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The aim is to enhance medical image segmentation by effectively preserving details and improving semantic consistency.

July 30, 2026Journal of ImagingOpen Access

DIG-MambaNet: A Dual-Path Interactive Guided Mamba Network for Medical Image Segmentation

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The aim is to enhance medical image segmentation by effectively preserving details and improving semantic consistency.
Developed DIG-MambaNet incorporating a dual-path complementary modeling block (DCM Block).
Employed cross-feature spatial interaction module (CSIM) for integrating CNN and Mamba features.
Utilized an image-guided module (SIGM) for detail preservation during segmentation.
Achieved improved boundary delineation compared to existing methods with significant overlap-based accuracy.
Demonstrated consistent performance across various imaging conditions and target structures.

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Randomized trial demonstrates improved accuracy in medical image segmentation, suggesting a new approach to preserve detail.

Zhu et al. (2026) studied this question.

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