Abstract
Rheumatoid arthritis (RA) is an autoimmune disorder that causes pain, swelling and stiffness in joints. Nowadays, ultrasound (US) has undergone an increasing role in RA screening since it is a powerful tool to assess disease activity. However, obtaining a good quality US frame is a tricky operator dependent procedure. For this reason, the purpose of this paper is to present a strategy to the automatic selection of informative US rheumatology images by means of Convolutional Neural Networks (CNNs). The proposed method is based on VGG16 and Inception V3 CNNs, which are fine tuned to classify 214 balanced metacarpal head US images (75% used for training and 25% used for testing). A repeated 3 fold cross validation for each CNN was performed. The best results were achieved with VGG16 (area under the curve = 90%). These results support the possibility of applying this method in the actual clinical practice for supporting the diagnostic process and helping young residents’ training.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Filippucci, E., et al.: Ultrasound imaging in rheumatoid arthritis. La Radiologia Medica, pp. 1–14 (2019)
Filippucci, E., et al.: Ultrasound imaging for the rheumatologist (2006)
Behrens, A.: Creating panoramic images for bladder fluorescence endoscopy. Acta Polytech. 48(3), 50–54 (2008)
Tajbakhsh, N., et al.: Automatic assessment of image informativeness in colonoscopy. In: Yoshida, H., Näppi, J., Saini, S. (eds.) International MICCAI Workshop on Computational and Clinical Challenges in Abdominal Imaging, vol. 8676, pp. 151–158. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-13692-9_14
Bashar, M.K., et al.: Automatic detection of informative frames from wireless capsule endoscopy images. Med. Image Anal. 14(3), 449–470 (2010)
Moccia, S., et al.: Learning-based classification of informative laryngoscopic frames. Comput. Methods Programs Biomed. 158, 21–30 (2018)
Nasr-Esfahani, E., et al.: Melanoma detection by analysis of clinical images using convolutional neural network. In: International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1373–1376. IEEE (2016)
Akbari, M., et al.: Classification of informative frames in colonoscopy videos using convolutional neural networks with binarized weights. In: International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 65–68. IEEE (2018)
Tajbakhsh, N., et al.: Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans. Med. Imaging 35(5), 1299–1312 (2016)
Mandt, S., et al.: Stochastic gradient descent as approximate bayesian inference. J. Mach. Learn. Res. 18(1), 4873–4907 (2017)
Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Ethics declarations
Conflict of interest
No conflict of interest to declare.
Ethical approval
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.
Informed consent
Informed consent was obtained from all individual participants included in the study.
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Fiorentino, M.C., Moccia, S., Cipolletta, E., Filippucci, E., Frontoni, E. (2019). A Learning Approach for Informative-Frame Selection in US Rheumatology Images. In: Cristani, M., Prati, A., Lanz, O., Messelodi, S., Sebe, N. (eds) New Trends in Image Analysis and Processing – ICIAP 2019. ICIAP 2019. Lecture Notes in Computer Science(), vol 11808. Springer, Cham. https://doi.org/10.1007/978-3-030-30754-7_23
Download citation
DOI: https://doi.org/10.1007/978-3-030-30754-7_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-30753-0
Online ISBN: 978-3-030-30754-7
eBook Packages: Computer ScienceComputer Science (R0)
