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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Vydané v:	Symmetry (Basel) Ročník 17; číslo 9; s. 1506
Hlavní autori:	Zhang, Shengnan, Cui, Xinming, Ma, Guangkun, Tian, Ronghui
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Basel MDPI AG 01.09.2025
Predmet:	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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Abstract	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.
AbstractList	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.
Audience	Academic
Author	Zhang, Shengnan Tian, Ronghui Cui, Xinming Ma, Guangkun
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Cites_doi	10.1038/s41592-020-01008-z 10.1007/978-3-031-25063-7 10.1109/CVPR42600.2020.00285 10.1016/j.compbiomed.2023.107470 10.1109/TPAMI.2021.3085766 10.1016/j.ekir.2019.04.008 10.1109/ACCESS.2023.3320064 10.1007/978-3-319-24553-9 10.1109/CVPR46437.2021.00866 10.1038/s41586-019-1629-x 10.1109/CVPR46437.2021.01142 10.1016/j.patcog.2021.108414 10.1038/s41581-020-0321-6 10.1007/s11633-022-1371-y 10.1007/s44267-023-00019-6 10.1016/j.kisu.2021.11.003 10.1109/CVPR52688.2022.01167 10.1007/978-3-030-01234-2_49 10.1016/j.cmpb.2019.105273 10.3934/mbe.2023007 10.1016/j.compmedimag.2021.101865 10.1109/ICASSP40776.2020.9053405 10.1109/CVPR52688.2022.00220 10.3390/rs12182932 10.1109/CVPR52729.2023.02111 10.24963/ijcai.2022/186 10.1016/j.media.2023.102802 10.1016/j.media.2022.102395 10.1109/CVPR.2017.660
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References	Xie (ref_20) 2021; 34 Zheng (ref_31) 2024; 132 ref_14 ref_36 Kannan (ref_21) 2019; 4 ref_35 ref_34 ref_11 ref_33 ref_10 ref_32 ref_30 Kovesdy (ref_1) 2022; 12 ref_19 ref_18 ref_17 ref_16 Barisoni (ref_2) 2020; 16 Dao (ref_13) 2022; 35 ref_15 ref_37 Isensee (ref_4) 2021; 18 Fan (ref_27) 2022; 44 Yan (ref_26) 2023; 11 Feng (ref_25) 2023; 20 ref_24 ref_23 ref_22 ref_3 Ji (ref_12) 2022; 19 ref_29 ref_28 ref_9 ref_8 ref_5 ref_7 ref_6
References_xml	– volume: 18 start-page: 203 year: 2021 ident: ref_4 article-title: nnU-Net: A self-configuring method for deep learning-based biomedical image segmentation publication-title: Nat. Methods doi: 10.1038/s41592-020-01008-z – ident: ref_7 – ident: ref_19 doi: 10.1007/978-3-031-25063-7 – ident: ref_10 doi: 10.1109/CVPR42600.2020.00285 – ident: ref_22 doi: 10.1016/j.compbiomed.2023.107470 – volume: 44 start-page: 6024 year: 2022 ident: ref_27 article-title: Concealed object detection publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2021.3085766 – volume: 35 start-page: 16344 year: 2022 ident: ref_13 article-title: FlashAttention: Fast and memory-efficient exact attention with IO-awareness publication-title: Adv. Neural Inf. Process. Syst. – ident: ref_34 – volume: 4 start-page: 955 year: 2019 ident: ref_21 article-title: Segmentation of glomeruli within trichrome images using deep learning publication-title: Kidney Int. Rep. doi: 10.1016/j.ekir.2019.04.008 – volume: 11 start-page: 105892 year: 2023 ident: ref_26 article-title: Self reinforcing multi-class transformer for kidney glomerular basement membrane segmentation publication-title: IEEE Access doi: 10.1109/ACCESS.2023.3320064 – ident: ref_15 doi: 10.1007/978-3-319-24553-9 – volume: 34 start-page: 12077 year: 2021 ident: ref_20 article-title: SegFormer: Simple and efficient design for semantic segmentation with transformers publication-title: Adv. Neural Inf. Process. Syst. – ident: ref_28 doi: 10.1109/CVPR46437.2021.00866 – volume: 132 start-page: 2920 year: 2024 ident: ref_31 article-title: Bilateral reference for high-resolution dichotomous image segmentation publication-title: Int. J. Comput. Vis. – ident: ref_37 doi: 10.1038/s41586-019-1629-x – ident: ref_11 doi: 10.1109/CVPR46437.2021.01142 – ident: ref_32 doi: 10.1016/j.patcog.2021.108414 – ident: ref_14 – volume: 16 start-page: 669 year: 2020 ident: ref_2 article-title: Digital pathology and computational image analysis in nephropathology publication-title: Nat. Rev. Nephrol. doi: 10.1038/s41581-020-0321-6 – ident: ref_35 – volume: 19 start-page: 531 year: 2022 ident: ref_12 article-title: Video polyp segmentation: A deep learning perspective publication-title: Mach. Intell. Res. doi: 10.1007/s11633-022-1371-y – ident: ref_3 doi: 10.1007/s44267-023-00019-6 – volume: 12 start-page: 7 year: 2022 ident: ref_1 article-title: Epidemiology of chronic kidney disease: An update 2022 publication-title: Kidney Int. Suppl. doi: 10.1016/j.kisu.2021.11.003 – ident: ref_18 doi: 10.1109/CVPR52688.2022.01167 – ident: ref_5 doi: 10.1007/978-3-030-01234-2_49 – ident: ref_23 doi: 10.1016/j.cmpb.2019.105273 – volume: 20 start-page: 128 year: 2023 ident: ref_25 article-title: ConvWin-UNet: UNet-like hierarchical vision transformer combined with convolution for medical image segmentation publication-title: Math. Biosci. Eng. doi: 10.3934/mbe.2023007 – ident: ref_24 doi: 10.1016/j.compmedimag.2021.101865 – ident: ref_16 doi: 10.1109/ICASSP40776.2020.9053405 – ident: ref_29 doi: 10.1109/CVPR52688.2022.00220 – ident: ref_36 – ident: ref_9 doi: 10.3390/rs12182932 – ident: ref_33 doi: 10.1109/CVPR52729.2023.02111 – ident: ref_30 doi: 10.24963/ijcai.2022/186 – ident: ref_6 doi: 10.1016/j.media.2023.102802 – ident: ref_8 doi: 10.1016/j.media.2022.102395 – ident: ref_17 doi: 10.1109/CVPR.2017.660
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SubjectTerms	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
Title	Symmetric Boundary-Enhanced U-Net with Mamba Architecture for Glomerular Segmentation in Renal Pathological Images
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