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Fully convolutional networks (FCNs)-based segmentation method for colorectal tumors on T2-weighted magnetic resonance images

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Abstract

Segmentation of colorectal tumors is the basis of preoperative prediction, staging, and therapeutic response evaluation. Due to the blurred boundary between lesions and normal colorectal tissue, it is hard to realize accurate segmentation. Routinely manual or semi-manual segmentation methods are extremely tedious, time-consuming, and highly operator-dependent. In the framework of FCNs, a segmentation method for colorectal tumor was presented. Normalization was applied to reduce the differences among images. Borrowing from transfer learning, VGG-16 was employed to extract features from normalized images. We conducted five side-output blocks from the last convolutional layer of each block of VGG-16 along the network, these side-output blocks can deep dive multiscale features, and produced corresponding predictions. Finally, all of the predictions from side-output blocks were fused to determine the final boundaries of the tumors. A quantitative comparison of 2772 colorectal tumor manual segmentation results from T2-weighted magnetic resonance images shows that the average Dice similarity coefficient, positive predictive value, specificity, sensitivity, Hammoude distance, and Hausdorff distance were 83.56, 82.67, 96.75, 87.85%, 0.2694, and 8.20, respectively. The proposed method is superior to U-net in colorectal tumor segmentation (P < 0.05). There is no difference between cross-entropy loss and Dice-based loss in colorectal tumor segmentation (P > 0.05). The results indicate that the introduction of FCNs contributed to accurate segmentation of colorectal tumors. This method has the potential to replace the present time-consuming and nonreproducible manual segmentation method.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (81571772).

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Authors and Affiliations

  1. University of Science and Technology of China, No. 96 Jinzhai Road, Baohe District, Hefei, 230026, Anhui, China

    Junming Jian

  2. Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, No. 88 Keling Road, Suzhou New District, Suzhou, 215163, Jiangsu, China

    Junming Jian, Wei Xia, Rui Zhang, Xiaodong Wu & Xin Gao

  3. Department of Radiology, The Sixth Affiliated Hospital of Sun Yat-sen University, No. 26 Yuancunerheng Road, Tianhe District, Guangzhou, 510655, Guangdong, China

    Fei Xiong & Xiaochun Meng

  4. Department of Anorectal, The people’s hospital of Suzhou New District, No. 95 Huashan Road, Suzhou New District, Suzhou, 215163, Jiangsu, China

    Jinhui Gu

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  1. Junming Jian
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  2. Fei Xiong
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  3. Wei Xia
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Correspondence to Xiaochun Meng or Xin Gao.

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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 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Jian, J., Xiong, F., Xia, W. et al. Fully convolutional networks (FCNs)-based segmentation method for colorectal tumors on T2-weighted magnetic resonance images. Australas Phys Eng Sci Med 41, 393–401 (2018). https://doi.org/10.1007/s13246-018-0636-9

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  • DOI: https://doi.org/10.1007/s13246-018-0636-9

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