Lifting wavelet transform-guided network with histogram attention for liver segmentation in CT scans

Abstract

Accurate liver segmentation in computed tomography (CT) scans is crucial for the diagnosis of hepatocellular carcinoma and surgical planning; however, manual delineation is laborious and prone to operator variability. Existing deep learning methods frequently sacrifice precise boundary delineation when expanding receptive fields or fail to leverage frequency-domain cues that encode global shape, while conventional attention mechanisms are less effective in processing low-contrast images. To address these challenges, we introduce LWT-Net, a novel network guided by a trainable lifting wavelet transform, incorporating a frequency-split histogram attention mechanism to enhance liver segmentation. LWT-Net incorporates a trainable lifting wavelet transform within an encoder-decoder framework to hierarchically decompose features into low-frequency components that capture global structure and high-frequency bands that preserve edge and texture details. A complementary inverse lifting stage reconstructs high-resolution features while maintaining spatial consistency. The frequency-spatial fusion module, driven by a histogram-based attention mechanism, performs histogram-guided feature reorganization across global and local bins, while employing self-attention to capture long-range dependencies and prioritize anatomically significant regions. Comprehensive evaluations on the LiTS2017, WORD, and FLARE22 datasets confirm LWT-Net’s superior performance, achieving mean Dice similarity coefficients of 95.96%, 97.15%, and 95.97%.