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October 23, 2025Scientific ReportsOpen Access

A lightweight network for brain MRI segmentation

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This framework utilizes deep learning for brain segmentation, enhancing accuracy while addressing model complexity and class imbalance.

Segmentation improves identification of brain diseases, making it crucial for medical imaging.
The method achieves high accuracy with a lightweight design, facilitating real-world application.
Analysis employing state-space modeling effectively captures long-range spatial dependencies for better performance.
This approach may enable superior segmentation results compared to traditional methods with higher complexity.

Chatterjee et al. (2025) studied this question.

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October 23, 2025Scientific ReportsOpen Access

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Authors

This framework utilizes deep learning for brain segmentation, enhancing accuracy while addressing model complexity and class imbalance.

Segmentation improves identification of brain diseases, making it crucial for medical imaging.
The method achieves high accuracy with a lightweight design, facilitating real-world application.
Analysis employing state-space modeling effectively captures long-range spatial dependencies for better performance.
This approach may enable superior segmentation results compared to traditional methods with higher complexity.

Chatterjee et al. (2025) studied this question.

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