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Rethinking the Dice Loss for Deep Learning Lesion Segmentation in Medical Images

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Abstract

Deep learning is widely used for lesion segmentation in medical images due to its breakthrough performance. Loss functions are critical in a deep learning pipeline, and they play important roles in segmenting performance. Dice loss is the most commonly used loss function in medical image segmentation, but it also has some disadvantages. In this paper, we discuss the advantages and disadvantages of the Dice loss function, and group the extensions of the Dice loss according to its improved purpose. The performances of some extensions are compared according to core references. Because different loss functions have different performances in different tasks, automatic loss function selection will be the potential direction in the future.

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Authors and Affiliations

  1. Key Laboratory of Space Active Opto-Electronics Technology, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, 200083, China

    Yue Zhang  (张月), Shijie Liu  (刘世界), Chunlai Li  (李春来) & Jianyu Wang  (王建宇)

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Yue Zhang  (张月), Shijie Liu  (刘世界) & Jianyu Wang  (王建宇)

  3. Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, 310024, China

    Shijie Liu  (刘世界) & Jianyu Wang  (王建宇)

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  1. Yue Zhang  (张月)
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  2. Shijie Liu  (刘世界)
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  3. Chunlai Li  (李春来)
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  4. Jianyu Wang  (王建宇)
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Correspondence to Jianyu Wang  (王建宇).

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Zhang, Y., Liu, S., Li, C. et al. Rethinking the Dice Loss for Deep Learning Lesion Segmentation in Medical Images. J. Shanghai Jiaotong Univ. (Sci.) 26, 93–102 (2021). https://doi.org/10.1007/s12204-021-2264-x

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  • DOI: https://doi.org/10.1007/s12204-021-2264-x

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