Evaluation of ultrasonic fibrosis diagnostic system using convolutional network for ordinal regression

Ryosuke Saito, Norihiro Koizumi, Yu Nishiyama, Tsubasa Imaizumi, Kenta Kusahara, Shiho Yagasaki, Naoki Matsumoto, Ryota Masuzaki, Toshimi Takahashi, Masahiro Ogawa

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1969-1975
Number of pages7
JournalInternational journal of computer assisted radiology and surgery
Issue number11
Publication statusPublished - Nov 2021


  • Deep learning
  • Liver fibrosis
  • Ultrasound image


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