BCIW-Net: Boundary-Conditioned Adaptive CT Windowing for Hepatocellular Carcinoma Segmentation

Background/Objectives: Hepatocellular carcinoma (HCC) is the most common primary liver malignancy, and accurate tumour segmentation in contrast-enhanced computed tomography is essential for treatment planning, assessment of ablation margins, and post-treatment evaluation. Existing deep learning methods typically use fixed-intensity CT preprocessing, overlooking the adaptive windowing employed by radiologists to enhance tumour visibility. To address this limitation, we propose BCIW-Net, a boundary-conditioned intensity-windowed network that integrates spatially adaptive and differentiable intensity windowing into the segmentation process as a clinically motivated inductive bias.

Methods

The proposed framework first generates complementary soft-tissue and tumour-enhancing intensity representations using differentiable sigmoid-based transformations. A primary encoder-decoder module predicts tumour guidance, boundary probabilities, and pixel-wise error proxy maps, which collectively guide the dynamic estimation of spatially varying window centre and width parameters. The resulting adaptively windowed features are selectively blended with fixed-intensity representations through an error-gated mechanism before final refinement in a secondary encoder-decoder network. A multi-task objective jointly optimises tumour segmentation, boundary prediction, error-proxy estimation, and adaptive window regularisation in an end-to-end manner.