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Deep learning for fully automated tumor segmentation and extraction of magnetic resonance radiomics features in cervical cancer



To develop and evaluate the performance of U-Net for fully automated localization and segmentation of cervical tumors in magnetic resonance (MR) images and the robustness of extracting apparent diffusion coefficient (ADC) radiomics features.


This retrospective study involved analysis of MR images from 169 patients with cervical cancer stage IB–IVA captured; among them, diffusion-weighted (DW) images from 144 patients were used for training, and another 25 patients were recruited for testing. A U-Net convolutional network was developed to perform automated tumor segmentation. The manually delineated tumor region was used as the ground truth for comparison. Segmentation performance was assessed for various combinations of input sources for training. ADC radiomics were extracted and assessed using Pearson correlation. The reproducibility of the training was also assessed.


Combining b0, b1000, and ADC images as a triple-channel input exhibited the highest learning efficacy in the training phase and had the highest accuracy in the testing dataset, with a dice coefficient of 0.82, sensitivity 0.89, and a positive predicted value 0.92. The first-order ADC radiomics parameters were significantly correlated between the manually contoured and fully automated segmentation methods (p < 0.05). Reproducibility between the first and second training iterations was high for the first-order radiomics parameters (intraclass correlation coefficient = 0.70–0.99).


U-Net-based deep learning can perform accurate localization and segmentation of cervical cancer in DW MR images. First-order radiomics features extracted from whole tumor volume demonstrate the potential robustness for longitudinal monitoring of tumor responses in broad clinical settings.


U-Net-based deep learning can perform accurate localization and segmentation of cervical cancer in DW MR images.

Key Points

U-Net-based deep learning can perform accurate fully automated localization and segmentation of cervical cancer in diffusion-weighted MR images.

Combining b0, b1000, and apparent diffusion coefficient (ADC) images exhibited the highest accuracy in fully automated localization.

• First-order radiomics feature extraction from whole tumor volume was robust and could thus potentially be used for longitudinal monitoring of treatment responses.

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Apparent diffusion coefficient


Dice similarity coefficient




Magnetic resonance


Positive predictive value


Region of interest




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The authors acknowledge the assistance provided by the Cancer Center and Clinical Trial Center (Statistician Dr. Lan-Yan Yang), Chang Gung Memorial Hospital, Linkou, Taiwan, which was founded by the Ministry of Health and Welfare of Taiwan MOHW106-TDU-B-212-113005.


This study was supported by the Chang Gung Medical Foundation grant nos. CPRPG3G0021-3, CIRPG3H0011, CIRPG3D0163, and CMRPG3I0141; Ministry of Science and Technology (Taiwan) grant no. MOST 106-2314-B-182A-016-MY2; and Chang Gung IRB97-2366B, IRB102-0620A3, IRB201800412B0, and IRB201702204B0.

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Corresponding author

Correspondence to Gigin Lin.

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The scientific guarantor of this publication is Gigin Lin, MD, PhD.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

Dr. Lan-Yan Yang kindly provided statistical advice for this manuscript.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional Review Board approval was obtained.


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• performed at one institution

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Lin, YC., Lin, CH., Lu, HY. et al. Deep learning for fully automated tumor segmentation and extraction of magnetic resonance radiomics features in cervical cancer. Eur Radiol 30, 1297–1305 (2020).

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  • Apparent diffusion coefficient
  • Diffusion-weighted imaging
  • Uterine cervical neoplasm
  • Deep learning
  • Radiomics