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Medical lesion segmentation remains difficult because complex backgrounds can interfere with image features and lesions can have highly varied shapes. The paper introduces FreNet, a feature-reconfiguration framework that applies pixel-level reconfiguration before encoding and feature-level reconfiguration during encoding. Its Implicit Prior Neural Network models a continuous spatial field and uses a visual prior from SAM to suppress background responses before the image enters the encoder. A Dual-domain Feature Reconfiguration module progressively updates backbone features during encoding. Within it, a Frequency Decoupling Module separates features in the frequency domain to improve foreground-background discrimination, while a Spatial Localization Module relocates features to improve spatial stability after decoupling. Experiments across nine medical image segmentation benchmarks and three imaging modalities report that FreNet outperforms the compared state-of-the-art methods. On the challenging ETIS dataset, it improves Dice by 5.0 percentage points over the state-of-the-art method and by 7.2 percentage points over SAM.