Boundary-Aware Spectral and Morphological Guidance Method for Feature-Driven Colorectal Cancer Segmentation

Precise segmentation of medical images plays a crucial role in modern clinical practice, providing important foundations for the quantitative analysis of medical images and clinical decision making. However, although deep learning techniques have achieved significant success in conventional medical image segmentation, they still exhibit obvious limitations when faced with complex structure segmentation tasks such as colorectal cancer: high-quality medical data acquisition is not only difficult, but also even when data is relatively sufficient, the diverse morphological features of lesions cannot be adequately represented due to their high variability, which severely limits the generalization capability of traditional data-driven segmentation methods. To address these challenges, we propose a feature-driven segmentation model that improves performance by deeply mining intrinsic data information rather than relying on parameter stacking. Specifically, we introduce a spectrum-prior-boundary triple modeling paradigm, where frequency domain reconstruction and modulation is employed to establish mappings between different frequency bands and heterogeneous signals to identify ambiguous signals, a level set-based segmentation algorithm is used to construct abdominal anatomical distance fields to incorporate morphological priors, and an auxiliary edge branch is designed by integrating deep semantic and shallow detail features to strengthen boundary awareness. Extensive experiments on colorectal cancer segmentation demonstrate that the proposed method achieves significant improvements in both segmentation accuracy and boundary perception over other state-of-the-art methods. Furthermore, evaluations on lung cancer and breast cancer segmentation tasks validate its strong generalization capability across diverse lesion types.