Abstract
Medical images are an essential input for the timely diagnosis of pathologies. Despite its wide use in the area, searching for images that can reveal valuable information to support decision-making is difficult and expensive. However, the possibilities that open when making large repositories of images available for search by content are unsuspected. We designed a content-based image retrieval system for medical imaging, which reduces the gap between access to information and the availability of useful repositories to meet these needs. The system operates on the principle of query-by-example, in which users provide medical images, and the system displays a set of related images. Unlike metadata match-driven searches, our system drives content-based search. This allows the system to conduct searches on repositories of medical images that do not necessarily have complete and curated metadata. We explore our system’s feasibility in computational tomography (CT) slices for SARS-CoV-2 infection (COVID-19), showing that our proposal obtains promising results, advantageously comparing it with other search methods.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Ahmad, H., Khan, M., Yousaf, A., Ghuffar, S., Khurshid, K.: Deep learning: a breakthrough in medical imaging. Curr. Med. Imaging 16(8), 946–956 (2020)
Anavi, Y., Kogan, I., Gelbart, E., Geva, O., Greenspan, H.: Visualizing and enhancing a deep learning framework using patients age and gender for chest X-ray image retrieval. In: Proceedings of the SPIE on Medical Imaging, vol. 9785, p. 978510 (2016)
Baur, C., Albarqouni, A., Navab, N.: Semi-supervised deep learning for fully convolutional networks. In: International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI, pp. 311–319 (2017)
Camalan, S., et al.: OtoMatch: content-based eardrum image retrieval using deep learning. PLoS ONE 15(5), art. no. e0232776 (2020)
Chollet, F.: Xception: Deep Learning with Depthwise Separable Convolutions. CVPR, pp. 1800–1807 (2017)
Gu, Z., et al.: Ce-Net: context encoder network for 2D medical image segmentation. IEEE Trans. Med. Imaging 38(10), 2281–2292 (2019)
Hamidinekoo, A., Denton, E., Honnor, K., Zwiggelaar, R.: An AI-based method to retrieve hematoxylin and eosin breast histology images using mammograms. In: Proceedings of SPIE - The International Society for Optical Engineering, vol. 11513, art. no. 1151319 (2020)
Haq, N., Moradi, M., Wang, Z.: A deep community based approach for large scale content based X-ray image retrieval. Med. Image Anal. 68, art. no. 101847 (2021)
Hyvonen, V.: Fast nearest neighbor search through sparse random projections and voting. BigData, pp. 881–888 (2016)
Lin, M., Chen, Q., Yan, S.: Network in Network. ICLR (Poster) (2014)
Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)
Liu, X., Tizhoosh, H., Kofman, J.: Generating binary tags for fast medical image retrieval based on convolutional nets and Radon transform. In: Proceedings of the International Joint Conference on Neural Networks (2016)
Muller, H., Unay, D.: Retrieval from and understanding of large-scale multi-modal medical datasets: a review. IEEE Trans. Multimedia 19(9), art. no. 7984864, 2093–2104 (2017)
Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Shah, A., Conjeti, S., Navab, N., Katouzian, A.: Deeply learnt hashing forests for content based image retrieval in prostate MR images. In: Proceedings of the SPIE on Medical Imaging, vol. 9784, p. 978414 (2016)
Swati, Z., et al.: Content-based brain tumor retrieval for MR images using transfer learning. IEEE Access 7, art. no. 8611216, 17809–17822 (2019)
Tong, N., Gou, S., Yang, S., Ruan, D., Sheng, K.: Fully automatic multi-organ segmentation for head and neck cancer radiotherapy using shape representation model constrained fully convolutional neural networks. Med. Phys. 45(10), 4558–4567 (2018)
Yu, Y., Li, M., Liu, L., Li, Y., Wang, J.: Clinical big data and deep learning: applications, challenges, and future outlooks. Big Data Mining Anal. 2(4), art. no. 8787233 288–305 (2019)
Acknowledgments
This work was funded by ANID FONDEF grant 19I10023, ANID FONDECYT grant 11170475, ANID Basal Project FB0008, and ANID PIA/APOYO AFB180002. Dr. Mendoza acknowledges support from ANID Fondecyt grant 1200211.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Molina, G. et al. (2022). A New Content-Based Image Retrieval System for SARS-CoV-2 Computer-Aided Diagnosis. In: Su, R., Zhang, YD., Liu, H. (eds) Proceedings of 2021 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2021). MICAD 2021. Lecture Notes in Electrical Engineering, vol 784. Springer, Singapore. https://doi.org/10.1007/978-981-16-3880-0_33
Download citation
DOI: https://doi.org/10.1007/978-981-16-3880-0_33
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-3879-4
Online ISBN: 978-981-16-3880-0
eBook Packages: EngineeringEngineering (R0)