结构与进展感知扩散用于医学图像分割
Structure and Progress Aware Diffusion for Medical Image Segmentation
AI总结：
本文提出SPAD方法，结合语义集中扩散和边界集中扩散，通过进展感知调度器实现医学图像分割的粗到细的扩散过程。
摘要：
医学图像分割对于计算机辅助诊断至关重要，这需要理解粗略的形态学和语义结构，以及刻画精细的边界。医学图像中的形态学和语义结构是目标理解有益且稳定的线索。然而，医学目标（如肿瘤和病变）的精细边界通常模糊且嘈杂，由于病变重叠、标注不确定性等因素，使其不能作为早期监督。然而，现有方法在整个训练过程中同时学习粗略结构和精细边界。在本文中，我们提出了一种结构与进展感知扩散（SPAD）用于医学图像分割，它由一个语义集中扩散（ScD）和一个边界集中扩散（BcD）所调节，由一个进展感知调度器（PaS）调节。具体来说，语义集中扩散引入了锚点保存的目标扰动，它扰动医疗目标内的像素，但保留未改变的区域作为语义锚点，鼓励模型从周围的语义上下文中推断嘈杂的目标区域。边界集中扩散引入了进展感知的边界噪声，它模糊不可靠和模糊的边界，从而迫使模型专注于粗略但稳定的解剖形态和全局语义。此外，进展感知调度器逐渐调节ScD和BcD的噪声强度，形成从粗到细的扩散范式，这鼓励在早期目标理解阶段专注于粗略的形态学和语义结构，并在后来的轮廓调整阶段逐渐转向精细的目标边界。
英文摘要：
Medical image segmentation is crucial for computer-aided diagnosis, which necessitates understanding both coarse morphological and semantic structures, as well as carving fine boundaries. The morphological and semantic structures in medical images are beneficial and stable clues for target understanding. While the fine boundaries of medical targets (like tumors and lesions) are usually ambiguous and noisy since lesion overlap, annotation uncertainty, and so on, making it not reliable to serve as early supervision. However, existing methods simultaneously learn coarse structures and fine boundaries throughout the training process. In this paper, we propose a structure and progress-aware diffusion (SPAD) for medical image segmentation, which consists of a semantic-concentrated diffusion (ScD) and a boundary-centralized diffusion (BcD) modulated by a progress-aware scheduler (PaS). Specifically, the semantic-concentrated diffusion introduces anchor-preserved target perturbation, which perturbs pixels within a medical target but preserves unaltered areas as semantic anchors, encouraging the model to infer noisy target areas from the surrounding semantic context. The boundary-centralized diffusion introduces progress-aware boundary noise, which blurs unreliable and ambiguous boundaries, thus compelling the model to focus on coarse but stable anatomical morphology and global semantics. Furthermore, the progress-aware scheduler gradually modulates noise intensity of the ScD and BcD forming a coarse-to-fine diffusion paradigm, which encourage focusing on coarse morphological and semantic structures during early target understanding stages and gradually shifting to fine target boundaries during later contour adjusting stages.
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。
