FEA-Net: End-to-End Frequency-Edge Automation for Ambiguous Lesion Boundary Segmentation in Gastrointestinal and Dermatologic Imaging

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.