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Multichannel three-dimensional fully convolutional residual network-based focal liver lesion detection and classification in Gd-EOB-DTPA-enhanced MRI

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) has high diagnostic accuracy in the detection of liver lesions. There is a demand for computer-aided detection/diagnosis software for Gd-EOB-DTPA-enhanced MRI. We propose a deep learning-based method using one three-dimensional fully convolutional residual network (3D FC-ResNet) for liver segmentation and another 3D FC-ResNet for simultaneous detection and classification of a focal liver lesion in Gd-EOB-DTPA-enhanced MRI.

Methods

We prepared a five-phase (unenhanced, arterial, portal venous, equilibrium, and hepatobiliary phases) series as the input image sets and labeled focal liver lesion (hepatocellular carcinoma, metastasis, hemangiomas, cysts, and scars) images as the output image sets. We used 100 cases to train our model, 42 cases to determine the hyperparameters of our model, and 42 cases to evaluate our model. We evaluated our model by free-response receiver operating characteristic curve analysis and using a confusion matrix.

Results

Our model simultaneously detected and classified focal liver lesions. In the test cases, the detection accuracy for whole focal liver lesions had a true-positive ratio of 0.6 at an average of 25 false positives per case. The classification accuracy was 0.790.

Conclusion

We proposed the simultaneous detection and classification of a focal liver lesion in Gd-EOB-DTPA-enhanced MRI using multichannel 3D FC-ResNet. Our results indicated simultaneous detection and classification are possible using a single network. It is necessary to further improve detection sensitivity to help radiologists.

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Acknowledgements

The Department of Computational Radiology and Preventive Medicine, the University of Tokyo Hospital, is sponsored by HIMEDIC Inc. and Siemens Healthcare K.K. This work was supported by Japan Society for the Promotion of Science (JSPS) KAKENHI Grant Numbers 17K17653 and 20K20215. This work was also supported by the Joint Usage/Research Center for Interdisciplinary Large-Scale Information Infrastructures and High-Performance Computing Infrastructure Projects in Japan (Project IDs: jh170036-DAH, jh180073-DAH, jh190047-DAH, and jh200042-DAG).

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Authors and Affiliations

  1. Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan

    Tomomi Takenaga, Yukihiro Nomura, Takahiro Nakao, Hisaichi Shibata, Soichiro Miki, Takeharu Yoshikawa & Naoto Hayashi

  2. Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan

    Shouhei Hanaoka & Osamu Abe

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  1. Tomomi Takenaga
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  2. Shouhei Hanaoka
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  3. Yukihiro Nomura
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  4. Takahiro Nakao
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  5. Hisaichi Shibata
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  7. Takeharu Yoshikawa
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  8. Naoto Hayashi
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  9. Osamu Abe
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Correspondence to Tomomi Takenaga.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1975 Helsinki Declaration, as revised in 2008(5). For this type of study, formal consent is not required.

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Takenaga, T., Hanaoka, S., Nomura, Y. et al. Multichannel three-dimensional fully convolutional residual network-based focal liver lesion detection and classification in Gd-EOB-DTPA-enhanced MRI. Int J CARS 16, 1527–1536 (2021). https://doi.org/10.1007/s11548-021-02416-y

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