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Clinical Article
The value of 3D convolution neural network based on multimodal MRI images in the classification of liver fibrosis
FAN Fengxian  HU Wanjun  JIANG Yanli  ZOU Jie  YANG Pin  ZHANG Jing 

Cite this article as: Fan FX, Hu WJ, Jiang YL, et al. The value of 3D convolution neural network based on multimodal MRI images in the classification of liver fibrosis[J]. Chin J Magn Reson Imaging, 2022, 13(9): 30-34. DOI:10.12015/issn.1674-8034.2022.09.006.

[Abstract] Objective To construct a 3D convolution neural network (CNN) model of multi-modal MRI images, and verify its value in classification of liver fibrosis (LF).Materials and Methods Two hundred and twenty four cases with LF confirmed by pathology were retrospectively collected. All patients underwent 3.0 T MRI exams. Collected the T1WI, T2WI, and apparent diffusion coefficient (ADC) images and randomly divided them into training group and testing group according to the ratio of 8∶2. After the images were preprocessed, the images of training group were used to iteratively train the network structure of the model. And then a 3D-CNN model was established to distinguish between no-significant LF (S0-S1) and significant LF (≥S2). The 3D-CNN model was composed of three convolution layers, three pooling layers and two fully connected layers. The accuracy (ACC), loss function curves and receiver operating characteristic (ROC) curves acquired by using the testing dataset were used to evaluate the performance of the 3D-CNN model.Results The area under the curve (AUC) value of 3D-CNN model based on multiparametric MRI for LF classification was 0.94 in the training group and 0.98 in the testing group.Conclusions The multiparametric 3D-CNN deep learning model may be an effective method, which can distinguish between no-significant and significant LF. It provides more options for non-invasive assessment of LF.
[Keywords] liver fibrosis;multimodal magnetic resonance imaging;machine learning;convolutional neural network

FAN Fengxian1, 2   HU Wanjun1, 2   JIANG Yanli1, 2   ZOU Jie1, 2   YANG Pin1, 2   ZHANG Jing1, 2*  

1 Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou 730030, China

2 Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou 730030, China

*Zhang J, E-mail:

Conflicts of interest   None.

ACKNOWLEDGMENTS Science and Technology Plan Project of Gansu Province (No. 21JR11RA122); Lanzhou University Second Hospital "Cuiying Science and Technology Innovation" Program (No. CY2021-QN-B09).
Received  2022-04-02
Accepted  2022-08-19
DOI: 10.12015/issn.1674-8034.2022.09.006
Cite this article as: Fan FX, Hu WJ, Jiang YL, et al. The value of 3D convolution neural network based on multimodal MRI images in the classification of liver fibrosis[J]. Chin J Magn Reson Imaging, 2022, 13(9): 30-34. DOI:10.12015/issn.1674-8034.2022.09.006.

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