面向体素医学图像分割的结合序数与关系先验的协同学习
Coordinative Learning with Ordinal and Relational Priors for Volumetric Medical Image Segmentation
AI总结：
针对体素医学图像分割中标注有限、现有方法丢失连续解剖相似性且忽略全局方向一致性的问题，提出CORAL协同学习框架，结合对比排序与序数目标学习解剖感知表征，在有限标注基准上取得SOTA性能。
摘要：
体素医学图像分割因固有的解剖结构及标注资源有限面临独特挑战。近期方法通过对比切片间空间关系展现出应用潜力，但这类方法依赖硬二值阈值定义正负样本，丢弃了有关解剖相似性的宝贵连续信息；此外，这类方法忽略了解剖演化的全局方向一致性，导致特征空间扭曲，无法捕捉跨患者共有的规范解剖流形。为解决上述局限，本文提出协同序数-关系解剖学习框架CORAL，用于捕捉体素图像的局部与全局结构：首先，CORAL采用对比排序目标利用连续解剖相似性，确保切片间的关系特征距离与其解剖位置差异成比例；其次，CORAL引入序数目标强化全局方向一致性，将学习到的特征分布与跨患者的规范解剖演化对齐。学习这类切片间关系可生成具备解剖学意义的表征，为下游分割任务提供支撑。通过该协同学习框架，CORAL在有限标注设置下的基准数据集上取得了当前最优性能，同时学习到具备有意义解剖结构的表征，代码可在https://github.com/haoyiwang25/CORAL获取。
英文摘要：
Volumetric medical image segmentation presents unique challenges due to the inherent anatomical structure and limited availability of annotations. While recent methods have shown promise by contrasting spatial relationships between slices, they rely on hard binary thresholds to define positive and negative samples, thereby discarding valuable continuous information about anatomical similarity. Moreover, these methods overlook the global directional consistency of anatomical progression, resulting in distorted feature spaces that fail to capture the canonical anatomical manifold shared across patients. To address these limitations, we propose Coordinative Ordinal-Relational Anatomical Learning (CORAL) to capture both local and global structure in volumetric images. First, CORAL employs a contrastive ranking objective to leverage continuous anatomical similarity, ensuring relational feature distances between slices are proportional to their anatomical position differences. In addition, CORAL incorporates an ordinal objective to enforce global directional consistency, aligning the learned feature distribution with the canonical anatomical progression across patients. Learning these inter-slice relationships produces anatomically informed representations that benefit the downstream segmentation task. Through this coordinative learning framework, CORAL achieves state-of-the-art performance on benchmark datasets under limited-annotation settings while learning representations with meaningful anatomical structure. Code is available at https://github.com/haoyiwang25/CORAL.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
