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Brain stroke lesion segmentation using consistent perception generative adversarial network

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Abstract

The state-of-the-art deep learning methods have demonstrated impressive performance in segmentation tasks. However, the success of these methods depends on a large amount of manually labeled masks, which are expensive and time-consuming to be collected. In this work, a novel consistent perception generative adversarial network (CPGAN) is proposed for semi-supervised stroke lesion segmentation. The proposed CPGAN can reduce the reliance on fully labeled samples. Specifically, a similarity connection module (SCM) is designed to capture the information of multi-scale features. The proposed SCM can selectively aggregate the features at each position by a weighted sum. Moreover, a consistent perception strategy is introduced into the proposed model to enhance the effect of brain stroke lesion prediction for the unlabeled data. Furthermore, an assistant network is constructed to encourage the discriminator to learn meaningful feature representations which are often forgotten during training stage. The assistant network and the discriminator are employed to jointly decide whether the segmentation results are real or fake. The CPGAN was evaluated on the Anatomical Tracings of Lesions After Stroke (ATLAS). The experimental results demonstrate that the proposed network achieves superior segmentation performance. In semi-supervised segmentation task, the proposed CPGAN using only two-fifths of labeled samples outperforms some approaches using full labeled samples.

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Acknowledgements

This work was supported by the National Natural Science Foundations of China under Grants 62172403 and 61872351, the International Science and Technology Cooperation Projects of Guangdong under Grant2019A050510030, the Distinguished Young Scholars Fund of Guangdong under Grant 2021B1515020019, the Excellent Young Scholars of Shenzhen under Grant RCYX20200714114641211 and the Shenzhen Key Basic Research Project under Grants JCYJ20180507182506416 and JCYJ20200109115641762.

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Authors and Affiliations

  1. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 518055, Shenzhen, China

    Shuqiang Wang, Zhuo Chen, Senrong You & Yanyan Shen

  2. School of Automation, Central South University, 410083, Changsha, China

    Bingchuan Wang

  3. School of Biomedical Engineering, Shenzhen University, 518060, Shenzhen, China

    Baiying Lei

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  1. Shuqiang Wang
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Wang, S., Chen, Z., You, S. et al. Brain stroke lesion segmentation using consistent perception generative adversarial network. Neural Comput & Applic 34, 8657–8669 (2022). https://doi.org/10.1007/s00521-021-06816-8

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