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Improving segmentation and classification of renal tumors in small sample 3D CT images using transfer learning with convolutional neural networks

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

Abstract

Purpose

Computed tomography (CT) images can display internal organs of patients and are particularly suitable for preoperative surgical diagnoses. The increasing demands for computer-aided systems in recent years have facilitated the development of many automated algorithms, especially deep convolutional neural networks, to segment organs and tumors or identify diseases from CT images. However, performances of some systems are highly affected by the amount of training data, while the sizes of medical image data sets, especially three-dimensional (3D) data sets, are usually small. This condition limits the application of deep learning.

Methods

In this study, given a practical clinical data set that has 3D CT images of 20 patients with renal carcinoma, we designed a pipeline employing transfer learning to alleviate the detrimental effect of the small sample size. A dual-channel fine segmentation network (FS-Net) was constructed to segment kidney and tumor regions, with 210 publicly available 3D images from a competition employed during the training phase. We also built discriminative classifiers to classify the benign and malignant tumors based on the segmented regions, where both handcrafted and deep features were tested.

Results

Our experimental results showed that the Dice values of segmented kidney and tumor regions were 0.9662 and 0.7685, respectively, which were better than those of state-of-the-art methods. The classification model using radiomics features can classify most of the tumors correctly.

Conclusions

The designed FS-Net was demonstrated to be more effective than simply fine-tuning on the practical small size data set given that the model can borrow knowledge from large auxiliary data without diluting the signal in primary data. For the small data set, radiomics features outperformed deep features in the classification of benign and malignant tumors. This work highlights the importance of architecture design in transfer learning, and the proposed pipeline is anticipated to provide a reference and inspiration for small data analysis.

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Funding

This work was supported in part by the National Natural Science Foundation of China (61803196), Natural Science Foundation of Guangdong Province of China (2020A1515010038), and Presidential Foundation of Zhujiang Hospital of Southern Medical University (yzjj2018rc03).

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Authors and Affiliations

  1. Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China

    Xi-Liang Zhu & Ying-Ying Xu

  2. Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, 510515, China

    Xi-Liang Zhu & Ying-Ying Xu

  3. Institute of Image Processing and Pattern Recognition, Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai Jiao Tong University, Shanghai, 200240, China

    Hong-Bin Shen

  4. Neurosurgery Center, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China on Diagnosis and Treatment of Cerebrovascular Disease, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, The Neurosurgery Institute of Guangdong Province Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China

    Haitao Sun

  5. Guangzhou Red Cross Hospital, Medical College, Jinan University, Guangzhou, 510220, Guangdong, China

    Li-Xia Duan

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  1. Xi-Liang Zhu
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  2. Hong-Bin Shen
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Correspondence to Li-Xia Duan or Ying-Ying Xu.

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The authors declared that they have no conflicts of interest in this work.

Ethical approval

This work was conducted retrospectively on data from clinical routine which was completely anonymized. Ethical approval was, therefore, not required. This work relies on the KiTS19 data set. For use of these data sets, no ethics statements are necessary.

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Informed consent was obtained from all individual participants included in the study.

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Zhu, XL., Shen, HB., Sun, H. et al. Improving segmentation and classification of renal tumors in small sample 3D CT images using transfer learning with convolutional neural networks. Int J CARS 17, 1303–1311 (2022). https://doi.org/10.1007/s11548-022-02587-2

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  • DOI: https://doi.org/10.1007/s11548-022-02587-2

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