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Invariant Content Representation for Generalizable Medical Image Segmentation

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Abstract

Domain generalization (DG) for medical image segmentation due to privacy preservation prefers learning from a single-source domain and expects good robustness on unseen target domains. To achieve this goal, previous methods mainly use data augmentation to expand the distribution of samples and learn invariant content from them. However, most of these methods commonly perform global augmentation, leading to limited augmented sample diversity. In addition, the style of the augmented image is more scattered than the source domain, which may cause the model to overfit the style of the source domain. To address the above issues, we propose an invariant content representation network (ICRN) to enhance the learning of invariant content and suppress the learning of variability styles. Specifically, we first design a gamma correction-based local style augmentation (LSA) to expand the distribution of samples by augmenting foreground and background styles, respectively. Then, based on the augmented samples, we introduce invariant content learning (ICL) to learn generalizable invariant content from both augmented and source-domain samples. Finally, we design domain-specific batch normalization (DSBN) based style adversarial learning (SAL) to suppress the learning of preferences for source-domain styles. Experimental results show that our proposed method improves by 8.74% and 11.33% in overall dice coefficient (Dice) and reduces 15.88 mm and 3.87 mm in overall average surface distance (ASD) on two publicly available cross-domain datasets, Fundus and Prostate, compared to the state-of-the-art DG methods. The code is available at https://github.com/ZMC-IIIM/ICRN-DG.

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Data Availability

The data used in the study is publicly available and can be downloaded. For more details, please refer to the code link provided in the abstract.

Code Availability

The code link is already included in the abstract, and it will be made publicly available upon publication.

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Funding

This work was supported in part by the Shandong Provincial Natural Science Foundation (Grant No.2022HWYQ-041), the Natural Science Foundation of Jiangsu Province (Grant No.BK20220266), and the National Nature Science Foundation of China (Grant No.62071415).

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Authors and Affiliations

  1. School of Automation, Hangzhou Dianzi University, Hangzhou, 310018, China

    Zhiming Cheng & Yuhan Gao

  2. School of Cyberspace, Hangzhou Dianzi University, Hangzhou, 310018, China

    Shuai Wang

  3. Suzhou Research Institute of Shandong University, SuZhou, 215123, China

    Shuai Wang

  4. Lishui Institute of Hangzhou Dianzi Universitu, Lishui, 323010, China

    Yuhan Gao & Zunjie Zhu

  5. School of Communication, Engineering, Hangzhou Dianzi Universitu, Hangzhou, 310018, China

    Zunjie Zhu & Chenggang Yan

Authors
  1. Zhiming Cheng
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  2. Shuai Wang
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  3. Yuhan Gao
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  4. Zunjie Zhu
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Contributions

Zhiming Cheng: conceptualization, methodology, writing — original draft. Shuai Wang: conceptualization, methodology, analysis, writing — review and editing, supervision, funding acquisition. Yunhan Gao: data curation. Zunjie Zhu: writing — review and editing, supervision. Chenggang Yan: writing — review and editing.

Corresponding author

Correspondence to Shuai Wang.

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This study uses data that are publicly available. The Institute Research Ethics Committee has confirmed that no ethical approval is required.

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

Consent for Publication

The data used in this study were obtained from publicly available datasets. These datasets have been anonymized and do not contain any individual details, images, or videos, thus obviating the need to obtain publication consent from individual participants.

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The authors declare no competing interests.

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Cheng, Z., Wang, S., Gao, Y. et al. Invariant Content Representation for Generalizable Medical Image Segmentation. J Digit Imaging. Inform. med. (2024). https://doi.org/10.1007/s10278-024-01088-9

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  • DOI: https://doi.org/10.1007/s10278-024-01088-9

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