Abstract
Purpose
Diagnosis of liver fibrosis is important for establishing treatment and assessing the risk of carcinogenesis. Ultrasound imaging is an excellent diagnostic method as a screening test in terms of non-invasiveness and simplicity. The purpose of this study was to automatically diagnose liver fibrosis using ultrasound images to reduce the burden on physicians.
Methods
We proposed and implemented a system for extracting regions of liver parenchyma utilizing U-Net. Using regions of interest, the stage of fibrosis was classified as F0, F1, F2, F3, or F4 utilizing CORALNet, an ordinal regression model based on ResNet18. The effectiveness of the proposed system was verified.
Results
The system implemented using U-Net had a maximum mean Dice coefficient of 0.929. The results of classification of liver fibrosis utilizing CORALNet had a mean absolute error (MAE) of 1.22 and root mean square error (RMSE) of 1.60. The per-case results had a MAE of 1.55 and RMSE of 1.34.
Conclusion
U-Net extracted regions of liver parenchyma from the images with high accuracy, and CORALNet showed effectiveness using ordinal information to classify fibrosis in the images. As a future task, we will study a model that is less dependent on teaching data.
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Acknowledgements
The authors gratefully acknowledge the financial support by the Japan Society for the Promotion of Science, KAKENHI Grants 20H02113 and 18H03548, and the Saitama Prefecture New Technology and Product Development Subsidy Project.
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Approval was obtained from the ethics committee of Nihon University Itabashi Hospital (RK-200908–4). The procedures used in this study adhere to the tenets of the Declaration of Helsinki.
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Informed consent was obtained from all individual participants included in the study.
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Saito, R., Koizumi, N., Nishiyama, Y. et al. Evaluation of ultrasonic fibrosis diagnostic system using convolutional network for ordinal regression. Int J CARS 16, 1969–1975 (2021). https://doi.org/10.1007/s11548-021-02491-1
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DOI: https://doi.org/10.1007/s11548-021-02491-1