.
Huang X, Shan J, Vaidya V. Lung nodule detection in CT using 3D convolutional neural networks. In: 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017); 2017. p. 379–383.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
About this paper
Cite this paper
Ahmed, J. et al. (2020). COPD Classification in CT Images Using a 3D Convolutional Neural Network. In: Tolxdorff, T., Deserno, T., Handels, H., Maier, A., Maier-Hein, K., Palm, C. (eds) Bildverarbeitung für die Medizin 2020. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-29267-6_8
Download citation
DOI: https://doi.org/10.1007/978-3-658-29267-6_8
Published:
Publisher Name: Springer Vieweg, Wiesbaden
Print ISBN: 978-3-658-29266-9
Online ISBN: 978-3-658-29267-6
eBook Packages: Computer Science and Engineering (German Language)