摘要： 针对现有的医学图像分割方法中由于不同器官的尺度剧烈变化导致的目标特征难以捕获，深浅层特征之间的语义差距引发的噪声干扰，上采样过程中由于固定网格导致边界几何信息丢失以及相邻器官组织黏连引发的边界划分模糊的问题，本文提出一种跨尺度几何校准的医学图像分割网络（Cross-Scale Geometric Calibration Network，CSGC-Net）。首先，网络采用PVTv2作为骨干编码器，利用自注意力机制有效提取图像的上下文信息，建立解剖结构的全局依赖。在此基础上，设计多尺度协同注意力模块（Multi-Scale Collaborative Attention Block，MCAB）。MCAB通过整合多尺度特征调节模块（Multi-Scale Feature Regulation Module，MSRM）、可共享多语义空间注意力（Shareable Multi-Semantic Spatial Attention，SMSSA）、渐进式通道注意力（Progressive Channel-wise Self-Attention，PCWSA），构建了多感受野，灵活感知因患者差异带来的器官尺寸波动；同时引入通道-空间双重注意力机制，在通道维度筛选关键语义并在空间维度聚焦显著性区域，有效抑制背景噪声，实现多粒度特征的协同增强并提高模型对复杂器官结构的敏感性。其次，设计双路自适应融合模块（Dual-path Adaptive Fusion Module，DAFM），不同于传统的高低维特征的简单拼接操作，DAFM利用可学习的权重评估不同维度特征的重要性，动态调节编码器的高分辨率空间细节与解码器的低分辨率语义的组合比例，缓解深浅层特征间的语义差距。然后，引入动态上采样模块（Dynamic Upsample Block，DUB），区别于线性插值上采样缺乏几何适应性的拉伸机制，DUB通过分组学习独立的空间偏移场，以固定初始几何网格为基准对上采样坐标进行自适应的几何校准。这一机制使得模型能根据真实的解剖拓扑进行动态形变，改善了模型上采样时由于等比例放大带来的锯齿效应，实现对不规则边界的亚像素级重构。最后，在监督训练阶段引入前景正交互斥损失（Foreground Orthogonal Loss），通过惩罚不同前景类别预测概率的空间重叠，实现了对相邻器官黏连边界的有效剥离，进一步提高整体分割精度。所提方法在三个公开数据集上与ECA-GC-Unet、DCAU-Net和MCT-Net等先进网络进行了对比实验。其中，标准版CSGC-Net-B的参数量为28.44M，远低于主实验次优模型的91.30M参数量。在精度表现上，CSGC-Net-B在腹部多器官数据集Synapse中，较次优模型的DSC提升0.33%，HD95下降1.14，并在胆囊、胰腺和胃三类器官上取得了最优分割精度；在心脏MRI数据集ACDC中，其DSC指标提升0.3%，并在心肌区域取得最优值；在肠道息肉内窥镜数据集CVC-ColonDB中，DSC较次优模型提升1.21%，mIoU提升0.98%，HD95显著下降3.04。此外，轻量版CSGC-Net-S的参数量缩减至4.33M，但仍在Synapse，ACDC和CVC-ColonDB数据集上分别取得了82.18%，91.74%和91.93%的DSC。总体实验结果表明，与现有主流网络相比，CSGC-Net在保持轻量化的同时提升了分割精度，展现出优异的泛化性能。

Abstract: To address target feature capture difficulties from extreme organ scale variations, noise interference from shallow-deep semantic gaps, boundary geometric information loss during fixed-grid upsampling, and boundary ambiguity from adjacent organ adhesion in existing medical image segmentation, this paper proposes a Cross-Scale Geometric Calibration Network. First, utilizing PVTv2 as the backbone encoder, the network employs a self-attention mechanism to effectively extract image contextual information and establish global dependencies of anatomical structures. Building on this, a Multi-Scale Collaborative Attention Block is designed. By integrating a Multi-Scale Feature Regulation Module, Shareable Multi-Semantic Spatial Attention, and Progressive Channel-wise Self-Attention, this block constructs multiple receptive fields to flexibly perceive organ size fluctuations from patient differences. Meanwhile, a channel-spatial dual attention mechanism filters key channel semantics and focuses on salient spatial regions, effectively suppressing background noise, collaboratively enhancing multi-granularity features, and improving model sensitivity to complex organ structures. Second, a Dual-path Adaptive Fusion Module is designed. Unlike traditional simple concatenation of high- and low-dimensional features, it uses learnable weights to evaluate feature importance across dimensions, dynamically adjusting the combination ratio of high-resolution encoder spatial details and low-resolution decoder semantics to alleviate the shallow-deep semantic gap. Then, a Dynamic Upsample Block is introduced. Unlike the non-geometrically adaptive stretching of linear interpolation upsampling, this block learns grouped independent spatial offset fields to perform adaptive geometric calibration on upsampling coordinates based on a fixed initial geometric grid. This mechanism enables dynamic model deformation matching real anatomical topology, improving aliasing effects from proportional enlargement during upsampling, and achieving sub-pixel reconstruction for irregular boundaries. Finally, a Foreground Orthogonal Loss is incorporated during supervised training. By penalizing spatial overlaps of predicted probabilities among foreground categories, it effectively disentangles adhered boundaries of adjacent organs, further improving overall segmentation accuracy. The proposed method is compared with advanced networks like ECA-GC-Unet, DCAU-Net, and MCT-Net on three public datasets. Notably, the standard version CSGC-Net-B has only 28.44M of parameters, far fewer than the 91.30M of the second-best model in the main experiments. Regarding accuracy, on the abdominal multi-organ dataset Synapse, CSGC-Net-B outperforms the second-best model by increasing DSC by 0.33% and decreasing HD95 by 1.14, achieving optimal accuracy for the gallbladder, pancreas, and stomach. On the cardiac MRI dataset ACDC, its DSC improves by 0.3%, securing optimal results in the myocardial region. On the intestinal polyp endoscopic dataset CVC-ColonDB, it improves DSC by 1.21%, mIoU by 0.98%, and significantly decreases HD95 by 3.04 compared to the second-best model. Moreover, the lightweight version CSGC-Net-S is reduced to just 4.33M in parameters, yet still achieves DSCs of 82.18%, 91.74%, and 91.93% on the Synapse, ACDC, and CVC-ColonDB datasets, respectively. Overall results demonstrate that compared with existing mainstream networks, CSGC-Net improves segmentation accuracy while maintaining a lightweight architecture, exhibiting excellent generalization.