Abstract
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.
Abstract (translated)
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结构和语义结构，并且能够精细地划分边界。在医学图像中的形态和语义结构是理解和定位目标的重要而稳定的线索。然而，医疗目标（如肿瘤和病变）的细小边界通常模糊不清并且带有噪声，由于病灶重叠、标注不确定等因素，这些信息不足以作为早期监督使用。尽管如此，现有的方法在整个训练过程中同时学习粗略结构与精细边界。 在本文中，我们提出了一种基于结构和进度感知的扩散（SPAD）方法用于医学图像分割，该方法由一个语义集中的扩散（ScD）、边界中心化的扩散（BcD），以及一个进度感知调度器（PaS）组成。具体来说： - 语义集中的扩散通过保持锚点不变的目标扰动引入，这种扰动只影响目标内部的像素而不改变背景区域作为语义锚点，从而鼓励模型从周围的语义上下文中推断噪声较大的目标区域。 - 边界中心化的扩散则引入进度感知边界噪声，模糊不明确和不可靠的边界，促使模型专注于粗略但稳定的解剖结构及全局语义。 此外，进度感知调度器逐步调整ScD和BcD的噪声强度形成一个从粗到细的扩散范式。这在早期目标理解阶段鼓励模型关注粗糙的形态学和语义结构，并逐渐过渡到后期轮廓调整阶段专注于细致的目标边界。 这种方法通过分阶段的学习策略解决了现有方法中同时学习粗略结构与精细边界的挑战，提高了医学图像分割任务中的精度和鲁棒性。
URL
https://arxiv.org/abs/2603.07889
