CoInS-Net：用于联合医学图像插值与分割的连续位置感知网络
CoInS-Net: A Continuous Position-Aware Network for Joint Medical Image Interpolation and Segmentation
- College of Medicine and Biological Information Engineering, Northeastern University（东北大学医学与生物信息工程学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
CoInS-Net是一种联合医学图像插值与分割的连续位置感知网络，通过双向交互实现两任务相互促进，在多数据集上优于传统单任务方案，为临床医学图像分析提供可靠通用技术。
AI中文摘要：
准确的医学图像插值与解剖结构分割是计算机辅助诊断和治疗规划的基础。具有稀疏层间采样的各向异性医学体积常存在结构不连续和边界模糊问题，阻碍了可靠的临床图像分析。现有多数方法独立执行插值与分割，引入冗余计算且无法充分利用连续切片间互补的跨切片结构信息。为解决这些问题，我们提出一种名为CoInS-Net的连续位置感知交互网络，用于联合帧插值与病灶分割。与传统的先插值后分割的级联范式不同，该框架在共享Swin编码器下通过连续空间坐标查询实现双向交互；空间连续位置插值模块从相对坐标和物理间距生成各尺度的目标位置特征；基于原型的任务互交互模块让分割与插值分支通过少量共享原型交换全局结构，而非密集特征混合；多尺度任务协作解码器进一步将各尺度分为共享与任务特定组件，使两个任务在保留边界层级独特需求的同时强化共同解剖结构，无需额外标注。在四个涵盖不同模态和解剖区域的公开医学成像数据集上的实验表明，所提方法优于传统单任务方案，该联合优化框架有效实现了插值与分割任务的相互促进，为智能临床医学图像分析提供了可靠且通用的技术方案。
英文摘要：
Accurate medical image interpolation and anatomical structure segmentation are fundamental for computer-aided diagnosis and treatment planning. Anisotropic medical volumes with sparse through-plane sampling often suffer from structural discontinuity and boundary blur, hindering reliable clinical image analysis. Most existing methods implement interpolation and segmentation independently, which introduces redundant computation and fails to fully exploit complementary cross-slice structural information between sequential slices. To address these issues, we propose a continuous position-aware interaction network, termed CoInS-Net, for joint frame interpolation and lesion segmentation. Unlike conventional cascaded interpolation-then-segmentation paradigms, the framework enables bidirectional interaction under a shared Swin encoder with continuous spatial coordinate queries. A spatially continuous position interpolation module generates target-position features at every scale from the relative coordinate and physical spacing, and a prototype-based task mutual interaction module lets the segmentation and interpolation branches exchange global structure through a small set of shared prototypes rather than dense feature mixing. A multi-scale task-cooperative decoder further separates each scale into shared and task-specific components, so the two tasks reinforce common anatomy while preserving their distinct requirements down to the boundary level, without extra annotations. Experiments on four public medical imaging datasets with diverse modalities and anatomical regions demonstrate that the proposed method outperforms conventional single-task schemes. The joint optimization framework effectively realizes mutual promotion between interpolation and segmentation tasks, providing a reliable and universal technical scheme for intelligent clinical medical image analysis.
