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Organ segmentation from computed tomography images using the 3D convolutional neural network: a systematic review

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Abstract

Computed tomography images are scans that combine a series of X-rays with computer processing techniques to display organs in the body. Recently, 3D CNN models have become effective in tasks relating to recognition, delineation, and classification. Therefore we propose a review to summarize different 3D CNN algorithms for segmenting organs in computed tomography images. This work systematically applies a two-stage procedure for review. A thorough screening of abstracts and titles to ascertain their relevance was done. Research papers published in the academic repositories were selected, analyzed, and reviewed. Insight relating to 3D organ segmentation is provided, with content such as database usage, disadvantages, and advantages. A comparison of two accuracies was carried out with a graph depicting database categories. Important insights, limitations, observations, and future directions were elucidated. After careful investigation, we observe that the encoder-decoder network is predominant for segmentation. The encoder-decoder framework provides a seamless procedure to segment CT images. A prediction of future trends with insightful recommendations for researchers is proposed. Finally, findings suggest that CNN algorithms produce good accuracies despite their limitations.

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Acknowledgements

Special thanks to the management of Medical Image Processing Group, Department of Radiology, University of Pennsylvania, and the anonymous referees of the review for valuable remarks.

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Authors and Affiliations

  1. Medical Image Processing Group, Department of Radiology, University of Pennsylvania, Goddard Building, 3710 Hamilton Walk, 6th Floor, Philadelphia, PA, 19104, USA

    Ademola E. Ilesanmi, Drew A. Torigian & Jayaram K. Udupa

  2. National Population Commission, Abuja, Nigeria

    Taiwo Ilesanmi

  3. West China Hospital of Sichuan University, Chengdu, China

    Oluwagbenga P. Idowu

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  1. Ademola E. Ilesanmi
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Ilesanmi, A.E., Ilesanmi, T., Idowu, O.P. et al. Organ segmentation from computed tomography images using the 3D convolutional neural network: a systematic review. Int J Multimed Info Retr 11, 315–331 (2022). https://doi.org/10.1007/s13735-022-00242-9

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