UniPro：通过传播实现从2D图像到3D体积的统一多模态医学图像分割

中文摘要 AI 辅助

医学图像分割在分割范式和数据维度两个轴上仍然存在碎片化问题。现有方法通常分别针对语义分割、上下文内分割和交互式分割进行开发，并进一步专门针对原生2D图像或3D体积数据。然而，在临床实践中，分割工作流程有多种形式：一个病例可能由语义预测、参考引导分割或用户交互初始化。无论以何种方式开始，精细的细化通常在2D视图上进行；对于体积扫描，此类2D编辑必须连贯地传播到体积的其余部分。我们提出了UniPro，一个统一模型，它通过传播将2D分割扩展到3D体积，从而弥合了分割范式和数据维度之间的鸿沟。我们的关键见解是，体积传播和上下文内分割共享相同的参考条件预测机制，仅在于参考图像-掩码对是来自其他病例还是来自先前分割的相邻切片。基于这一观点，UniPro在单一的基于切片的框架内支持语义分割、上下文内分割、交互式分割和基于传播的分割，使用类别先验、参考示例、用户点击和相邻切片预测作为模式特定的条件输入。为了提高传播可靠性，UniPro进一步结合了双向和3D监督，以正则化切片级传播，超越逐切片损失。跨多种模态和解剖结构的广泛实验表明，UniPro在所有分割设置中均实现了强劲性能，使得从稀疏2D初始化进行注释高效的3D分割成为可能，并减少了逐切片校正的工作量。

英文摘要

Medical image segmentation remains fragmented along two axes: segmentation paradigms and data dimensionality. Existing methods are typically developed separately for semantic, in-context, and interactive segmentation, and are further specialized to either native 2D images or 3D volumetric data. In clinical practice, however, segmentation workflows take many forms: a case may be initialized by semantic prediction, reference-guided segmentation, or user interaction. Regardless of how it begins, fine-grained refinement is naturally performed on 2D views; for volumetric scans, such 2D edits must propagate coherently to the rest of the volume. We present UniPro, a unified model that bridges segmentation paradigms and data dimensionality, using propagation to extend 2D segmentation to 3D volumes. Our key insight is that volumetric propagation and in-context segmentation share the same reference-conditioned prediction mechanism, differing only in whether the reference image-mask pairs come from other cases or from previously segmented neighboring slices. Building on this view, UniPro supports semantic, in-context, interactive, and propagation-based segmentation within a single slice-based framework, using class priors, reference exemplars, user clicks, and neighboring-slice predictions as mode-specific conditioning inputs. To improve propagation reliability, UniPro further incorporates bidirectional and 3D supervision to regularize slice-wise propagation beyond per-slice losses. Extensive experiments across diverse modalities and anatomies show that UniPro achieves strong performance across all segmentation settings, enabling annotation-efficient 3D segmentation from sparse 2D initialization and reducing slice-by-slice correction effort.

发表机构

• Rutgers University（罗格斯大学）
• Stanford University（斯坦福大学）
• The University of Texas at Arlington（德克萨斯大学阿灵顿分校）
• New York University（纽约大学）

机构由 AI 辅助整理，请以论文原文为准。