DS-UNet: enhancing liver tumor segmentation in CT images via dense state space selection and gating mechanism

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Early diagnosis and precise localization of malignant liver tumors are crucial for effective clinical decision-making. However, existing automated liver tumor segmentation methods for CT images still face the following challenges: (1) Traditional U-Net and its variants struggle to achieve accurate tumor localization and fail to resolve blurred segmentation boundaries in complex anatomical backgrounds; (2) CNN-based methods are limited by fixed local receptive fields, failing to model long-range contextual dependencies for small and morphologically heterogeneous tumors and (3) Existing mainstream methods fail to balance segmentation accuracy and computational efficiency in resource-limited clinical scenarios. To overcome them, we propose Dense State Space Selection U-Net, a dense state space selection network, to enhance liver tumor segmentation from CT images. By integrating a gating mechanism and dense state space blocks, DS-UNet effectively models spatial correlations and improves feature extraction, resulting in superior segmentation accuracy. Quantitative experiments on public datasets demonstrate the effectiveness, which achieves Dice coefficients of 74.76% and 74.04% on LITS and HCC datasets respectively. Besides, ours provides a feasible and accurate solution for automated liver tumor segmentation, contributing to advancements in medical image analysis. The code for this paper has been released at https://github.com/Fan-XYin/DS-UNet .