Abstract
This study addresses the automated recognition of the axial computed tomography (CT) slice content in terms of a predefined region of the body for the computer-aided diagnosis purposes. A 23-layer convolutional neural network was designed, trained and tested for the axial CT slice classification. The system was validated over 120 CT studies from publicly available databases containing 21 704 images in two experiments with different definitions of classes. The classification accuracy reached 93.6% and 97.0% for the database partitions into 9 and 5 classes, respectively.
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References
American Association of Physicists in Medicine. Size-specific dose estimates (SSDE) in pediatric and adult body CT examinations. AAPM Report No. 204. https://www.aapm.org/pubs/reports/RPT_204.pdf (2011)
Armato, S.G., McLennan, G., Bidaut, L., McNitt-Gray, M.F., Meyer, C.R., Reeves, A.P., et al.: The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans. Med. Phys. 38(2), 915–931 (2011)
Armato, S.G., McLennan, G., Bidaut, L., McNitt-Gray, M.F., Meyer, C.R., Reeves, A.P., et al.: Data from LIDC-IDRI. The Cancer Imaging Archive (2015). https://doi.org/10.7937/K9/TCIA.2015.LO9QL9SX
Bosch, W.R., Straube, W.L., Matthews, J.W., Purdy, J.A.: Data from Head-Neck\_Cetuximab. The Cancer Imaging Archive (2015). https://doi.org/10.7937/K9/TCIA.2015.7AKGJUPZ
Gao, X.W., Hui, R., Tian, Z.: Classification of CT brain images based on deep learning networks. Comput. Methods Programs Biomed. 138, 49–56 (2017)
IRCAD France. 3Dircadb. 3D image reconstruction for comparison of algorithm database. http://www.ircad.fr/research/3d-ircadb-01/. Accessed 20 May 2019
Kawa, J., Juszczyk, J., Pyciński, B., Badura, P., Piętka, E.: Radiological atlas for patient specific model generation. In: Information Technologies in Biomedicine, vol. 4, pp. 69–82 . Advances in Intelligent Systems and Computing, vol. 284 (2014)
Litjens, G., Kooi, T., Bejnordi, B.E., Adiyoso Setio, A.A., Ciompi, F., Ghafoorian, M., van der Laak, J., van Ginneken, B., Sanchez, C.I.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)
Masood, S., Sharif, M., Masood, A., Yasmin, M., Raza, M.: A survey on medical image segmentation. Curr. Med. Imaging Rev. 11(1), 3–14 (2015)
Piętka, E., Kawa, J., Badura, P., Spinczyk, D.: Open architecture computer-aided diagnosis system. Expert Syst. 27(1), 17–39 (2010)
Shin, H.-C., Roth, H.R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., Summers, R.M.: Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans. Med. Imaging 35(5), 1285–1298 (2016)
Acknowledgements
The work was carried out as part of the research project ”Service platform for monitoring doses of ionizing radiation for medical exposures used for diagnostic purposes” financed by the National Centre for Research and Development (NCBR), project number: POIR.01.01.01-00-1319/17. This research was partially supported by the Polish Ministry of Science and Silesian University of Technology statutory financial support number BK-241/RIB-1/2019.
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Badura, P., Juszczyk, J., Bożek, P., Smoliński, M. (2021). Automated Classification of Axial CT Slices Using Convolutional Neural Network. In: Gzik, M., Paszenda, Z., Pietka, E., Tkacz, E., Milewski, K. (eds) Innovations in Biomedical Engineering. AAB 2020. Advances in Intelligent Systems and Computing, vol 1223. Springer, Cham. https://doi.org/10.1007/978-3-030-52180-6_34
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DOI: https://doi.org/10.1007/978-3-030-52180-6_34
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