September 10, 2025ResearchOpen Access

E-SegNet: E-shaped Structure Networks for Accurate 2D and 3D Medical Image Segmentation

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The E-structured approach outperforms U-shaped models, achieving state-of-the-art performance with fewer parameters.
Experiments on 2D and 3D medical images across multiple benchmark datasets demonstrate significant performance gains.
The multi-scale large-kernel convolution module effectively captures both local and global contextual information.
This novel framework contributes to advancements in medical image segmentation and practical applications.

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Implication

The innovative E-shaped structure reduces parameters while improving accuracy in medical image segmentation, highlighting advantages over traditional U-structures.

Cite This Study

Wu et al. (2025) studied this question.

synapsesocial.com/papers/68c2427db210217d647a441d https://doi.org/10.34133/research.0869

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Implication

The innovative E-shaped structure reduces parameters while improving accuracy in medical image segmentation, highlighting advantages over traditional U-structures.

E-SegNet: E-shaped Structure Networks for Accurate 2D and 3D Medical Image Segmentation

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Discussion

Implication

Cite This Study

E-SegNet: E-shaped Structure Networks for Accurate 2D and 3D Medical Image Segmentation

Key Points

Discussion

Implication

Cite This Study

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