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.