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Updated: Dec 6, 2025

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Deep Learning for Neuroimaging Segmentation with a Novel Data Augmentation Strategy.

Wenshan Wu, Yuhao Lu, Ravikiran Mane

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 6, 2020
    PubMed
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    This study introduces a novel deep learning method to improve stroke lesion segmentation. The approach uses generative adversarial networks for data augmentation and a specialized convolutional neural network, significantly enhancing accuracy in stroke imaging analysis.

    Area of Science:

    • Medical image analysis
    • Artificial intelligence in healthcare
    • Neurology

    Background:

    • Stroke, including cerebral ischemia and intracranial hemorrhage, presents significant mortality risks.
    • Manual medical image analysis for stroke is time-consuming and labor-intensive.
    • Deep learning methods for medical imaging require substantial improvement and more labeled data for clinical use.

    Purpose of the Study:

    • To address the challenge of limited labeled data in deep learning for stroke lesion segmentation.
    • To propose an integrated deep learning method combining data augmentation and advanced segmentation techniques.
    • To improve the accuracy and efficiency of stroke lesion segmentation in medical images.

    Main Methods:

    • Developed a data augmentation framework using a conditional Generative Adversarial Network (cGAN) to generate synthetic brain images from lesion masks.

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  • Employed a Convolutional Neural Network (CNN) with depth-wise-convolution based X-blocks and a Feature Similarity Module (FSM) for supervised segmentation.
  • Integrated cGAN-based data augmentation with the specialized CNN for enhanced stroke lesion segmentation.

    • Main Results:

    • The proposed method successfully generated meaningful brain images through cGAN-based data augmentation.
    • The CNN with X-blocks and FSM demonstrated improved training efficiency and segmentation performance.
    • The integrated deep learning strategy outperformed current state-of-the-art methods on the Anatomical Tracings of Lesions After Stroke (ATLAS) dataset.

    Conclusions:

    • The proposed integrated deep learning method effectively mitigates the problem of insufficient labeled data for stroke lesion segmentation.
    • The combination of cGAN data augmentation and advanced CNN architecture significantly improves segmentation accuracy.
    • This approach shows great potential for clinical application in stroke diagnosis and management.