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.

Background: Accurate medical image segmentation is essential for clinical diagnosis and treatment planning, yet precise boundary delineation remains a significant challenge, particularly in cases with highly ambiguous boundaries such as gastrointestinal polyps, skin lesions, and pathological tissues. Despite the considerable progress achieved by existing methods, their reliance on manual parameter tuning and limited capability in detecting highly obscure boundaries hinder their practical application in clinical settings. Purpose: To overcome these limitations, we propose FEA-Net, a novel medical image segmentation model that, for the first time, integrates Camouflaged Object Detection (COD) technology with frequency-domain feature enhancement to improve boundary delineation in medical images. By leveraging COD’s capability to detect hidden objects and combining it with frequency-domain processing, our method effectively addresses the challenges posed by boundary ambiguity. Methods: We propose FEA-Net, a segmentation framework leveraging FTEM to enhance high-frequency boundary signals while suppressing noise. It incorporates a Semantic Edge Enhancement Module (SEEM) for initial edge extraction, followed by FTEM for frequency-domain refinement. The Adaptive Edge-grained Feature Module (AEFM) and Context Attention Aggregation Module (CAAM) further refine edge granularity and enhance contextual aggregation. Results: Experiments on ISIC2018, Kvasir-SEG, and KPIs2024 show that FEA-Net outperforms state-of-the-art models, improving mIoU by 2.67%, mDice by 2.20%, and F1-score by 2.47%. Notably, it enhances boundary detection in ambiguous regions, reducing false positives and improving segmentation precision. Conclusion: By pioneering the fusion of COD technology with frequency-domain feature enhancement, FEA-Net establishes a new paradigm in medical image segmentation. It provides an automated and highly effective solution for tackling ambiguous boundaries, paving the way for more precise and clinically viable segmentation methods.

Accurate glomerular segmentation is crucial for clinical diagnosis and pathological analysis in renal histology research. To address the challenge of blurred boundaries between glomeruli and surrounding tissues in nephropathology analysis, this paper proposes EdgeAttenNet, an innovatively improved model based on the BGNet architecture that enhances the recognition accuracy of glomerular boundaries. The model primarily consists of three enhanced core modules improved from BGNet: Attention-Guided Boundary Module (AGBM), Edge-guided Feature Enhancement Module (EFEM), and Adaptive Fusion Context Module (AFCM). The AGBM first captures glomerular boundary information through multi-scale attention mechanisms. The EFEM enhances boundary feature representation at different scales by incorporating the extracted edge semantic information. Subsequently, the AFCM integrates these multi-scale features through attentional feature fusion to strengthen the model's global feature characterization capabilities. Experimental results demonstrate that EdgeAttenNet achieves superior glomerular segmentation performance over the baseline BGNet with a 1.76% improvement in mIoU, reaching 95.14% and 94.32% mIoU on the KPIs2024 and HuBMAP datasets, respectively. These results validate our model's excellent performance and robustness under complex pathological image conditions. The findings demonstrate EdgeAttenNet's great potential in clinical applications and provide new insights into addressing blurred boundaries in medical image segmentation.