Symmetric Boundary-Enhanced U-Net with Mamba Architecture for Glomerular Segmentation in Renal Pathological Images

Accurate glomerular segmentation in renal pathological images is a key challenge for chronic kidney disease diagnosis and assessment. Due to the high visual similarity between pathological glomeruli and surrounding tissues in color, texture, and morphology, significant “camouflage phenomena” exist,...

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Vydáno v:Symmetry (Basel) Ročník 17; číslo 9; s. 1506
Hlavní autoři: Zhang, Shengnan, Cui, Xinming, Ma, Guangkun, Tian, Ronghui
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.09.2025
Témata:
Accuracy
Ambiguity
Chronic kidney failure
Deep learning
Diagnosis
Efficiency
Feature extraction
Image processing
Image reconstruction
Image segmentation
Kidney diseases
Medical imaging
Medical imaging equipment
Modules
Morphology
Pathology
Semantics
ISSN:2073-8994, 2073-8994
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Shrnutí:Accurate glomerular segmentation in renal pathological images is a key challenge for chronic kidney disease diagnosis and assessment. Due to the high visual similarity between pathological glomeruli and surrounding tissues in color, texture, and morphology, significant “camouflage phenomena” exist, leading to boundary identification difficulties. To address this problem, we propose BM-UNet, a novel segmentation framework that embeds boundary guidance mechanisms into a Mamba architecture with a symmetric encoder–decoder design. The framework enhances feature transmission through explicit boundary detection, incorporating four core modules designed for key challenges in pathological image segmentation. The Multi-scale Adaptive Fusion (MAF) module processes irregular tissue morphology, the Hybrid Boundary Detection (HBD) module handles boundary feature extraction, the Boundary-guided Attention (BGA) module achieves boundary-aware feature refinement, and the Mamba-based Fused Decoder Block (MFDB) completes boundary-preserving reconstruction. By introducing explicit boundary supervision mechanisms, the framework achieves significant segmentation accuracy improvements while maintaining linear computational complexity. Validation on the KPIs2024 glomerular dataset and HuBMAP renal tissue samples demonstrates that BM-UNet achieves a 92.4–95.3% mean Intersection over Union across different CKD pathological conditions, with a 4.57% improvement over the Mamba baseline and a processing speed of 113.7 FPS.
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ISSN:2073-8994
2073-8994
DOI:10.3390/sym17091506