标题： HANS-Net：用于CT成像中准确且可泛化的肝脏和肿瘤分割的双曲卷积和自适应时间注意力 显示英文标题

标题： HANS-Net: Hyperbolic Convolution and Adaptive Temporal Attention for Accurate and Generalizable Liver and Tumor Segmentation in CT Imaging

摘要： 准确的腹部CT图像肝脏和肿瘤分割对于可靠的诊断和治疗计划至关重要，但由于复杂的解剖结构、肿瘤外观的变异性以及标注数据有限，仍然具有挑战性。 为解决这些问题，我们引入了双曲卷积自适应时间注意力与神经表示和突触可塑性网络（HANS-Net），这是一种新颖的分割框架，协同结合了用于分层几何表示的双曲卷积，用于多尺度纹理学习的受小波启发的分解模块，用于自适应特征增强的生物启发突触可塑性机制，以及用于建模细粒度和连续解剖边界的隐式神经表示分支。 此外，我们引入了感知不确定性的蒙特卡洛丢弃来量化预测置信度，并采用轻量级的时间注意力以提高切片间一致性而不牺牲效率。 对LiTS数据集的广泛评估表明，HANS-Net实现了平均Dice得分为93.26%，IoU为88.09%，平均对称表面距离（ASSD）为0.72毫米，体积重叠误差（VOE）为11.91%。 此外，在3D-IRCADb-01数据集上的跨数据集验证获得了平均Dice得分为87.45%，IoU为80.30%，ASSD为1.525毫米，VOE为19.71%，表明在不同数据集之间具有强大的泛化能力。 这些结果证实了HANS-Net在提供解剖一致、准确且可靠的肝脏和肿瘤分割方面的有效性和鲁棒性。 显示英文摘要

摘要： Accurate liver and tumor segmentation on abdominal CT images is critical for reliable diagnosis and treatment planning, but remains challenging due to complex anatomical structures, variability in tumor appearance, and limited annotated data. To address these issues, we introduce Hyperbolic-convolutions Adaptive-temporal-attention with Neural-representation and Synaptic-plasticity Network (HANS-Net), a novel segmentation framework that synergistically combines hyperbolic convolutions for hierarchical geometric representation, a wavelet-inspired decomposition module for multi-scale texture learning, a biologically motivated synaptic plasticity mechanism for adaptive feature enhancement, and an implicit neural representation branch to model fine-grained and continuous anatomical boundaries. Additionally, we incorporate uncertainty-aware Monte Carlo dropout to quantify prediction confidence and lightweight temporal attention to improve inter-slice consistency without sacrificing efficiency. Extensive evaluations of the LiTS dataset demonstrate that HANS-Net achieves a mean Dice score of 93.26%, an IoU of 88.09%, an average symmetric surface distance (ASSD) of 0.72 mm, and a volume overlap error (VOE) of 11.91%. Furthermore, cross-dataset validation on the 3D-IRCADb-01 dataset obtains an average Dice of 87.45%, IoU of 80.30%, ASSD of 1.525 mm, and VOE of 19.71%, indicating strong generalization across different datasets. These results confirm the effectiveness and robustness of HANS-Net in providing anatomically consistent, accurate, and confident liver and tumor segmentation.